Conference Agenda

Solar photovoltaic systems are a key renewable energy solution, but behavioural rebound effects offset their environmental potential. As Solar Home System adoption expands in low- to middle-income countries, understanding how contextual factors (e.g., social norms) shape these effects is crucial, yet research on this topic is scarce. Through a systematic literature review, this study identifies 15 contextual factors influencing behavioural rebound mechanisms (BRM). Findings are integrated into a design tool, helping developers analyse contexts, anticipate BRM, and apply prevention strategies.

Digital platforms for food and mobility offer sustainability and convenience, but their global adoption is context-dependent. This paper analyzes eight platforms in Turkey, contributing to the discourse on sustainable consumption. The analysis reveals diverse platform configurations and identifies key consumer barriers to widespread adoption, including trust issues, platform misuse, power imbalances, and limited service. The paper concludes with recommendations for motivating Turkish users, managing stakeholder trust dynamics, and leveraging existing consumption habits in new platform design.

Retrofit is often seen as a technical fix to boost efficiency and cut emissions, yet it also reshapes social relations, skills, and material flows. This paper reframes retrofit as participatory design cultivating ecological citizenship, the shared capacity to learn, make, and care for ecological relations through the built environment. Drawing on three cases, The Wild House, Ag. Lab, and the Retrofit Community Champion project, we propose a framework and design implications to scale equitable, circular, neighbourhood-based retrofit.

The study documents the approaches, processes, methods, and tools used by 11 start-ups to develop their smart products, while focusing on the sustainable concepts and techniques (SCTs) adopted. Although the majority of start-ups demonstrate awareness of the environmental impacts of their products and tend to implement relevant practices, the results indicate a lack of formalised SCT usage. Several start-ups have therefore recognised the value of a bespoke “eco-design toolbox” and aim to work towards reducing the environmental impacts of their later product versions.

The choice of modular product structure strategies has far-reaching implications for development, production, and other aspects of the product life cycle. So far there is only limited methodological support for the decision process. This contribution proposes a conceptual impact model that illustrates the relationships between modular product strategies, induced effects, and resulting economic targets. The proposed impact model supports the decision making process in the early product development stage and serves as a basis for further methodological development.

Design automation (DA) frameworks are often too specialized to be broadly evaluated. This paper proposes the use of deliberately simple, accessible implementations to facilitate the collection of user feedback. The evaluation of a DA framework is demonstrated through the Bike Connector Tool, which automates the design of personalized bicycle accessory connectors. A case study yields valuable insights, including the need for spatial guidance, manual intervention and expanded design options. The results indicate that simple demonstrators can effectively support the evaluation of DA approaches.

This paper provides a structured overview of methods for assessing assembly complexity in manufacturing. A systematic literature review classifies approaches as product-, information-, or system-centered, each reflecting distinct sources of complexity and application contexts. A four-dimensional scheme enables consistent comparison. The results highlight methodological gaps and support future development of scalable, integrable models for planning and decision-making in high-variety production environments.

This study presents a structured approach for developing new modular, size-variable product families in small and medium-sized enterprises (SMEs), demonstrated through a case study on air filtration units. Starting from a minimum viable product (MVP), the approach provides a framework for size level definition and systematic generation of alternative modular concepts while considering product-specific design trade-offs. An evaluation combining qualitative criteria assessment with quantitative cost forecasting enables transparent concept comparison.

Agile teams often encounter obstacles in systematically identifying the underlying root causes of collaboration challenges and deriving effective countermeasures. Grounded in the Design Research Methodology, this study investigates a hybrid AI-human approach for targeted generation of problem-specific reference and impact models to enhance systematic improvement in agile product development. A structured workflow integrates AI capabilities (e.g. scaffolding, consistency) and expert knowledge (causality, context), while a three-stage review ensures methodological rigor and result reliability.

In the early design stage of AI systems, designers face the challenge of addressing interdisciplinary needs with technical feasibility. This case study presents learnings from engaging a cross-functional team in the early design of an AI Vision System to support operators in the assembly process of electrical equipment. Our approach has been to embed Human-AI guidelines into boundary objects. The findings indicate that boundary objects helped participants reach common understandings, identify needs, and develop plans to mitigate future problems during the development of the AI system.

This study investigates how clinicians and technicians describe the preparation and use of CAD models in collaborative design sessions for mass personalised products. Semi-structured interviews with ten professionals were analysed using thematic analysis. The findings revealed three themes: the input data required before modelling, the additional information that supports interaction with the CAD model, and the role-specific ways in which contributors evaluate it. These insights guided the development of an initial parametric CAD model intended to support future collaborative work.

This paper explores the adaptation needs of employees in the context of implementing virtual reality (VR) in product development. Rather than analysing the overall process, the study focuses specifically on the employee aspects, including their roles, tasks, and challenges within the workflow. Existing work-related activities were analysed and visualized to identify inefficiencies. A set of tailored assessment criteria was created to systematically evaluate various sources of waste and process-related challenges.

This study aims to examine the influence of semantic feedback on the functional connectivity of students' brains in design education. We evaluated functional connectivity using EEG. After the instructor provided feedback, we observed a significant reduction in students’ alpha-band activity across 16 channel pairs. It suggests that, after receiving feedback, participants relied more on localized neural circuits rather than on broad, diffuse connections. Semantic feedback potentially facilitates participation in more efficient cognitive processes, thereby assisting design ideation.

Designers are often asked to state their assumptions. However, how assumptions are made is not well understood. We administered short and long versions of ill-structured problems to 22 students and analysed their responses using reflexive thematic analysis. Participants constructed analogies from recent experiences and distant memories. Interpretations in the short version often persisted in the long version, indicating assumptive inertia, a tendency to maintain initial assumptions. The findings offer insights into the process of assumption-making and its role in design decision-making.

This study examines designers’ cognitive and emotional experiences during the design thinking process and the effect of time constraints. Using the MetaCogno tool, 83 participants reported moment-to-moment experiences across Problem Analysis, Ideation, Evaluation, and Sketching. Positive experiences dominated, with time-limited designers showing higher enjoyment, focus, and engagement. Findings highlight the dynamic interplay of cognition and emotion, and suggest that time pressure can enhance focus and motivation during design.

Various methods are offered to measure design creativity. Nonetheless, a debate continues over the development of a trustworthy and objective method to mitigate the influences on the evaluation. Therefore, we propose and develop a novel tool and approach for evaluating design creativity, which reframes creativity evaluation as a structured and data-driven process. This tool represents a significant advancement in the evaluation of design creativity by mitigating objective factors. Importantly, our tool enables researchers and educators to adopt it freely and objectively for evaluation.

Advances in additive manufacturing (AM) enable the use of AM components in demanding complex applications with high functional requirements. As a result, integrating standardized machine elements such as conventional rolling bearings is gaining growing relevance. However, limitations regarding achievable tolerances or surface qualities in the MEX process stand in contrast to strict specifications for bearing integration. This study introduces a novel interface element and a corresponding integration process that considers both bearing requirements and the layered structure of MEX components.

This paper presents a methodical investigation of ten types of contacts for electrically conductive polymer composites manufactured using additive manufacturing. The study examines the suitability of three different bonding agents for reducing contact resistance and measures the contact resistance of the contact systems as well as the tensile strength. Based on these results, design guidelines and a design catalogue are developed. The results show that although each contact type is fundamentally suitable, they should be adapted to the respective application using the derived design guidelines.

The powder bed fusion by laser beam of metals (PBF-LB/M) offers the possibility of directly integrating particle dampers during manufacturing. Building on an existing optimization tool, this article investigates the optimization and multi-material additive manufacturing (AM) of a bracket for an atom chip of a quantum inertial sensor. The bracket is optimized in terms of mass, stiffness, and damping properties, and subsequently manufactured using Scalmalloy and tungsten in a PBF-LB/M process. The results provide findings into component design as well as into the pre-processing phase of AM.

Additive Manufacturing (AM) faces process limitations such as build-volume restriction and thermal distortion. While AM favors integral design, certain restrictions can be overcome by combining differential design with subsequent joining technologies (JT). Yet little is known about suitable JT selection in AM. To bridge this gap, established JT selection frameworks from conventional manufacturing (CM) were examined and adapted to AM. The resulting framework supports JT selection in AM based on joint design and AM-specific criteria while remaining applicable to CM and enabling combined designs.

Project-based learning is a key format in engineering design education. However, many students face difficulties due to a lack of prior knowledge required for successful project participation. To address this issue, we developed e-learning content to support students during the self-study time in preparation for the project work. This paper presents an evaluation of the impact of the e-learning content when used alongside project-based activities. The results indicate increased student confidence in developing mechatronic systems and a positive effect on acquiring professional competencies.

Product teardowns are a common educational tool in engineering design courses, with many benefits such as hands-on experience and improved engineering knowledge acquisition. This exploratory study investigates the role of product teardown activities on students' engineering education, specifically focusing on technical knowledge acquisition among industrial design students. Results indicate the teardown caused a qualitative increase in students' understanding of product function, components, materials, and manufacturing processes.

Compliant mechanisms achieve motion through elastic deformation, yet their core ideas are rarely taught before digital tools. This paper presents a foundation-first, hands-on method using lattice structures to introduce compliant mechanism design. In a pilot workshop, students analysed how rigid-body mechanisms move and redesigned them as compliant via tactile exploration and paper-based sketches, producing clear conceptual outputs and reporting improved understanding and confidence. The results show the value of early experiential learning before progression to digital and automated tools.

A blended learning approach was introduced to extend guideline-based training from key users to the wider end-user community in order to implement a new CAD/PLM environment. By combining asynchronous self-study of guidelines with synchronous, trainer-led sessions, the programme fostered procedural understanding and consistent modelling practices, as well as learner engagement. The results demonstrate how scalable blended learning strategies can bridge the gap between industrial training and academic education.

Companies lack methods to anticipate rebound effects (RE) in design, jeopardising their sustainability ambitions. This action research at Beiersdorf pilots a framework for ex-ante RE identification, modelling, and prevention. The study found 31 economic, behavioural, and social rebound mechanisms triggered by a refillable packaging innovation, using system dynamics to find leverage points for prevention (e.g., foster non-msaterial practices via packaging design). This paper offers a first attempt at a practical approach to integrate RE analysis into design, towards absolute sustainability.

The paper assesses the social impacts of composting and anaerobic digestion facilities for household biowaste in France. Using the Social Life Cycle Assessment (SLCA), it identifies 16 indicators that compare workers’ conditions, community impacts, and societal benefits. This work proposes a framework for incorporating social dimensions into a multi-criteria assessment of anaerobic digestion and composting facilities in Europe, with a particular focus on France.

The transition to a circular economy requires products that encourage circular consumer behaviour. Despite the central role of designers in this transition, the design for circular behaviour (DfCB) approach remains under-explored. This paper presents a literature-based conceptual model explaining which factors need to be in place, and how they interrelate, in order for designers to facilitate circular behaviours through product design. By pointing out gaps in the current state, future research directions are suggested to foster the establishment of DfCB.

Rebound effects occur when sustainability interventions trigger behavioural or systemic responses that offset environmental benefits. This paper explores how designers encounter and seek to prevent them in practice, based on nine interviews with sustainability-oriented practitioners. We identify twelve challenges across micro, meso and macro levels, showing that effective prevention requires aligning behavioural literacy, organisational governance and structural incentives across design contexts.

Sustainability is a central challenge in engineering. Early architectural design decisions strongly influence a product’s ecological footprint and long-term sustainability potential. Addressing these aspects in the concept phase is therefore essential. This paper analyses how architecture decisions - such as modularity, standardisation, redundancy, and updateability - affect ecological sustainability. The qualitative assessment helps practitioners to anticipate environmental impacts and foster sustainability awareness in product architecture development.

A key aspect of Circular Economy (CE) is focusing on value creation through customer functionality and service across the entire product life cycle, supported by digitalization tools for improved management. This shift has led to the rise of smart Product-Service Systems (PSS) models. However, designing smart PSS is complex, requiring methodological support for successful implementation. This study explored the feasibility of a novel tool based on the Quality Function Deployment (QFD) framework through its practical application in the photovoltaic industry.

This study explores how users perceive the centrality and sustainability of design attributes associated with modular design, energy efficiency, and design for disassembly, selected as illustrative eco-design strategies. 42 participants evaluated nine products through a bespoke online survey. The results show that salience and clarity of the environmental benefits associated with product attributes outweigh centrality in the perception of sustainability. This stresses the importance of clear and interpretable sustainability cues to improve users' understanding of environmental performance.

This work investigates the development of sneakers designed under Design for Disassembly principles and supported by additive manufacturing to promote a more sustainable and circular product life cycle. By responding to the limitations of traditional footwear assembly, the study identifies and organises 35 technical requirements derived from consumer needs. The proposed model offers a clear and adaptable framework that enhances decision-making in early design stages, guiding the creation of innovative, recyclable and environmentally responsible footwear solutions.

This article explores how design tools can enhance shared understanding of problems within West African entrepreneurial ecosystems, where new product development occurs amidst resource scarcity and intense market opportunitiies. The findings emphasize that design tools serve as flexible cognitive intermediaries that promote collaboration, uncover underlying assumptions, and foster new perspectives for solution development. Their value emerges through a progressive translation process, where researchers reinterpret these tools to bridge the diverse object worlds of entrepreneurs.

To bridge capability gaps between designers and artisans that hinder fashion and textile business collaboration and social innovation effects, this study explores how capability-based co-design fosters sustainable partnerships. Based on 20 case studies in China’s textile sector, it connects craft values, product features, and capability-based interactions. The model links this approach to make co-design practical amid skill gaps and enhance social innovation. It offers actionable strategies for balancing co-design methods in skill asymmetric partnerships in cultural and creative industries.

This paper introduces negotiation games as a method for staging and structuring collaboration in sustainability-oriented engineering design. Building on the Staging Negotiation Spaces (SNS) framework, it shows how scenarios can be re-staged as rule-based artefacts that provoke dialogue and alignment across organisational roles. Drawing on a case in scenographic production, the study demonstrates how negotiation games enable stakeholders to surface divergent concerns, reframe challenges, and co-evolve problem and solution spaces through situated alignment.

Co-creation with end-users is a well-known process. While this approach has been widely adopted in B2C, B2B companies have been slower to adopt it due to complex decision-making structures. This case study is based on co-creation sessions with end-users to respond to the needs of B2B industries. The study highlighted the importance of co-creation with end-users in B2B industries to understand real-life use scenarios, and finally to propose a final product that meets their expectations.

Generative AI (GenAI) tools are getting more and more integrated into creative workflows, evolving from assistants to collaborators, and reshaping human-AI interactions in the creative process. To better understand the human side of this co-creation, an interview study was conducted with 19 architecture students participating in a GenAI-supported design futuring course. The study identified 18 roles humans and AI can take during co-creation, along with tool-specific variations and insights into emotional dynamics, creative experiences, perceived agency, and control during the design process.

This study examines how designers’ experiences with text-to-image GenAI relate to their interaction patterns during a design hackathon. Survey data and two contrasting cases show that positive experiences align with shifting prompts and broader command use as designers move from exploration to refinement, while negative experiences correlate with fixed prompting and limited variations. The study demonstrates how interaction data can inform adaptive GenAI support across design phases, offering opportunities to enhance both practice and tool development.

This study aimed to detect designers’ motivations (Personal Identity, Conformity, Life Efficiency, and Information) in using Generative AI in AI-assisted design and how these motivations related to post-design evaluations of AI (Attitudes, Satisfaction, and Continuance Intention). The results showed that personal identity, conformity, and efficiency motives can predict attitudes and satisfaction for the use of Generative AI in AI-assisted design. No motivation indicated in the study can predict continuance intention, which suggests that long-term AI usage depends on factors beyond motivation.

This paper reviews 89 studies on AI in product platform design, further focusing on 21 multi-domain contributions. The dominant archetype is AI as a Tool × Method × Solution Proposal, with AI mainly used for automation and optimization. Collaborative roles remain rare, especially in requirements and architecture phases. Robust evaluation of AI benefits is largely missing, revealing an automation-centric paradigm and key gaps for co-intelligent, cross-domain platform development.

Part separation and subsequent adhesive bonding of additively manufactured (AM) subcomponents is a promising strategy to overcome manufacturing constraints and improve cost efficiency of AM processes. This study presents a three-dimensional scarf joint geometry, designed to maximize bond strength at a minimum use of substrate volume. Based on geometrical measurements, measures for improved accuracy of fit between PBF-LB/M substrates made of AlSi10Mg and Ti6Al4V were derived. Static tensile tests confirmed an almost twofold increase in bonding performance compared to conventional scarf joints.

This paper examines how ambient airflow, temperature, and humidity impact the print quality of upcycled biomaterials in Direct Ink Writing, and explores strategies for mitigation. A standardized pecan shell flour ink was used with optimized slicing parameters. Experiments in a controlled climate chamber involved sensor logging and statistical analysis. Airflow improved structural stability, overhang fidelity and bridging, but increased Z-axis shrinkage. Higher temperatures slightly improved bridging, while elevated humidity reduced stability and increased sagging, despite small bridging gains.

We present a thermal process-monitoring system for MEX tracking layer temperature as a proxy for interlayer adhesion. Python-based hottest-point tracking by infrared thermography is implemented on a chamber-heated desktop printer to track nozzle movements and measure the temperature field millimeters ahead of deposition logging the results on a CSV file. We quantify accuracy versus camera distance (Δd=73mm) and probe radius (R2-R5). Where R3-R4 provided just a ΔRMSE of 1.52°C suggesting R3 as the optimal distance. The results can inform mechanical properties in load-bearing AM applications.

Fused Layer Modeling (FLM) is one of the most popular additive manufacturing techniques. Its application is often limited caused by the procedurally anisotropy. This work addresses FLM’s weakness by examining a new path planning concept that replaces printing several adjacent parallel lines, for example in perimeters. The new technique was compared with conventionally manufactured reference samples in tensile tension and three-point-bending tests. The results show an improvement of the tensile strength in build direction of the samples by up to 40% and a reduction of anisotropy by 28%.

During the transition to CAD/PLM software, key users underwent guideline-based training aligned with company workflows. This practical approach, which linked tool functions to real design practices, accelerated the acquisition of skills, ensured modelling consistency, and improved understanding of digital engineering. The study identifies key users as knowledge multipliers and reveals how such methods develop competence. The findings emphasise the significance of problem-solving training and the relevance of guideline-based methods for industrial practice and design education.

The Design Society unites researchers and practitioners to advance innovative engineering design. As entrepreneurship grows in importance, integrate design principles into its education seems necessary. This paper reviews Design Society publications to map how entrepreneurship, education, and sustainability intersect, it identifies the Design Society's contributions in the field to propose strategies and future research to strengthen its influence. It highlights the possible role of the Design Society in leading education advancements in entrepreneurship and sustainability.

This paper presents the Systems Engineering Method Matrix (SEMM), a tool that links activities, methods, and artefacts to address gaps in existing method repositories. SEMM enables artefact-consistent process modelling and supports method selection, planning, and documentation in engineering projects. A case study in a project-based engineering design course demonstrates that SEMM enhances students’ conceptual understanding, broadens the range of methods, and provides instructors with a coherent framework for project planning.

In order to respond to today’s needs, engineers must be able to develop sustainable and environmentally compatible products and systems. To meet this requirement, new or adapted courses and curricula are needed in the field of engineering. This paper reviews the integration of a modular and scalable course concept for sustainable product development. The multi-institutional case study of 18 implementations across four German universities implies two primary models of use: stand-alone courses for specialisation and integrated modules for dissemination.

Delving into a case company, this paper provides a practitioner perspective on implementing sustainable product development (SPD). Strategic implementation is achieved when i) tools are integrated at critical stages of product development, ii) awareness and responsibility are spread across the organization and iii) there is cohesiveness between tools sustainability approach and metrics used. While SPD tools are systematically used and help the company in capability building, the current process does not guarantee systematic sustainability improvements, calling for further research.

Sustainability transitions in manufacturing require new competences and organisational learning. This paper presents Schedazioni, a learner-led assessment tool that helps companies analyse past sustainable design actions. Developed through case studies and a Research-through-Design process, and piloted in industry, it enables teams to map transformations, identify problems, and reflect on impacts. By shifting assessment to internal sensemaking, it supports shared understanding and strengthens sustainability capability.

This paper aims to explore and categorize the product innovation initiatives that European manufacturing companies with science-based greenhouse gas (GHG) reduction targets are adopting to advance toward their decarbonization goals. An inductive analysis was conducted on climate change management reports from 326 European manufacturing companies. Two main categories of strategies emerged from the analysis: the development of new low-carbon products and the improvement of existing products. Within these categories, the study highlights specific initiatives adopted by companies.

This paper introduces a method that embeds transdisciplinary conversations as structured reflection phases within the Double Diamond process model. Across two case studies, the approach shows how dialogue with diverse experts stimulates creative idea generation and sharpens the understanding of the interrelationships in sustainable systems. In this way, the method supports more well-founded design decisions and creative solutions for sustainability-oriented products.

Digital twin implementation in space systems lags behind product development maturity. This study evaluates transfer potential of digital twin approaches to space systems. Expert interviews (n=12) with product development and space specialists validated literature-based solution-challenge mappings. Abstraction level moderates transferability: organizational solutions transfer readily while technical solutions require space-specific adaptations. New Space contexts show higher transfer potential. The framework enables systematic digital twin implementation prioritization for space applications.

This paper posits that a Digital Twin can be viewed as a collection of nodes and edges where nodes represent actions on/with data and edges represent the flow of data between nodes. The paper provides a schema whereby the nodes and edges can be defined and the costs and benefits can be attributed, as well as analysis techniques enabled by the schema. The potential of the schema in supporting the design of Digital Twins is then demonstrated through a worked example, in which it is shown that traditional bottom-up cost estimates significantly overestimate costs when compared with this approach.

Building and maintaining a digital twin requires considerable technical and financial effort. Thus, its economic viability depends on creating value across the full product lifecycle to balance the initial costs. Therefore, this study examines what potentials and challenges arise from the use of the Digital Twin throughout the product lifecycle with regard to its benefits for product development, using a systematic literature review. The identified factors were clustered and mapped to key components, highlighting the strengths and weaknesses of the individual components of the digital twin.

This study utilises low-cost 2D pose tracking to analyse individual technique differences in elite rowers. We established key biomechanical metrics, revealing variations linked to anthropometrics, training style and flexibility. A technique mapping tool was developed, providing objective insights that supplemented expert opinion. A pilot demonstrated the utility of this analysis to generate actionable insights for equipment personalisation. This showcases that low-cost automated methods can provide proactive and meaningful insights suitable for individualized training strategies.

Venture Clienting enables firms to identify and realize innovation opportunities more rapidly through collaboration with start-ups. However, its implementation remains fragmented and prone to failure because organizations lack a coherent strategic architecture. This paper introduces the Venture Clienting Strategy Canvas, a single-view framework developed through Action Design Research. Fourteen essential elements were identified and organized into a layered structure. Applied in two firms, the canvas improved strategy design, articulation, and internal communication.

This paper investigates how advanced manufacturing firms mature technologies within mission-driven ecosystems. Two multi-partner case studies show how the design across six innovation dimensions—purpose, strategy, leadership, governance, innovation process, and budgeting & planning—enable co-maturation of novel technologies. Findings demonstrate that strategic partnerships with a shared mission, dual iterative/linear processes, and aligned governance accelerate mission-driven innovation from idea to scaled implementation.

Perpetual innovative products (PIPs) enable the reuse of components from previous generations to create new products with improved functionality and performance, supporting a circular economy. However, the concept entails uncertainties in design due to degradation and functional integration. This paper examines how testing can reveal and reduce these uncertainties through the analysis of testing activities. A four-step process is proposed that integrates testing in PIP development. The process strengthens decision-making by translating heterogeneous testing into actionable design knowledge.

Academic tools for sustainable product development often fail to achieve widespread use in industry. Based on a case study of a consultancy firm, this study explores factors that enable consultants to adopt and adapt such tools and act as intermediaries that translate and integrate academic findings into practice. Interviews and a survey revealed that a solid conceptual foundation, clear client value, result visualization, adaptability, and integration with existing workflows are most important, and the study proposes nine lessons learned to guide future tool development and collaboration.

This contribution addresses the lack of a structured framework for idea workshops in the Integrated Design Engineering (IDE) and resource-constrained settings. The current workshops in IDE are based on general creativity literature rather than on the processes and experiences inherent in IDE. This contribution derives a sequenced mode and integrates proposals to overcome helps to overcome common pitfalls. The sequencing shows best-practice in IDE and enables untrained users to enhance idea quality and process efficiency. The contribution offers a foundation for creativity technique assignment.

This study introduces an NLP-based Memory Model that structures how framing and reframing evolve throughout design. Grounded in constructive and situated memory theories, it models memory as a dynamic system of activation and decay, enabling measurement of the number and semantic value of frames in design discourse. Analyses of architecture students’ sessions show framing peaks during exploration and declines as solutions stabilize. They also show semantic diversity cycles through expansion and narrowing, revealing framing as a continuous, memory-driven reinterpretive process.

Why do all design acts begin by explicating a bounded frame of work? In design ontology, framing is the selection and representation of components and features in a system to guide perception and decision of designers but remains implicit. As a structural abstraction it becomes an explicit principle, formalised by a computational methodology that parameterises bounds and projects elements of a design having weighted attributes, in a relational context. Thus, the cognitive act becomes epistemic to compute for generating and evaluating frames, aligning design reasoning with scientific discourse.

This paper proposes a practical reframing design thinking as early as possible in the design process, so that sustainability is treated as integral rather something to be retrofitted. We present the broadened context phase as the first part of the Impact Design process. This iterative set of steps help designers exploring environmental problem spaces before commiting to a target group. We evaluated the approach with university students, in a sustainability course and a master project. Findings indicate that the method helps select the most impactful problem to address.

Thermal history is critical to part performance and reliability in material-extrusion additive manufacturing. Using encoders and an infrared camera, we developed a method to generate thermal clouds, where each node has its distinct spatio-thermal data. Filters removed up to 20.68% of the data while preserving relevant thermal features. This study enables in-situ process monitoring that establishes the basis for part certification, particularly for high-performance polymers, and for predicting material strength from thermal clouds.

This study evaluates the repeatability of print results in FDM through tests made with a generatively designed robotic limb. Five specimen were printed in two build orientations each with the same other process parameters. Deviations were measured via 3D scanning and CMM on both outer surfaces and functional features. Measured deviations exhibit small mean values and a clear orientation-dependent variability. As findings highlight deterministic effects in the propagation of deviations, design guidelines to improve repeatability of 3D prints are formulated as a result.

Individualisation in military equipment aims to improve performance by aligning design with soldier-specific needs. Existing DfAM methodologies lack structured integration of user variability and iterative evaluation in defence contexts. This study develops an iterative DfAM process linking anthropometric input, additive strategy, constraints, and performance assessment. Demonstrated through a helmet liner case, three iterations addressed geometry, manufacturability, and impact-response behaviour within regulatory limits.

We experimentally characterise the effect of layer temperature on the mechanical properties of PA6-CF manufactured by MEX. Ultimate tensile strength (UTS) and tensile modulus was investigated across layer temperatures ranging from 67 °C to 165 °C. UTS increased from 7.55 MPa at 67 °C to 36.04 MPa at 165°, while tensile modulus increased from 1.6 GPa at 67°C to 4.0 GPa at 140 °C. Measurements on a manufactured component show in-process layer temperatures between 88 °C and 123 °C. These findings quantify the attainable performance window and implications for functional component design.

Product development increasingly integrates generative AI tools to enhance creativity and efficiency. However, their actual impact on structured design work, particularly on method application and resulting designs, is not well understood. This study examines the effect of (1) method application quality on (2) product concept quality, influenced by (3) potential confounders like AI usage. Statistical analysis reveals that method application quality correlates positively with product concept quality, while higher AI usage correlates negatively with both, indicating limitations in AI usefulness.

The integration of Generative AI in engineering education requires a deeper understanding of diverse student adoption patterns. This study applies cluster analysis grounded in the Technology Acceptance Model and extended constructs on survey data to create different user profiles. Four distinct user profiles emerged: Empowered Optimizers, Mainstream Pragmatists, Skeptical Minimalists, and Ethical Achievers. The findings challenge one-size-fits-all approaches, providing a student-centred framework for designing tailored instructional strategies, GenAI training, and ethical guidelines.

Learning MBSE is hindered by abstraction and complex tools. This paper identifies barriers via literature review and interviews to design a RAG-based chatbot acting as a "smart view" for contextual guidance. Evaluated through a semester-long field study and a controlled experiment, the prototype shows high usability and reduces cognitive load. While performance is comparable to traditional e-books, the RAG-enabled system effectively mitigates entry-level barriers and aids authentic project work through stepwise tutoring, offering a scalable, interactive complement to MBSE education.

This study explores how AI workflows and prompt engineering reshape storytelling in design education. Students utilized tools such as ChatGPT, Midjourney, Runway, and Meta Glasses to reframe existing projects through iterative scripting, image generation, and reflection. Analysis of 88 visual projects and over 80 videos showed a shift from static documentation to multimodal narratives. Findings suggest AI enhances communication fluency, engagement, and reflective practice through adaptive, platform-native storytelling.

Cities play a major role in designing future mobility plans. Our question is how to contribute to sustainable mobility design while effectively accounting for social equity, health, and wellbeing considerations. After defining a list of mobility-related social issues, two stakeholder-based workshops with mobility users from two major cities, namely Paris and Cairo, were conducted. Participants explored mobility problems through eighteen purposive persona models in total. In Cairo, participants mainly reported safety and security issues while in Paris, mobility stress was dominant.

Transition Design has gained attention for sustainability, yet its cultural dimensions remain underexplored. This paper introduces Transition Design for Cultural Inclusivity, examining the halal ecosystem in Fukuoka, Japan, as a case of cultural transition in a non-Islamic context. Using a systemic service design approach, the study maps the current ecosystem, envisions a 2045 inclusive future, and outlines transition pathways. The research contributes by addressing a gap and extending Transition Design into the cultural domain, highlighting its role in fostering inclusive futures.

This contribution analyses key factors for establishing innovation communities within the circular economy. A mixed-method approach combines systematic literature review and a stakeholder survey to identify success conditions, governance requirements, and implementation challenges. The results underline the importance of open, interdisciplinary networks and adaptive, participative governance. Recommendations focus on iterative evaluation, stakeholder inclusion, and scalable models for long-term impact.

Design is transitioning modalities, from participation to deep engagement, creating active citizen(s). Authors define, communicate, and navigate post-participatory sustainable design, (interviewing 50+ project leaders), catalysing sustainable activities. Engaging Design, enables creative individuals, communities & collective action(s) to craft/design synergies: motivated by mutual respect, designing ‘with’ not for, shifts understandings’ of public engagement, transcending disciplines, providing sustainable value. Analysis and insights, yield recipes to cultivate sustainable active citizen(s).

Engineers simulate system behavior to support decisions in product engineering. Leveraging such engineering simulation data in strategic product planning can support idea generation and early evaluation of design alternatives and limitations. However, limited resources and expertise hinder broader uptake in strategic product planning. This paper investigates simulator integration into automated workflows and key processing components to enable simulation without in-depth expertise. This approach improves strategic product planning by creating data-based decision support.

This paper presents a design support framework that focuses on linking product design with risk management within the pharmaceutical packaging industry. The framework is intended for use by packaging designers and adopts a multi-user perspective to identify design requirements and integrate them into risk mitigation activities. It promotes safe and effective packaging through proactive design, reducing costly redesign measures. A preliminary version is presented which has been developed through studies with key industry stakeholders, including pharmaceutical packaging designers.

The present paper proposes a framework for translating lost haptic cues of textiles into digital environments with a view to reducing perceptual uncertainty in the context of online shopping. The model's integration of touch-related attributes and multimodal representations facilitates reliable customer perception, enhances material communication, and guides designers towards informed decisions. The paper outlines the challenges and opportunities inherent in the domain of multisensory digital textile experiences, while concurrently establishing a foundation for future research in this field.

Engineer-to-Order (ETO) companies often grow around ad-hoc know-how, and when they scale, usually they must standardize and choose a BOM setup, yet literature offers options but little guidance on how to decide. We propose a Focus Identification Model, a lightweight, tool-agnostic method that profiles context drivers, asks weighted decision questions, and links answers to a go-to setup with explicit governance. Calibrated on two industry cases, it yields a driver heatmap, decision matrix, and governance card to establish a common language and identify fit-for-purpose BOMs with less friction.

In the early phases of product development, cost estimation is crucial for supporting design decisions. When cost estimates are inaccurate or delayed, design engineers cannot assess economic viability and may pursue concepts that later prove too costly. In this context, artificial intelligence (AI) offers new possibilities for more accurate and timely cost estimates, yet adoption in practice remains limited. Based on an interview study with 22 cost engineers and an online survey of 102 respondents, this study examines practices, challenges, and expectations for AI-assisted cost estimation.

Large infrastructure projects often face misalignments that Delays outcomes. This paper presents a co-design workshop addressing such challenges in a European railway project. Guided by complexity theory, the workshop enabled participants to reflect and co-create strategies for alignment. Pre/post surveys and facilitator observations show increased confidence and engagement, with notable awareness in addressing misalignments. These findings highlight co-design’s value as an adaptive, participatory approach for creating recognition and managing complexity in interorganizational projects.

To support culture-sensitive creative problem-solving in distributed product development, two methods, the CSS Method and the Guideline, were developed. This study presents a Decision Tool to help users select the appropriate method based on team context and goals. Using Design Research Methodology, the tool was developed, evaluated, and refined through initial expert validation. Results show the tool improves method selection and usability while highlighting room for improvements and real-world testing.

Metamodels are replacing costly validation simulations and experiments in clinch joint design. If materials or conditions change, existing metamodels may no longer be reliable. This paper presents an approach that uses model uncertainty, the Coefficient of Prognosis and the R² score to decide if a model should be reused or recalibrated, or if fine-tuning is needed. Two case studies show that the framework can provide sufficient recommendations and reused, recalibrated and fine-tuned models can match new models while reducing simulation and training effort.

This study examines neural differences between high- and low-performing designers using EEG. Participants viewed an image of IKEA furniture and created alternative designs. Performance was evaluated based on the composite of fluency, flexibility and originality scores. Results reveal that high-performing designers exhibited greater beta and gamma frequency band power in frontal and right-frontal regions compared to low-performers. Although these differences did not remain significant after multiple-comparison correction, their large effect sizes suggest meaningful neural distinctions. 

Designers use GenAI tools during bioinspired design (BID) process to understand biological inspiration. We investigate the influence of using ChatGPT with BID on their creative thinking. We present BID stimuli to 30 designers in three modes: BID only, ChatGPT only, and BID + ChatGPT; and record their EEG data across four design phases. Their creativity is analyzed through convergent and divergent thinking (CT and DT), measured by average β and α TRP, respectively. Results show that BID stimuli’s influence on CT and DT is mode and phase dependent, indicating CT and DT as continuous processes.

We examine how the social condition of work influences design cognition. By applying cognitive load theory, we explore that individual work fosters internal self-regulation and user-centered pragmatism, whereas group work creates the collaborative substitution paradox, in which digital resources supplant interaction, thus encouraging external regulation and experiential narratives. The findings suggest that social conditions act as a moderator of cognitive load, indicating that individual work is beneficial for deep learning, while structured group work help mitigate substitution effects.

Design Space Exploration (DSE) supports the comparison of alternatives in complex, multi-objective problems. Despite advances in human-in-the-loop and visual analytics, most frameworks still assume a predefined design space. This paper reviews DSE and design cognition literature to reveal this conceptual gap and proposes a dynamic cognitive structure of the design space through an extended DSE framework, framing exploration as a co-evolution between cognition and representation.

This exploratory study examines what forms of expert support DfAM novices need and how they perceive AI-based expert systems. The results show that cognitive orientation, transparent communication and reliable information are most valued, while social or emotional expert attributes play a minor role. The study derives requirements for explainable, trustworthy AI support tailored to the early needs of DfAM.

Industrial adoption of additive manufacturing (AM) remains limited, partly due to challenges in determining when AM is more suitable than conventional processes. Since this decision must be made early to enable effective design for AM, understanding the factors that shape such assessments is essential. This study used iterative need analysis and prototype development loops to investigate these factors. The findings identify key needs and barriers influencing early decisions on when to design for AM and show that effective support requires a deep understanding of the underlying problem.

Additive manufacturing (AM) eases conventional manufacturing (CM) constraints allowing new design freedom. Yet designers still rely on experience and face AM high energy demand and variable waste benefits. This paper introduces an ecodesign approach to AM workflow through a tool comparing AM and CM via lifecycle metrics. The tool anticipates design and sustainability challenges providing environmental insights already at initial stages. This is highlighted with two use scenario: a new design and a redesign task. Future work involves tool development and validation with industrial case studies.

This paper examines whether the empirical knowledge of the TRIZ design theory is suitable for Design for Additive Manufacturing (DfAM). We systematically assess TRIZ engineering parameters (EP) and inventive principles (IP) in the context of contradiction analysis via DfAM, drawing on 11 semi-structured interviews. Findings indicate thematic alignment between DfAM methods and TRIZ IP, but reveal that the original TRIZ engineering parameters inadequately capture the multidimensional design space offered by DfAM. We outline directions to adapt the TRIZ EP for improved applicability.

This study examines how different AR platforms support learning and creativity in Additive Manufacturing (AM) education. Design students used either a smartphone- or headset-based AR app to explore virtual AM models before completing a design task and questionnaire. Expert reviews and Mann–Whitney U tests showed that headset AR users reported higher usability, better AM understanding, and produced more creative designs. The results highlight the educational value of immersive AR in enhancing technological comprehension and creative performance.

Mixed reality assistance guide posture and hand positioning, and familiarize material behaviour within craft prototyping. The development of the resulting framework focuses on non-intrusive assistance. Barriers include reduced immersion, observing precise hand movements, limited spatial interpretation, and understanding material behaviour. A 3D animation prototype, with roots in embodied knowledge, intends to improve spatial comprehension and enhance process and material understanding. The eventual framework should aid virtual assistance for design skill transfer.

Myopia remains a major barrier to immersive VR use, causing blur and discomfort. This review compares software, hardware, and hybrid compensation approaches. Software methods offer flexible, low-cost enhancement but limited optical correction. Hardware solutions provide accurate adjustment at ergonomic cost. Hybrid systems combine adaptive optics and real-time rendering, showing promise for personalized correction. Key trade-offs and future design directions are outlined.

This study employs a hybrid bibliometric analysis and the TCM (Theory, Context, Method) framework to examine the integration of emerging technologies like AR, VR, and AI in design education. Utilizing VOSviewer and CiteSpace on Web of Science data, it identifies pivotal research clusters and trending topics. The analysis reveals a shift toward immersive representational ecosystems and highlights critical research gaps. Consequently, the paper proposes a preliminary conceptual framework for collaborative design, offering a roadmap for pedagogical and curriculum transformation.

The product life cycle (PLC) is the basis for every development task. Its modeling is especially important in the context of the circular economy, as recirculation within the PLC forms its basic concept. To derive requirements for circular products, it must be known which phases are to be passed through within which circular strategy. This paper links R-strategies and life cycle phases by analyzing 56 life cycle models in regard to the number of phases, sequence, and other characteristics. A dependency matrix consolidated from the life cycles allows the findings to be utilized further.

This design-led study explores how circular economy principles can be embedded in research prototyping while comparing environmental sensing methods. A reusable outdoor prototype, deployed in three contexts, combined embedded sensors and API data. Using proxy indicators (energy, material mass, emissions) and end-of-life planning, API sensing indicated a far lower impact, while embedded sensing offered hyper-local data. All prototype components were either reused or repurposed, demonstrating circular prototyping in practice, with findings intended as design-informing rather than definitive.

This study examines barriers for circular ecosystems in literature, and identifies 11 enabling factors for collaboration in circular ecosystems. Based on a web-based analysis of 763 European CE projects, the study analyses how factors are addressed in practice. Collaborative processes, trust building, and technological enablers were most frequent, supporting relational foundations via digital tools. Projects often signal collaboration but rarely detail governance or ecosystem orchestration. Findings highlight design capabilities to foster shared-value creation in circular ecosystems.

Cradle to Cradle (C2C), as an eco-effective approach to the circular economy, helps mitigate the environmental impacts of the linear economy; however, its implementation in product development remains challenging. Due to limited prior reviews, this research investigates the implementation of C2C in product development. Through a systematic literature analysis, we identify key topics and challenges and examine how eco-efficiency and eco-effectiveness are addressed. Based on these findings, future research should develop a framework for implementing eco-effectiveness in product development.

Engineering software is evolving through the integration of artificial intelligence, creating new opportunities for enhanced assistance within product development. This paper proposes a use context model to systematically align and classify the functionalities of Generative Engineering and Design software with respect to the combination of product development phase, the nature of the task, and the level of support provided. Based on this model, a methodological guideline is proposed, offering a structured framework for the development and application of these tools in product development.

The rapid diffusion of generative AI is pushing creative work toward human–AI co-creation (HAIC). This paper designs a conceptual HAIC model that specifies several indispensable elements of effective co-creation: Human, AI, Artifact, Instruction, and Interaction. We demonstrated through a case study of a large-scale management information system development project how the HAIC model helps organizations implement HAIC. The proposed framework offers both an analytical lens for researchers and prescriptive guidance for practitioners seeking to engineer reliable human–AI collaboration.

Development of complex interdisciplinary products increases engineering challenges, that AI supported engineering approaches attempt to reduce by increasing automation. The resulting AI generated engineering artifacts, however, need to be classified, verified and managed to enable traceability and auditability of engineering decisions. This paper presents a classification and management approach for these artifacts, allowing verification of AI generated engineering artifacts. A use case on the iterative development of an e-bike demonstrates the approach.

A technology-oriented approach to AI predominates in research and practice, yet despite a high level of technological readiness, projects often fail due to poor domain-specific problem framing and data quality in early-stage AI system development. This contribution conducts an analysis of existing AI-related readiness models, to identify gaps in addressing these factors. The use case-centered AI readiness level framework is proposed on the basis of these findings – a unified, evidence-based model that links problem, data, and technology readiness across planning and implementation stages.

Custom manufacturers of engineered products face growing challenges in managing complex and variant Bills of Materials (BOMs). This paper proposes a visualization-driven framework for structuring and analyzing overcomplete (150%) BOMs in Engineer-to-Order environments. The framework integrates configurator rule logic, generic BOM structures, and metadata to enable explicit traceability and diagnostic analysis of variant-specific BOMs. A proof-of-concept prototype evaluated in a European fibre-laser manufacturer demonstrates support for variant validation, error detection, and alignment.

Gaps in lived experience hinder designers from understanding users. Virtual Reality (VR) may bridge this gap with immersive simulations, but its validity lacks comprehensive examination. This study addresses that gap via a VR scenario simulating the challenges of essential tremor (involuntary hand movements). Following the experience, designers demonstrated significantly enhanced empathy and a deeper user understanding. This translated into more accurate and contextually grounded problem framing. Designers’ rationales also suggest they intuitively internalized core design thinking principles.

In iterative product development, teams encounter various issues, such as difficulty communicating easily with stakeholders during reviews or internally during sprint planning. The present paper proposes a product-oriented visualization method that highlights engineering changes and deviations, enhances communication, anchors review feedback directly to components, and supports deriving actionable planning steps. Its implementation in development settings has demonstrated the enhancement of transparency, shared understanding, and traceability.

Mixed Reality (MR) prototyping offers significant design opportunities but introduces complexity in prototype specification. This paper presents a card-based design tool to support designers in this specification process. The tool is based on a comprehensive taxonomy of MR prototype fidelity and foundational research into the interplay between, and value of, different physical and virtual characteristics. A validation study demonstrates that the developed tool supports and guides designer reasoning, resulting in higher quality MR prototypes with stronger rationale for their implementation.

Engineers need to connect knowledge, based on science and technology, with knowledge about humans and society. To operate in a sociotechnical context a variety of different people with different skills are needed. This paper argues that therein lies an opportunity for all who have the skills and interests to find a fulfilling role in engineering that aligns interests in technical task, their role, their identity, their personal strengths and their values, illustrated by women in engineering and sustainability.

Knowledge management (KM) is crucial for efficient cross-sectoral R&D. Our study, performed with academic and industry experts (n=17), reveals a deep distrust in formal KM platforms and a high reliance on personal networks. Based on the findings of how the personal networks serve for knowledge management and exchange, we propose a concept and basic design requirements for an AI-powered 'knowledge orchestrator’. Accounting the promise and the capabilities of the modern AI, this AI-powered 'knowledge orchestrator’ may serve as a new generation of KM system for modern cross-sectoral R&D.

Here we propose seven meta-theoretical parameters of change (7MTPC)—Plannability, Manageability, Stability, Trajectory, Origin, Motor, and Scale—to provide a shared coordinate system for comparing organizational change approaches. Derived from classic and contemporary theories, the 7MTPC make explicit the assumptions about predictability, agency, and causality that underlie them. The framework offers scholars and practitioners a common language to map, contrast, and combine models, towards a systematic and evolutionary understanding of organizational change.

The increasing digitalization and connectivity of development processes are forcing companies to transform their engineering comprehensively. The presented engineering reference capability map provides a structured framework for this transformation. The capability map is a four-level hierarchical model, contains essential engineering capabilities and is inspired by the periodic table of elements. Standardized profiles describe the characteristics and dependencies of each capability. The map serves as a reference framework for identifying gaps, potential, and development needs in engineering.

Visual communication plays a vital role in conveying public health messages, yet design quality and effectiveness vary. This online study with 16 designers examined whether applying evolutionary psychology theory enhances designers' understanding, acceptance, visual cohesion of materials, and the effect of embedding biomimetic patterns. COVID-19 posters were used as context. Theory informed posters showed greater cohesion and were rated as more attention-grabbing. Results raise questions over tensions between theory- and creative demands and highlight designers' impact on health communication.

Nadheim offers healthcare to persons selling sexual services. Using relational, feminist, and system-oriented design, a rich design methodology combined cultural probes, vignette studies, and giga mapping unc toovered issues of service fragmentation, stigma, and digital exclusion. A co-created digital tool offering anonymous, centralized access to health, legal, and support services. An added speculative concept imagines a sex worker union to allow for radical change. Findings highlight trust, inclusion, and co-agency, positioning design as a catalyst for social justice.

As digital mental health interventions expand, integrating EU regulations into the design process remains essential but challenging due to their complexity. This study explores how the GDPR, AI Act, EHDS, HTA, MDR, and IVDR influence the design of AI-based mental health chatbots by mapping them onto a framework. The proposed mapping approach provides an overview of the regulatory landscape at each stage, revealing tensions between innovation and compliance as well as opportunities to use regulatory principles as structured checkpoints that guide responsible digital mental health design.

Conventional service design methods are valuable for improving healthcare experience, but are limited in scale and information capture. Based on a constructed database of 2,320 stories from patients and carers with multiple long-term conditions (MLTC), this paper shows how real-life experiences can be used to inform healthcare service redesign. By combining the richness of qualitative insight with the breadth and representativeness of large-scale data, it identifies "Continuity of care", "Care coordination", and "Temporal - Access to services" as the priority redesign opportunities for MLTC.

Residual stress is inherent in Additive Manufacturing process due to the heat cycling of the material being deposited on the build plate. The manufacturing toolpath can have a considerable effect on the development of residual stress distribution within a component. This paper examines the impact of the shell feature to generate some design heuristics on whether to include it when residual stress is of concern. The stress feature did increase the residual stressed observed during printing but was mitigated by the cooling regime after the process was complete.

We present a geometry-based complexity factor for additive manufacturing that estimates relative printing effort of fused filament fabricated parts from STL geometry alone. A reference effort is derived by slicing thousands of parts and volume-equivalent cubes. Eight interpretable geometric metrics feed a constrained, regularised index with monotonic calibration, achieving robust test accuracy and revealing which shape features dominate structural complexity.

Turbine blades are high-value components whose replacement is costly and slow, increasing the demand for effective repair strategies. Although PBF-LB/M supports precise additive repair, its application is limited by manual and time-intensive part preparation. This work introduces an automated digital workflow for cutting plane definition, repair geometry reconstruction and part alignment, improving reproducibility and reducing preparation time in PBF-LB/M-based turbine blade repair.

For industries transitioning to circular economy, understanding the business case is essential. This paper highlights key business model factors for remanufacturing within a heavy truck manufacturer. Twenty-six interviews were held, the barriers to profitability were identified as cost structure, key resources and cores and key activities. Applying systems engineering principles can help translate business needs into requirements, mitigate complexity and align stakeholders for effective transformation. Recommended future research is to investigate partnerships, profitability, and model design.

This paper presents a novel research and development framework for “Design for R” (DfR), which aims to systematically translate R strategies into actionable targets in engineering design. Building on the principles of Design for X and guided by the Design Research Methodology, it outlines a comprehensive research structure for developing, testing, and iteratively refining the DfR approach. The goal is to facilitate specific and robust technical solutions as well as practically applicable sustainability integration at the product level through structured methods, tools, and design principles.

This paper shows the implementation of the Global Sensitivity Analysis (GSA) as a Life Cycle Assessment simplification (LCA) method to identify the most influential design parameters for the design of packaging reuse loops. The practical execution of GSA relies on Monte Carlo simulations to propagate the input parameter effects in the LCA model and Sobol indexes to quantify the variance contribution of each parameter. This methodology was applied to a case study in the French region of Île-de-France. This method allows identify the key design parameters to prioritize the design key decisions.

Remanufacturing can be facilitated by design activities considering value creation, preservation, and recovery. Design-related decisions for remanufacturing can affect the performance of business models, but there is a lack of literature to identify these barriers or enablers. Through an analysis of selected remanufacturing cases, an initial step to bridge this gap is provided. Findings highlight the potential of design for remanufacturing for enhanced value creation processes and new service offerings, and present recurrent barriers and enablers to remanufacturing in the cases.

While studies on generative artificial intelligence for product development have gained momentum, they consistently report recurring challenges. To synthesize these obstacles, we surveyed 1074 papers, resulting in a taxonomy of 27 distinct barriers. The study analyzes their frequency, discusses their interrelations, and contextualizes their root causes. Our findings show that model capability, output validity, and user trust are the most dominant obstacles, while aspects like environmental concerns are often overlooked. The study concludes with recommendations for research and practitioners.

Generative Artificial Intelligence (GenAI) is transforming design practice yet research lacks empirical insights into adoption in real-world design organisations. Through the case study of a European automotive OEM, we found that GenAI could accelerate ideation, but adoption was limited due to critical concerns regarding intellectual property, data security, originality, and the risk of skill atrophy. Thus, organisational capabilities like workflow specific training, transparent governance of data protection policies, and cohesive toolchains are needed for successful GenAI integration.

Colour, Material and Finish (CMF) designers face rising circularity demands but lack tools that combine reliable data, traceable reasoning and creative control. This paper reports a case study with automotive CMF designers, identifying pain points in data access, evaluation of circular options, authorship and trust in AI. We propose design requirements and a conceptual model for agentic AI systems that support circular CMF work while preserving designer agency, accountability, and confidence in material decisions.

OEMs are shifting from product-centric offerings toward smart services, but adoption is still hindered by technical development barriers. We conduct a systematic literature review of peer-reviewed studies with original industrial evidence and identify eight barrier categories across data, semantics, integration, governance and modularity. We map them onto a smart-service architecture and key analytics roles, and relate them to GenAI building blocks such as LLMs and knowledge graphs, outlining a research agenda for overcoming technical barriers towards scalable OEM smart service development.

While Immersive Reality (XR) design tools continue to emerge, the industry perspective on their value is unclear. This paper presents outputs of a workshop with 16 design experts testing a wide range of XR design tools. Value exceeding that of traditional tools was reported, driven by human-centric affordances like flexibility, interactivity, and response rate. Perceived detriments were linked to implementation challenges, such as fidelity and skill. Findings validate XR's potential in design and direct future work towards overcoming key technical hurdles to unlock its value.

This study presents a lean experimental method for investigating Proofs of Concept (PoCs) in early product development. By adapting and extending Design of Experiments (DoE) with complementary frameworks, the method enables efficient identification of minimal functional parameter sets. Based on a cube-oriented model and iterative one-factor-at-a-time (OFAT) testing, the design space is systematically refined. Experimental validation on a smart shaft–hub connection demonstrates the method’s effectiveness in reducing required samples while ensuring feasibility.

Printed circuit boards (PCBs) fix and connect electrical components and are widely used. Current design methods emphasise mature products and do not leverage the potential of PCBs as prototyping tools. Accordingly, an alternative approach using PCBs for prototyping electrical and mechatronic solutions is evaluated through three case studies. Insights formed five concrete recommendations for designers: Increase fidelity deliberately, design for prototyping, iterate incrementally, parallelise prototyping, and prototype and test early. These aim to make prototyping with PCBs more accessible.

This paper evaluates the criticality assessment within the Criticality-Based Planning of Prototype Sequences method. Two independent application studies investigate whether the existing scale definitions for novelty, technical difficulty and importance are sufficient for systematic and context-independent use. The results show that operationalised criteria and clearer evaluator guidance significantly improve consistency, reproducibility and applicability across different development projects.

Data-Driven Design (DDD) is emerging as a transformative approach in engineering design, leveraging AI tools to extract knowledge from design data that drive product development and innovation. While large language models have advanced DDD through the analysis of textual data, technical drawings remain largely unexplored. To address the limitations of current vision-language models, this study presents a novel object detection pipeline that automatically identifies components in patent images, enabling data-driven analysis of component geometries, interfaces, and spatial configurations.

Patents contain valuable design insights, yet manual analysis remains time-consuming and complex. This study explores Large Language Models’ capacity to automate patent analysis for engineering design. GPT-5 and Gemini 2.5 Pro were evaluated across Motivation, Novelty, and Key Invention Features using three patents and expert evaluators assessed outputs through Accuracy & Fidelity, Comprehensiveness, and Analytical Depth. Results indicate LLMs demonstrate proficiency in feature synthesis but exhibit inferential limitations in motivation analysis, underscoring the necessity for human oversight.

Design Structure Matrices (DSMs) capture dependencies between system entities and help analyze system complexity, but manually creating them from unstructured documents is time consuming. This work proposes an automated DSM extraction framework using LLMs and RAG with an explicit reasoning step before the LLM determines the presence of a dependency between two system entities. Using a hand-curated dataset, we evaluate three LLM models (GPT-4o-mini, GPT-3.5, and GPT-4o) across six performance metrics and cost.The findings show that reasoning length affects LLM's DSM extraction performance.

This study evaluates the efficacy of various freely available Large Language Models (LLMs) in conducting semi-automated purpose-oriented technology searches to support design activities as well as Technology Intelligence for innovation management, using a systematic manual search as a baseline for comparison. The case to run the comparison focuses on identifying water purification technologies suitable for mobile systems. The results show that LLMs can target more technologies than human-based searches, reducing time demands and providing wider entry points for additional technology analysis.

This study explores patient perspectives on hospital sustainability initiatives. Building on the Theory of Planned Behaviour, 30 interviews with patients reveal support for sustainability such as the use of reusable medical textiles, provided safety and quality are maintained. Patients view sustainability as a hospital responsibility, but value integrating sustainability communication into patient journeys. Informing and engaging patients can help shift sustainability from a background initiative into a trusted part of the healthcare system with patients as informed partners.

Circular economy strategies like refurbishing and upgrading are gaining insterest in healthcare, but their adoption depends on several factors linked to social acceptance, technical and economic feasibility. The study examines these factors through a survey among medical practitioners in a French hospital. Results show that technical reliability, long-term performance, and access to new functionalities are key factors, while environmental, cost benefits and physical appereance are secondary. The study offers insights for designers to enhance sustainability of circular medical devices.

Pharmaceutical waste (PW) poses environmental, economic and public health challenges, yet effective solutions remain underexplored. This study used a co-design approach across two NHS Scotland workshops to examine medicine-use experiences and generate interventions. Three concepts emerged: transparent patient packaging, a tactile communication aid and a gamified virtual medicine-cabinet app, highlighting the value of bottom-up design methods for addressing PW.

This study explores how designing reuse models for clinical trial packaging can stimulate circular transitions in healthcare logistics. Using a Design Inclusive Research approach, four reuse models were explored, developed and evaluated through a Product-Service System lens together with stakeholders from the value chain. Findings underline the complexity of implementing RPSs in the clinical trial context. Implementing circular solutions therefore demands an added design layer focused on quality assurance, along with new protocols, digitalisation, partnerships, scale, and standardization.

This paper presents a concept for an AI-supported DfAM framework aimed at supporting knowledge extraction, focusing on early design phases. The concept is derived from a set of objectives and integrates, in addition to the user, an agile DfAM process model, an AI copilot based on a large language model, and a structured knowledge base. A configured GPT is used as a prototype to demonstrate the feasibility of selected required functions. With regard to a full-scale framework, findings from this prototyping process and remaining open questions are discussed.

Generative AI and additive manufacturing (AM) are shifting orthotic design from generic devices to data-driven, patient-specific solutions. This paper presents a systematic review of Generative AI in Design for AM (DfAM) for orthotic devices. It examines how AI-driven methods generate customised, lightweight orthoses via 3D printing, improving both design efficiency and anatomical fit. The review identifies biomechanical and workflow challenges that hinder adoption and outlines how Generative AI can advance orthotic DfAM, providing a conceptual workflow and suggestions for future research.

This study compares solid and shell generatively designed PLA components for material extrusion (MEX) at matched mass targets (100, 150, 200 g). Geometries were generated Generative Design (GD) and manufactured by MEX, then tested in a 30° quasi-static compression rig representing prosthetic heel strike. Solid designs achieved up to 92% higher peak load, but failed abruptly, whereas shells exhibited lower strength but progressive, energy-dissipating failure. Results show that simple shelling of GD outcomes cannot replace iterative GD refinement for weight-critical, load-bearing parts.

This study examines how CAD geometry variations affect finite element (FE) crash simulations for automotive front rail assembly and motivate the use of combined impact measures that better reflect the physical response. Based on these insights, we outline a machine learning formulation that links geometric modifications to their simulation effects. The study centers on geometric representation, employing UV‑based graph encodings to capture local shape changes and provide the basis for advancing and validating the full prediction pipeline.

The study examines integrating absolute sustainability, based on planetary boundaries, into product design and development. A review of current frameworks shows that they focus on relative sustainability and lack mechanisms to encourage innovation within ecological limits. Analysis of fourteen publications shows that a design support tool should facilitate early-stage decision-making, incorporate scientific thresholds and circular economy principles, align with the SDGs and provide guidance on product development to create sustainable and transformative product solutions.

This study evaluates two approaches for sustainable product development and their complementarity through workshops and surveys in four companies. Findings show high perceived usefulness but lower usability, with applicability dependent on integration into existing processes. The study identifies value-carrying characteristics – clarity, adaptability, and process linkage – and highlights improvement needs. Results offer guidance for developing coherent methodological support to enable systemic, systematic, and strategic sustainability decisions in early design phases.

This study presents a method for early identification of AM-suitable components and circular design routes. A structured, data-lean questionnaire with AHP-based weighting, combined with rule-based R-strategy identification and an AM-specific feasibility gate, enables transparent screening in early development. Demonstrated on a structural bus component for both diesel and electric operation, the approach proposes a dominant R-strategy and integrates a prospective LCA-in-the-loop workflow, showing how early circularity signals and life-cycle feedback inform robust redesign decisions.

Political goals, emerging EU sustainability regulations, and industrial digitalization are driving the introduction of Digital Product Passports (DPPs) to enhance transparency, traceability, and compliance across product life cycles. However, the appropriate granularity of DPP integration across product architectures remains ambiguous. This paper introduces a structured, decision oriented framework that links product structure, regulatory relevance, and information depth to define consistent DPP levels, supporting both industry implementation and future standardization.

Artificial intelligence influences requirements engineering, but it remains unclear which activities benefit and how. This paper reviews 15 studies from the last five years, classifying AI approaches with an established RE framework. Current work focuses on operational tasks: requirements determination, analysis, consolidation, and traceability. About two thirds address single activities rather than integrated solutions. Early-phase tasks like knowledge elicitation receive little support despite being central to practice. The mapping clarifies existing AI support and gaps for future work.

Leveraging the vast interconnection of language and ideas through Large Language Models, a designer’s understanding of the needs, wants and desires of intended stakeholders defines the value proposition and product design requirements of a product or service through implementation of the Value Opportunity Analysis (VOA). The resulting VOA LLM Bot explores emotion, aesthetic and other human-valued attributes, and significantly increases perception of the VOA as a useful method for identifying product requirements, and analyzing opportunity solutions.

Requirements quality shapes engineering design, yet natural language specifications remain vulnerable to ambiguity. We investigate how LLMs support ambiguity detection using a hybrid dataset combining NASA JWST requirements with systematically injected defects. Using auto-extracted domain knowledge, we compare a domain-agnostic baseline with a context-aware approach. Incorporating domain knowledge helps LLMs better distinguish genuinely ambiguous requirements from acceptable ones, highlighting the potential of context-aware AI assistants for requirements engineering and early-stage design.

High-quality requirements are essential for successful product development. This work proposes a model-based requirements engineering framework and AI-supported tool. The framework links design characteristics and measured properties via an OPM-based system model. This enables the implementation of a tool for systematic verification and validation of requirements in early product development stages, supporting the transition from experience- to data-/evidence-driven decision making and industry 4.0 paradigms. A hydraulic-press case-study demonstrates feasibility of the end-to-end workflow.

This paper reports iterative industrial prototyping of data collection systems for simulating seafood factories. We identify the data necessary to achieve the level of realism factory designers need for effective design exploration, and propose methods to obtain them. Sixteen physical prototypes showed how prototyping shape dynamic requirements in the design process. Findings indicate that models need 3D shape and texture, which can be obtained from smartphone photogrammetry, and bending stiffness and multidirectional friction from cantilever and inclined plane tests.

Chemotherapy (CTX) is essential for cancer treatment, yet its cytotoxic properties pose significant risks to clinicians. As cancer incidence rises, demand for safe and reliable CTX preparation methods intensifies; Automated Drug Compounding (ADC) promises to address these issues. Nonetheless, technical and operational barriers limit adoption. Foremost, the reliable manipulation of syringes by robotic end effectors remains a challenge. This paper examines limitations of current end effectors for ADC systems, identifies design requirements necessary, and proposes a novel design to address them.

Foresight prototyping uses speculative artifacts to explore futures and support anticipatory design thinking. This study develops and validates an evaluation framework for HCI design education to support studio critique and assessment. Based on a literature review, author interviews, and quantitative analyses of perceptual ratings, four dimensions are identified: Functional Visibility, Sensory Experience, Future Adaptability, and Creative Divergence. The resulting FPEF supports consistent, evidence-based evaluation and feedback, and motivates future validation in design reviews.

The early stages of NewSpace missions can be in the fuzzy front end because of unclear requirements. This paper explores clearing this design phase through low-fidelity prototyping, using a case study of 23 prototypes organized into five concepts. The goal is to enable post-launch deployment and focus of a modified commercial-off-the-shelf (COTS) telescope for an Earth observation hyperspectral imaging satellite mission. A final payload design is developed, and the lessons learned inform the design methodology for the early phases of NewSpace projects.

Aligned with Industry 5.0’s human-centred and collaborative design vision, this paper examines how knowledge representation (KR) supports design communication through a dual-function lens, distinguishing knowledge transmission and knowledge generation. Based on a review of 83 studies, we map KR across stakeholder interactions and design stages. Transmission dominates early cross-stakeholder communication, while generation is largely confined to designer-centred ideation, revealing structural imbalances and opportunities for broader KR deployment.

The framework proposed operationalized through four interrelated components: encoding, retrieval, mapping, and evaluation. Semantic networks serve as the underlying knowledge representation that enables information structuring and cross-domain association. By translating cognitive reasoning into a computational architecture, the proposed framework establishes a unified structure for supporting analogical design and provides theoretical and technical guidance for developing future semantic-network-based tools that more effectively facilitate creativity and innovation in conceptual design.

This study proposes a new design for a knowledge audit, focusing on human-AI knowledge augmentation within a consulting firm’s setting. It adopts a mixed-method paradigm, including interviews, quantitative surveys and Social Network Analysis, to identify obstacles, facilitators, and AI-mediated flows within a Community of Practice. Findings show strong motivations and AI readiness coexist with poor documentation and codification. The paper reframes the audit as a design-oriented tool for mapping how human–AI collaboration shapes organisational knowledge maturity.

Digital design tools are omnipresent today, but which is right for the job? This study reviews previous approaches to categorise design tools revealing a lack of comprehensive catalogues. Given this gap, a set of requirements, classification schema and prototype catalogue (IDEATE) were developed. A survey explored selection factors, format preferences and evaluated the prototype with IDEATE scoring 6.44/10 compared to 5.28/10 for a table format. This evidenced interest in mapping the ecosystem though future iterations should prioritise refined navigation and enhanced searchability of tools.

The implementation of services into complex systems is not well understood in design. We explore this issue by interviewing 24 design professionals with experience in implementing digital services in healthcare. We asked when they consider such services as implemented, and how they view the relation between design and implementation. Results reveal diverse perspectives on both topics. Given the wide dispersion in views, we propose two categories to describe implementation goals (impact on, and integration with systems), and to view design as a contributor to the implementation phase.

Long-COVID is a complex, multi-system condition with variable care across the UK. Using a systems and design engineering approach underpinned by Model-Based Systems Engineering (MBSE), this study examined Long-COVID clinic pathways through semi-structured interviews with 15 clinicians and patients. Thematic analysis identified five domains—attitudes, relationships, service integration, technology adoption, and safety netting. The final synthesised swimlane diagram revealed opportunities to improve coordination, operational efficiency, and patient safety within evolving care models.

Adolescence is a critical period for establishing oral health habits, yet motivating young people remains challenging. This paper presents a pilot intervention in the Norwegian Public Dental Service, developed through co-design and guided by salutogenic and hope theory. The intervention consisted of three touchpoints: an SMS invitation, an in-clinic goal-setting dialogue, and a follow-up SMS embedded in routine care. Based on interviews with clinicians and adolescents, we explore how hope-oriented communication can be enacted within everyday clinical practice.

This paper explores the role of artificial intelligence to reduce resource burden and support service delivery processes in generalist secondary-care mental health services in the Netherlands. Through semi-structured interviews with domain experts and using service blueprinting as a stimulus, we identified challenges and bottlenecks in mental health care pathways and intervention opportunities. We propose four intervention directions for design researchers and developers to prototype and assess how AI technologies may alleviate capacity issues in mental healthcare.

This study investigates the potential of multi-material additive manufacturing (MMAM) designs for improving microchannel reactors for ammonia decomposition. Using CFD simulations, designs made from stainless steel 316L and CuCr1Zr to enhance specific surface area and temperature distribution were analyzed. Results show that MMAM designs can reduce temperature gradients by up to 26.81 K and boost fuel processor efficiency by up to 3.2 percentage points compared to mono-material designs. These findings underscore the potential of MMAM in optimizing the reactor efficiency.

AM enables the design of compliant mechanisms that encode functions directly into geometry. Existing DfAM frameworks rarely address microscale AM, such as two-photon polymerisation (2PP). We present the design process of an airtight, monolithic bellows structure in rigid 2PP resin that serves both as a sensor and an actuator. Through co-evolution of problem and solution, we identify 2PP-specific design considerations and opportunities, including fabrication uncertainties, cross-scale iteration, and design for post-processing, contributing to a case-based DfAM framework for microscale AM.

This work presents an ML-based inverse design framework for multi-material lattices with curved struts, targeting mechanical and thermal performance. Using cubic-spline parameterization and discrete material assignment, the design space expands beyond conventional lattices. A workflow combining a material classifier, property predictor, and inverse generators addresses one-to-many mapping, enabling probabilistic sampling and diverse designs. The approach supports multi-objective trade-offs and lays the foundation for multi-scale optimization of functionally graded metamaterials.

Additive Manufacturing (AM) enables the local adjustment of material properties using multi-material strategies, especially with Material Extrusion (MEX). Electrically conductive structures like conductors, Joule heating structures, and their transitions can be realised with conductive polymer composites (CPC). However, specific Design for Additive Manufacturing (DfAM) guidelines for the afore mentioned structures are still missing. This work uses experimental data by thermography and the measurement of resistivity to derive twelve design rules. The rules are applied to an application example.

This paper operationalizes ISO 59020 for product-level circularity by implementing the Holistic Product Circularity Analysis (HPCA) with fixed boundaries and explicit reporting rules. HPCA enables consistent assessment and targeted optimization by combining qualitative ratings with quantitative flows across all stages of the product lifecycle. The approach is demonstrated on a children’s balance bike by comparing a reference scenario with an optimized circular scenario and deriving decision-relevant improvement levers.

The Circular Life Cycle Blueprint (CLB) is a four-step design methodology for integrating circular economy principles at the component level in product design. Developed via a design science approach with iterative prototyping and evaluation, the CLB guides design teams from conceptualizing circular strategies to mapping component lifecycles and conducting a sustainability assessment. Pilot evaluation with industry professionals indicates that the CLB is effective and user-friendly, fostering innovative circular design and demonstrating practical viability in sustainable product development.

As companies face increasing accountability for resource efficiency, circularity measures like repair or remanufacture offer a promising solution. Modeling expected product lifecycles in early design phases is crucial for planning their effective application. This paper introduces a hierarchical lifecycle model to represent component-specific lifecycle paths across all product architecture levels in a single model. Our approach ensures consistency across hierarchy levels and facilitates precise application of component-specific circularity measures, promoting effective circular product design.

The circular economy is central to sustainability, yet many products cannot comply to its principles. Equally, assessing circularity remains challenging due to the multitude and complexity of approaches. This contribution presents a feasible multi-criteria assessment tool, grounded in established DfCE principles, for evaluating product circularity at various system levels. Developed through an exploratory literature review, it integrates qualitative and quantitative indicators with a retention-option-based weighting system. Its applicability is demonstrated through a medium-complex case study.

We introduce a method that turns online customer reviews into design insights. By analysing smartphone reviews, we extract the product features customers talk about and identify the sentiment linked to them. The approach combines Large Language Models (LLMs) with the Kano model, showing how specific features influence satisfaction or dissatisfaction. The results are coherent with the dimensions of the Kano model. The work demonstrates that LLMs can be informed and constrained by established design frameworks, bridging LMMs and design reasoning to provide theory-grounded insights.

Conventional user research with human participants faces significant challenges, including substantial time and resource requirements, and limited scalability. In response, this study presents an efficient, cost-effective workflow driven by large language models (LLMs) for simulating user research with synthetic participants (SPs) at scale. In a case study in design augmented reality for education, SPs’ open-ended answers were plausible and comprehensive, yet semi-open and closed items diverged from those of humans. SPs can augment early qualitative work, but cannot replace human studies.

Chatbot-based surveys offer low-burden, in-situ data collection, yet unconstrained LLMs often drift from research aims. We conducted 359 ultra-short, post-experience voice interviews in a public venue to compare a framework-guided LLM, an unconstrained LLM, and fixed questions. The guided approach produced significantly longer responses than fixed questions and yielded the richest diversity of process-specific accounts. These findings show that probe control is essential for eliciting actionable, experience-grounded feedback in real-world, time-limited settings.

Design thinking fosters creativity but it’s susceptible to cognitive biases. We propose a rule-based framework supported by large language models that uses a Prompt-Reflection-Reframe loop to identify bias mechanisms in designers’ verbal reasoning and generate theory-grounded reflective prompts. Through scenario-based evaluations, we validate the framework’s theoretical foundations and establish a methodological basis for supporting bias-aware design practice.

Design research lacks a unified methodological framework, leaving researchers underexposed to diverse approaches and limiting informed method selection. This paper proposes a multidisciplinary cartography of design research methods, mapping research designs, data collection techniques, associated risks, and required resources. It provides a structured overview to support method selection and planning. This non-exhaustive, high-level cartography serves as a proof of concept to demonstrate feasibility and lay the foundation for a future community-driven Design Research Quality ecosystem.

Industry–academia collaborative research faces a persistent challenge: how to promote effective knowledge transfer while maintaining both scientific rigor and industrial relevance. This paper introduces nine methodological guidelines for a Dual Impact Research Methodology (DIRM). The guidelines include mechanisms such as dual-purpose artifacts, hybrid evaluation criteria, and institutionalized knowledge capture. The paper concludes the guidelines provide a coherent yet adaptable framework for achieving dual impact: advancing theory while delivering actionable industrial value.

Traditional design methods fall short for complex socio-technical systems where social and technical elements co-evolve and emergent behaviors resist decomposition. This paper proposes a seven-stage Design for Complexity Framework integrating systems science and design theory. Stages 3–6 form an iterative co-evolution space where modeling, architecture, and stakeholder co-design mutually shape problem and solution development. A healthcare example illustrates how the framework's co-evolutionary approach addresses coordination failures that purely technical or purely participatory methods miss.

Product variety increases complexity across product, process, and organizational domains, yet existing complexity measures offer limited guidance for design decisions. This study implements established metrics within a framework to enable consistent assessment across domains. The results reveal substantial redundancies among these metrics - with the notable exception of modularity, emphasizing its central role in complexity management. The consolidated measure set provides practitioners with a systematic basis for evaluating design strategies and managing complexity in product families.

As systems become increasingly data-centric, interdisciplinary engineering design faces growing complexity and interdependencies. This paper investigates how a combined Design Structure Matrix (DSM) and graph-based modeling approach supports interdisciplinary decision-making by revealing critical data dependencies, compared to standalone DSM or graph models. Based on a case study on autonomous public transportation and expert input, the results illustrate complementary insights enabled by the combined approach and discuss its implications for industrial system design.

Technical Debt (TD) is a buzzword that has gained traction among IT practitioners and researchers, as a financial metaphor explaining how existing elements can hinder future changes. In engineering design terminology, managing TD can be equated to managing the redesign of existing systems, a task in which change propagation plays a key role. Via a partnership with Ubisoft, a game development firm that owns and operates a global IT network, we present a case study showcasing a novel way to explore interfaces and propagations in engineering systems interventions, analyzed using Axiomatic Design.

Product configuration systems support customized design in complex engineering. However, as products grow in complexity, the configuration model also grows, making it important to manage these models effectively. Based on industrial case studies, This study shows how companies structure their configuration models and how modularization helps improve flexibility, maintainability, and scalability. The results provide empirical insights and practical guidance for structuring robust configuration models in complex engineering contexts.

With the introduction of SysML v2—featuring a new metamodel and standardized APIs—new opportunities arise for automating MBSE tasks through generative AI. However, existing LLM approaches lack interoperability due to limited support for complex SysML v2 API operations. This paper presents a framework for implementing Model Context Protocol (MCP) servers for SysML v2, enabling LLMs to interact with system models via the API. This framework provides a foundation for agentic MBSE by allowing automation of modeling tasks while maintaining interoperability across tools and generative AI systems.

This study presents the “P-Heroes”, a superhero family designed through a Research through Design process to support children with urinary incontinence. Inspired by children’s own superhero drawings, expert input, and validation sessions with children, the design evolved through iterative prototyping. Each hero embodies traits such as persistence, comfort, emotional regulation, resilience and structure. Together, the "P-Heroes" reflect the diversity of children’s journeys toward continence and serve as a playful conversation starter to help them express their needs and experiences more openly.

The market for transcranial direct current stimulation (tDCS) expands. tDCS is a non-invasive technique that delivers a weak direct electrical current to the scalp via electrodes. It is used for enhancing cognitive functions and mood. Existing research addresses technical aspects; yet, understanding users’ perceptions and broader design issues are crucial for acceptance and usability. This paper investigates the perceptions of tDCS practitioners, volunteers, and designers on commonly used electrode fixation methods. It presents design dimensions and recommendations for novel tDCS designs.

A parametric framework for personalised hand-wearable cooling devices is presented to optimise fluid and thermal performance using patient-specific anthropometrics. Iterative prototyping phases validated improved uniformity and efficacy. The approach bridges ergonomic customisation and thermal optimisation, enabling scalable, clinically effective wearable heat-exchangers for diverse patient populations. This study builds on prior work on personalised scalp cooling with Paxman, applying parametric principles to limb cooling for Chemotherapy-Induced Peripheral Neuropathy prevention.

User-product-environment interaction is a dynamics reflected in the concept of affordance and, consequently, in the user experience. The study of these three concepts is indeed evidently interconnected and mutually dependent. Accordingly, this study presents a tool developed for coding the user experience, namely UX grammar (Dabouis et al., 2024a, 2024b), as a suitable framework for further capturing affordances and their characteristics. An application of the UX grammar model, along with affordances evaluations derived from the coding output, is presented to validate the proposed methodology.

Additively manufactured particle-damped (AMPD) beam structures produced by PBF-LB/M are tested in the Einstein-Elevator under microgravity conditions. The first bending mode is evaluated by laser Doppler vibrometry and compared with results from microgravity experiments, using power spectral density inputs resynthesised from those runs and replayed on a shaker. Frequency-domain transmissibility with confidence intervals shows stable mode-specific damping behaviour and supports a validated workflow for future space structures.

This paper explores the application of Web3.0 technologies to provide de-centralised secure, private, and provenance preserving trust networks for society's increasingly digital design and manufacture workflows. It provides an overview of the key technologies involved and an example of a minimal trust framework required for issuing jobs between actors and machines in a makerspace. A comparison with centralised AM farm platforms is made and demonstrates how Web3.0 can support emergent trust structures compared to fixed centrally managed structures that actors need to agree to.

E-scooters have cemented their position as a convenient transport solution in urban areas, with hundreds of millions of e-scooter trips completed globally each year. This study investigates and presents useful tyre performance data for 12 e-scooter tyres, including three novel 3D printed tyres made from 90A TPU. The results highlight the potential of 3D printed tyres to provide comparable performance to existing e-scooter tyres. The information presented in this study is useful to better understand the energy losses associated with these devices.

Industry is experiencing rising thermal loads, so geometries that improve energy transfer are needed. However, defects arising from overhang in additive manufacturing affect the functionality of triply periodic minimal surface (TPMS) based heat exchangers. This study addresses how TPMS superposition affects heat transferring and overhang critical surfaces. The objective is to quantify the functional and manufacturing trade-offs, and to identify the optimal hybrid cells formed from gyroid, Schwarz and diamond units.

The paper presents an automated decision support application for sustainability metrics in product design by leveraging semantic technologies. The proposed framework utilizes an ontology for structured representation of design characteristics and correlating lifecycle data. A rule-based approach is investigated for enabling automated reasoning. The implementation showcases real-time impact assessments based on CAD models, supporting sustainable product development. This approach demonstrates significant potential for advancing lifecycle design practices in industrial contexts.

Common ecodesign tools, such as LCA, remain challenging for designers, limiting their use in product design. At the interface between design and ecodesign, CAD-environmental assessment integration could be a solution. We aim to complement previous assessments of such tools, focused primarily on assessment capabilities, by proposing an evaluation framework based on common LCA limitations. Applied to three commercial CAD modules, it highlights differences in operational capabilities and limitations, providing a differentiated appreciation of CAD-integrated tools.

This paper presents the IMPRINT framework, a structured method to enhance the interpretation of EF 3.1 midpoint indicators in energy-intensive industries. By clustering the 25 indicators into five domains—climate and energy, human health, aquatic quality, air emissions, and resource depletion—the framework improves the readability of LCA results, highlights environmental hotspots and trade-offs, and supports more informed decision-making in industrial technology evaluation and sustainability planning.

Early embodiment design requires sustainability considerations for design alternatives that traditional LCA techniques cannot efficiently support. This paper presents a fast parametric LCA model that expresses environmental impacts as linear functions of material mass, recycled content and recyclability potential, allowing rapid evaluation of design alternatives. Applied to load break switch drives of two generations, the model achieves a MAPE below 5% relative to conventional ISO-compliant LCA results and demonstrates its capability to support design space exploration.

Early-stage concept evaluation is critical for selecting viable designs. This study introduces a multi-agent generative AI framework for assessing concepts across four configurations: AI with retrieval-augmented knowledge, AI without external knowledge, human experts, and a hybrid approach. The findings show that AI panels tend to produce uniform evaluation patterns, while retrieval-augmented knowledge alters rating behaviour without leading to closer alignment with human judgement. Hybrid setting achieved closest alignment, indicating AI is effective when combined with expert interpretation.

Suboptimal product design and compliance failures lead to economic losses. While AI excels in domain-specific tasks like defect detection, existing solutions lack cross-domain reasoning and explainability. This paper presents Product Singularity, a universal AI framework that integrates multimodal data (images, text, etc) for comprehensive product evaluation across quality, safety, performance, ergonomics, and compliance. A proof-of-concept in consumer bottles validated by experts achieved 90% agreement and reduced evaluation time. Its modular design supports adaptation to other product categories.

This paper presents a deep learning-based approach to automatically classify the rust level of screws using ResNet-18 and MobileNetV3 convolutional neural networks. A controlled salt-spray chamber was used to simulate corrosion on metal screws over 0h, 48h, 96h, and 168h of exposure. Images were processed with a circle-detection algorithm to extract individual screws, followed by data augmentation and training. The final models achieved a classification accuracy greater than 94% on the validation set.

Cost planning for Product-Service Systems faces rising complexity, making life-cycle cost estimates essential. This paper investigates how machine learning (ML) can be applied for life-cycle cost estimation in product development. A literature review was conducted to identify ML-based methods, classify them across life cycle phases, and compare them against traditional methods. Results show that traditional models remain transparent but limited in early stages, while ML methods achieve higher accuracy in data-rich phases. A clear research gap exists for hybrid models and end-of-life costing.

This study proposes a framework to map biomimetic innovation progress along TRLs and identify recurring development patterns. Pilot results reveal stagnation between TRL 2–4, linked to generic upscaling struggles and biomimetic-specific barriers. Emerging hypotheses suggest early onset of upscaling challenges post-POC and influence of biological model knowledge on progress. Study insights open paths for methodological work to bridge POC obtention and validation difficulties, and for further use of the framework on bigger datasets, to build a baseline for biomimetic innovation development.

This paper introduces a visual tool that facilitates engagement with speculative design futures while exposing the boundaries of understanding. Building on the Future Archeologies Method, the canvas guides participants through tactile speculation, scenario-making and reflection via eight components structured around a Line of Speculation. We tested the tool in two workshops with community members and futures experts. Results suggest that the canvas (1) accelerates the process, (2) supports collaboration, and (3) enables accessible yet radically creative futures thinking.

This paper reconsiders the concept of sense of place through the perspective of worlding. While often defined as the emotional and cognitive bond between people and environments, this study expands it toward a design-oriented view that reinterprets and regenerates local meanings. Based on a design inquiry into regional contexts, it examines how making and storytelling can translate local materials into prototypes that evoke new relations to place. The study suggests worlding as a generative process through which place is continuously reimagined and experienced within dynamic local contexts.

This study explores how spatial and sensory design influence critique interactions. The "Room for Critique" prototype was developed through a research-through-design process grounded in neuroaesthetic research and evaluated across five PhD feedback sessions. Findings indicate that spatial layout and multisensory ambiance shape focus, perceived equality, and comfort. The paper introduces a dual framework of spatial neutrality and affective design and proposes six actionable guidelines for creating feedback environments that support calm, constructive, and balanced dialogue.

Connected and Autonomous Vehicles (CAV) are increasingly complex, making resilience hard to guarantee. Resilience means maintaining functionality and availability despite disruptions while ensuring safety. Designing a resilient system requires system‑level analysis early in the concept phase to identify and mitigate risks, thereby securing reliability and availability. This paper introduces the Automotive Resilience Maturity Model (ARMM), which evaluates automotive systems’ resilience levels based on their system architecture.

Critical infrastructures are complex, interdependent systems on which our societies are reliant. A better understanding of these interdependencies is vital to improving their functioning and resilience. While various studies and surveys have been conducted, we aim to cast a new perspective by focusing on what Rinaldi et al. introduced in 2001, as "logical interdependencies" and their modeling and simulation considering the human factor, and by adopting a cross-area approach to guide future works through the identification of research directions and common design challenges, good practices.

Large railway projects suffer from major cost overruns and delays, partly due to project complexity. This study explores how such complexity emerges in the early design stages and affects the project outcomes. Data from 14 interviews were compared with four project complexity frameworks. The results indicate that complexity is mainly institutional rather than structural. Optimism bias, fragmented requirement governance, and weak coordination create self-reinforcing loops of cost growth, showing that governance and decision processes, not technical uncertainty, drive early-stage complexity.

In recent years, redundant sensing has gained attention as a means to improve the resilience of autonomous cyber-physical systems (CPS). Resilience, however, has not been stringently defined. In this work, a standard-based understanding of resilience was derived, resulting in 11 properties that contribute to resilience. This understanding was used to analyse literature on autonomous CPS, their domains, operating environments, and the disruptions they encounter. A subsequent analysis shows a prevalence of measures acting during the disruption, while anticipating or learning measures are scarce.

CMF design goes beyond aesthetic enhancement by shaping the user–product relationship through functional, ergonomic, and symbolic dimensions. The qualitative analysis shows that colour and material primarily influence first impressions and emotional engagement, while surface finish reinforces perceived quality, symbolic meaning, and identity expression. Ergonomic performance is closely linked to material and tactile surface properties. Overall, CMF acts as a strategic design tool that strengthens brand identity, product desirability, and long-term emotional attachment.

The aim of this exploratory pilot study is to examine the subjective user experience of operating a pillar drilling machine and a minting machine. Clustering show three recurring perception profiles: predominantly positive, negative/demanding, and mixed. Operator posture strongly influences experience, while individual factors such as gender are less predictive. Ground-level, medium-reach positions get the most favourable ratings. The findings provide a first basis for extending behaviour cards with perception-based "experience cards" to support user-centred ergonomic design.

This study aims to examine the effects of luminance contrast on cognitive experience. An experiment was conducted under controlled conditions with high/low luminance contrast settings. Perceived time was measured by tapping rate, while subjective ratings of astonishment, concentration, comfort, and a semi-structured interview were collected. The results showed that higher luminance contrast elicited greater astonishment, and perceived time was associated with concentration and comfort but limited with astonishment, suggesting that astonishment may involve higher-order cognitive reappraisal.

Digital news interfaces often prioritise speed and visual intensity, overlooking the material conditions through which information is experienced. This paper investigates how a text-only news device influences perceptual clarity, attention, and emotional tone in everyday use. Using a research-through-design approach with a deployed design probe, findings reveal material and formal qualities shape news engagement, introducing the concept of "minimal media" and informing the design of calmer information devices.

This work presents the design and simulation-based validation of a next-generation cell-to-pack battery system for hybrid-electric refrigerated transport. The configuration integrates a welded stainless-steel frame, liquid-cooling system, and power-electronics module within a compact modular structure that improves assembly efficiency and adaptability. Finite-element and thermal analyses confirmed compliance with vibration and thermal requirements, achieving improved volumetric efficiency and scalability. The results provide a validated foundation for prototyping and future optimization.

This paper proposes a methodological approach for designing smart composite hydrogen tanks using strain gauges and finite element analysis for continuous structural health monitoring. Simulation identifies critical stress points to optimize sensor placement. Laboratory burst and cyclic tests provide a baseline and proof of concept for a remaining useful life analysis, improving safety and resource efficiency in hydrogen storage. Results demonstrate that strain data reflect stress patterns and material responses, supporting effective monitoring of tank condition during pressurization cycles.

This research was initiated to investigate the dynamic behaviour of Len Lye's Kinetic Sculpture Trilogy. Safety concerns and reliability issues have led to the call for a redesign of the original mechanism. Numerical methods including FEA are used to model the artworks dynamic behaviour. Vibratory mode shapes and natural resonance frequencies of the artwork are found. Motion Capturing (MOCAP) is employed to experimentally analyse and record the dynamic behaviour of the artwork. Whirling behaviour in the form of mode shapes was recorded equally below and above the resonance frequencies.

A power electronics pack for refrigerated transport was redesigned to overcome limitations in power density, efficiency, and compactness. An iterative approach combining CAD modeling with thermal and mechanical simulations guided geometry, material selection, and cooling design. Applying Design-for-Excellence principles, multiple submodules were consolidated into a single enclosure with three functional zones. Structural and thermal validations confirmed compliance under extreme conditions. The optimized pack achieved 31 kg (57% reduction) and 671 W/L power density (338% improvement).

This research addresses the perceptual conflict wherein consumers place greater trust in independent repair providers than in brand-led initiatives. Positioned within design for sustainability discourse, this paper, firstly examines repair service providers’ perspectives, revealing their challenges & systemic complexities. Secondly it presents their role in shaping the industry through skill transfer, community building etc. Lastly, design interventions are analyzed using systemic leverage points framework to expose varying depths of repair services to bring systemic change of fashion sector.

This study diagnoses Circular Economy practices in Brazil’s food-away-from-home sector using survey data (n=1,002) interpreted through the Design for Sustainability framework. Results show fragmented, technocentric actions and weak collaboration, with minimal regenerative practices. Mapping gaps across DfS levels reveals leverage points for redesign in governance, services, value-chain relations, and ecological loops, highlighting the need for systemic, design-led transitions.

Low-tech approaches and practices have developed in recent years, both as a model of strong sustainability in a context of polycrisis, and as an alternative technological discourse opposed to the prevailing techno-solutionism. Various organisations have drawn inspiration from them for the transition or redirection of their system. This article refers to this as "integration of low-tech approaches". It provides an overview of such integrations in industries through the study of seven cases. Then, it discusses the challenges of their definition, which underpin their sustainability potential.

Increasing e-waste demands design approaches beyond repair and recycling. This study explores adaptability, upgradability, and flexibility, as proposed to mitigate product obsolescence. Processes and guidelines are examined from legislative, academic, and industrial perspectives, complemented by a workshop (n=16). Applications show potential yet remain scarce and uncover rebound effects. A common framework for adaptable design is suggested. Future research is recommended to evaluate it, expand guidelines empirically and address barriers like consumer perceptions and business model challenges.

AI in Requirements Engineering (RE) relies on industrial data, yet safety and privacy risks limit its use. While the GDPR distinguishes only between anonymization and pseudonymization, we use neutralization as a semantics-preserving technique. In AI-supported RE, data heterogeneity and cross-domain variability impede model training. We propose guidelines for semantics-preserving preprocessing for RE datasets based on ISO 29148 criteria, showing that neutralization does not compromise semantics. The approach enables industry–academia collaboration through AI-assisted RE in product development.

This paper examines integrating Large Language Models (LLMs) into the TRIZ contradiction matrix (TRIZ-C+LLM) to support engineering students in creative problem-solving. Experiments with three problems show that LLMs did not always improve design quality for complex tasks but reduced cognitive workload, improved understanding of contradictions, and increased perceived usefulness. Prompting experience strongly influenced outcomes, highlighting both the promise and limits of combining TRIZ with generative AI.

In this work, we propose a multimodal, language-model–based design assistance framework for the design ideation stage. The framework leverages large language models (LLMs) to interpret user intentions with mood boards, enrich initial ideas with essential contextual details, and produce structured instructions for visual language models (VLMs) to enhance the accuracy and consistency of visual feedback.

In plant engineering and industrial solution business, the focus is on developing customer-specific products. At the same time, finding suitable templates from previous projects (adaptation design) is essential for efficient product development. Conventional search tools in PDM/ERP systems are not suitable for this purpose, which is why structure-based similarity search was proposed in an earlier article. In this article, a feasibility study is conducted to determine what typical use cases exist and whether these can be easily SME-implemented with a large language model (LLM) as a search tool.

This paper examines the role of product design in digital age-inclusivity. As digitalisation accelerates, older adults face persistent exclusion. We argue that interaction-level tensions between the intangible nature of screen-based systems and embodied human perception contribute to this divide. We introduce the Embodied Inclusion Framework as a structured approach to enhancing physical accessibility, cognitive usability and somatic safety in digital interaction.

This research describes a tool based on subjective well-being, value, and practical design. We present a Value-Driven Ladder Canvas and 78 Value Insight Cards to support SWB-oriented practice. Studies 1 and 2 focus on discovering insights based on the user's product experience and converting them into toolkits. Study 3 evaluates and verifies toolkits with SUS, UEQ, PANAS, etc. This research highlights the connection between Needs and positive experiences, providing actionable guidance. Our findings demonstrate that Canvas and Insight cards are designed to improve SWB sufficiently to be useful.

We reviewed 36 web-based toolkits supporting health and care design and improvement and identified five classification dimensions: novelty (novel or established tools), scope (specialised or generic), origin (research- or practice-based), motivation for use (risk reduction or benefit enhancement), and application level (individual or group use). We also identified five types of toolkit developers and seven end-user roles. Most toolkits were generic and practice-based, developed by commercial or academic actors, targeted at practitioners and leaders, and supported both individual and group use.

Providing effective assistive technologies is challenging due to misalignment with users’ needs, often leading to product abandonment. Occupational therapists play a key role in prescribing, adapting, and creating personalized ATs, yet technical and marketplace barriers complicate this work. Computational design tools can support OTs, yet no clear design guidelines currently exist. From four case studies, we identify six considerations: workflow integration, intuitive interfaces, real-time visualization, collaboration, customization, and safety, to guide OT-focused design tool development.

While top-down System Engineering supports the definition of system boundaries and interfaces, practical implementation often proceeds in a bottom-up manner, resulting in the need for model integration of SysML models. This is currently hindered by inconsistent naming, different abstraction levels, and unclear interfaces. In this work, a novel approach to integration is proposed, utilizing RDF knowledge graphs and LLM-driven entity alignment with similarity thresholds to perform semantic fusion. A practical use case shows correct consolidation based on cosine similarity thresholds.

Engineers often treat margins as buffers or excesses which are added to parameters, appearing much later in the product development process. However, many key lifecycle properties e.g., reliability stem from early architectural decisions which either need margins for their enablement or create margins in the process. These buffers are rarely treated explicitly as margins. This paper argues that there is a clear relationship between architectural objectives and margins and explores four examples of ilities providing a new perspective for reasoning about ilities in early system design.

This paper compares nine Model-Based Systems Engineering methodologies with ISO 15288:2023 on the basis of their technical process coverage, to assess the extent to which the methodologies and standard are aligned. The technical processes covered include the definition of stakeholders, stakeholder needs, system requirements, architecture, design, verification, and validation. Two comparison criteria are also linked to the standard, namely, traceability and customisation. The results indicate that the definition of system requirements and system architecture are common core components of SE.

Systems engineering continues to face challenges such as high manual modelling costs and insufficient tool support. With the rising importance of AI methods, SE assistants, software systems that support engineers in typical SE tasks, are gaining attention. However, there is currently no systematic classification of such assistants. At the same time, their usefulness depends heavily on the quality of the human AI interaction. This paper addresses these gaps by systematically categorising SE assistants and analysing the role of interaction design in their development and application.

This study modelled service dynamics, specifically focusing on cognitive misalignments among actors, and conducted a multi-agent simulation using Bayesian inference, referring to an improvisational dance experiment. The results revealed that individual cognition influences context convergence: "No decay" condition fixed initial biases and hindered convergence, whereas faster decay increased fluctuation but enabled reconfiguration, suggesting the need for unlearning. When actors weighted others’ expressions less, cognitive misalignments widened despite strong subjective conviction.

This paper explores the assessment of employee induction as a service, through the lens of service design. The service assessment methodology was a multi-method and a multi-stakeholder approach, uncovering how processes, touchpoint interactions and servicescape shape onboarding experiences of new joiners. The paper highlights the researchers’ reflections about using the methodology in organizational context and portrays the value of assessing organizational services through service design lens. The reflections offer practical insights applicable to assessment of other organizations services.

This paper addresses the lack of empirically grounded user typologies for understanding acceptance of autonomous buses in the Munich Metropolitan Area. We close this gap through a large-scale online survey and a clustering approach based on mobility preferences and subjective expected utility. The results identify five distinct clusters of users with varying acceptance levels, showing that successful autonomous bus adoption requires tailored communication, service design, and integration strategies.

Human-Centered Design focuses on individuals who struggle to grasp the relational aspects crucial in designing for care. This proposes a relational framework that visualizes the relational expectation misalignments between stakeholders' perceptions. We extend the Theory of Planned Behavior to model dyadic care relationships. Expert interviews and autoethnographic analysis evaluated the model. Our findings reveal two layers of misalignments: the model's ability to describe the structure of conflict and its potential as a reflective tool for stakeholders to resolve conflicts.

This paper presents the Designable Inclusive Design Methodology, a modular framework that was tested across two consecutive semesters in a company-sponsored design studio within academic settings. The toolkit provides practical and repeatable methods that embed inclusion throughout the design process. Developed through research through design, it offers a fast and flexible alternative to large-scale curriculum reform. Initial findings indicate that the use of the methodology enhances student confidence, fosters deeper reflection, and promotes inclusive thinking in everyday studio work.

This paper draws on Method Content Theory and the Method Teaching framework to analyze the User Testing Toolkit, in its dual role as a design and a teaching tool. The paper shows how the toolkit, by supporting the contextualization of user testing in a broader design context, not only support design activities, but also the critical reflection on design context and actions, necessary for development of appropriate design practices. As such, the paper sheds light on how design methods can be developed to support both effective design action and the development of appropriate design practices.

Engineering outreach helps to address the STEM "leaky pipeline," yet often lacks inclusive design guidance. This paper presents a methodology for co-designing activities using Inclusivity, Diversity, Equity, and Accessibility (IDEA) principles. Through three workshops integrating pedagogy training and storytelling, the method proved effective: participating engineers’ IDEA knowledge jumped from 20% to 75%, and delivery confidence rose from 24% to 71%. This methodology successfully equips professionals with the confidence and skills to foster a more diverse engineering workforce.

This study identifies the challenges design educators face in understanding, using, and teaching design methods and derives features from these challenges for an enhanced methods repository to support design practice and pedagogy. Interviews with 21 educators yielded 14 distinct themes of challenges, based on which the following features are proposed: (a) detailed method information with context-based recommendations and teaching resources, (b) a platform to learn and practice with guidance, and (c) step-by-step walkthroughs with cross-disciplinary examples.

Electronic waste is one of the fastest-growing waste streams, yet only a small fraction is properly recycled. Many challenges in recycling originate in product design; for example the choice of materials and joining methods. This paper presents the first version of the Design for Recycling of Electronics Guide, developed to bridge the gap between design and recycling practice. Based on case studies, shredding experiments, and method reviews, it provides practical guidance to help designers anticipate and improve recyclability during product development.

The EU 2050 carbon neutrality target drives growing interest in renewable energy (RE) integration in building design, yet social housing organizations face difficulties integrating them early in design due to complexity and high upfront costs.  This paper presents a three-step method based on the Set Based Design approach to define the design space using a morphological chart. This offers a robust and adaptable framework to formalize and structure domain-specific knowledge, clarifies design options, and supports informed decision-making for RE integration in social housing design.

Implementing circular business models (CBMs) like Product-as-a-Service entails high uncertainty, necessitating costly and prolonged business experimentation. To efficiently mitigate this uncertainty, Bayesian Optimal Experimental Design is applied to the CBM context, selecting conditions that maximize the Expected Information Gain for unknown CBM parameters. Applied to an air conditioner subscription case study, the method successfully identified optimal conditions from 124 candidates. This approach facilitates CBM implementation by efficiently minimizing uncertainty under limited resources.

This paper proposes a methodical framework for developing functional surfaces and coatings for circular automotive applications. It addresses three gaps: the missing classification of surface mechanisms, limited empirical PSPP models, and the lack of an integrated link between microstructural surface design and system-level development. The framework connects top-down design with bottom-up materials engineering, introduces working-principle analogues in design catalogues, and offers the use of DoE and sensitivity analysis to build predictive PSPP models.

Current generative artificial intelligence for Computer-Aided Design (CAD) optimizes for geometric similarity, neglecting engineering criteria like functionality, manufacturability, and sustainability. This paper addresses this gap and proposes a conceptual framework to reorient generative CAD from replicating shapes to achieving function. We introduce two hybrid training strategies: a pre-learning approach using synthetically labeled datasets (evaluated via FEA, CAM, LCA) and a self-learning approach where GenAI uses these knowledge-based tools as a reinforcement feedback loop.

This study investigates the mechanical performance of PA6-CF and PLA components fabricated with desktop material extrusion additive manufacturing. To define the geometry, low-cost 3D scanning was used in combination with Generative Design in Autodesk Fusion 360. PA6-CF outperformed PLA by 25% in pre-failure peak load (1.85 kN vs. 1.47 kN), despite the datasheet values suggesting a 450% advantage in interlayer strength. Poor interlayer bonding of PA6-CF is attributed to low layer temperatures (87–136 °C) during the printing process, indicating that a chamber temperature of 60 °C is inadequate.

This paper investigates how prompt structure influences the use of Large Language Models in early engineering design. A structured prompting framework aligned with the engineering design cycle is proposed to support Design for Multi X reasoning and more coherent problem exploration. Using a prosthetic knee-cover case study, six engineering designers engaged with both generic and structured prompting workflows. A mixed methods study examines how prompt organisation shapes LLM assisted reasoning, problem framing and the articulation of design constraints and considerations.

The use of artificial intelligence, especially large language models (LLMs), is increasingly explored to support early system development. This paper evaluates low-threshold LLM-based tools for supporting conceptual design. Through an experiment, two LLM-based tools were compared generating alternative solutions using an existing function model of an electro-mechanical system as input. Functions were provided in natural language and using the Functional Basis. Results show limitations and significant potentials for effective and efficient conceptual design support.

This study examines how ChatGPT support influences verbal communication in synchronous collaborative CAD activities. Using a verbal protocol analysis of teams solving an embodiment design task, the results show that ChatGPT-supported teams communicated less, devoted less verbal communication to problem- and analysis-related communication, and shifted toward process and solution synthesis, indicating a shortened design co-evolution cycle in which teams move more quickly toward generating solutions. Future work should integrate these findings with broader teamwork and taskwork analyses.

This study examines how engineers formulate natural language prompts for searching existing assemblies in mechanical design. A survey with 48 engineers produced 169 prompts for different assemblies. Results show that prompts are mostly written as bullet points with an average of three and up to seven requirements. The engineers describe assemblies mainly through implicit functional descriptions and geometric or physical parameters. These findings form an empirical basis for developing generative AI-driven, prompt-based systems to foster design reuse.

Speech-capable AI systems introduce new possibilities for communication and collaboration in design, yet methods for analysing human-AI interactions through speech remain limited. This paper proposes and applies a method for analysing conversational interactions in speech-based human-AI design activity. Grounded in conversation analysis, this method reveals how conversational structure and designer roles emerge through spoken interaction, offering an analytical framework for examining communication, cognition, and collaboration in design.

We designed and evaluated an AI-based Application to enhance human creativity in design thinking workshops. The results indicated that AI hindered human creativity, resulting in fewer idea generations. The findings from quantitative and qualitative analyses comparing the only-human and human-AI teams indicated that AI contributed to the usability of ideas during the divergent phase and supported humans in converging on more novel ideas. The further development of the application is necessary to consider how humans can collaborate with AI without relying on it.

Transdisciplinary (TD) engineering design is a useful approach for engineers when responding to the wicked problems of sustainability and systems transitions. A research gap lies in understanding the quality criteria for TD engineering design, and in investigating to what extent existing studies in the field exhibit TD qualities. In this semi-systematic literature review we develop a framework of quality criteria for TD engineering design and then analyse relevant literature using the framework. The paper concludes methodological recommendations for future TD engineering design studies.

As environmental sustainability pushes organisations to integrate sustainable system design into engineering workflows, there remains a gap in translating high-level environmental objectives into actionable design practices. This research addresses the integration gap by developing a Model-Based Systems Engineering enabled ecodesign approach tailored for early-stage product design at a Dutch radar system developer. A ten-step methodology integrates carbon footprint analysis with MBSE functional data to identify architectural hotspots, enabling data-driven decisions within existing workflows.

This article motivates the use of MBSE and SysML for organizational development and argues for a model-based integration of sustainable leadership (SL) into sustainable manufacturing (SM). We discuss whether modeling requirements resulting from SM and SL literature can be met by SysML and introduce an initial black box perspective and meta-model. The work is part of a larger research project aiming to transfer findings from SM and SL research into a model-based integrated SM in order to support a human-centered perspective. This work shows that SysML is suitable for organizational use cases.

Early design decisions are central to many circular economy approaches and largely determine environmental impacts. Yet LCA practice is often fragmented and difficult to integrate into engineering development processes. This paper proposes an MBSE approach that represents and computes LCA-based (LCIA) impact indicators within SysML models using reusable, configurable model elements and automated import of characterization factors. A proof-of-concept implementation demonstrates technical feasibility, illustrates process integration, and discusses limitations and future work.

This study investigates the gap between objective and subjective garment quality and explores how insights from subjective quality can inform industry practices to enhance product longevity. Based on 16 interviews, findings reveal that consumers rely on subjective-intuitive aspects to form quality expectations and subjective-aligned aspects that emerge through experiences. Qualitative use emerges as a crucial connection between subjective and objective quality. These insights inform design strategies promoting garment longevity across three phases: design, primary retail, and secondary retail.

This study investigates how product attributes shape user interpretation of unfamiliar products in terms of functions and context of use. This was made possible through an experiment involving 71 participants who were administered three unfamiliar end-use products without any additional cue. Findings reveal that visual cues, material semantics, and contextual imagination shape understanding, with misinterpretations often arising from analogical reasoning and partial cue activation. Designers should harmonize cues and leverage material symbolism to guide user perception and acceptance.

As concerns about climate change and biodiversity loss intensify, circular economy strategies are crucial for decoupling economic growth from resource depletion. Yet, the consumer behavioural dimension including returning, repairing, and accepting refurbished products remains underexplored, in particular in the bicycle industry. By conducting a survey of bicycle users, this study finds a strong willingness to engage in these slow-the-loop practices, driven by cost savings, convenience, and trust, but hindered by knowledge gaps and quality concerns, implying recommendations for manufacturers.

There is an increasing need to understand how rising environmental pressures and the EU’s PPWR (Packaging and Packaging Waste Regulations), which requires more sustainable and standardised packaging, affect brand identity. This paper evaluates how standardisation alters brand recognition and the extent to which visual and verbal cues can preserve brand identity and heritage. Mixed-method case studies show that coherent cues can maintain authenticity, brand meaning, and consumer acceptance emphasising the importance for brands seeking to balance sustainability with consumer perception.

Industry 4.0 creates a need to transform education to meet evolving labor market skills. This study reviews and compares major instructional design models ADDIE, SAM, ASSURE, MISA, and Dick and Carey using Gropper’s framework enriched with Education 4.0 criteria. Results show these models offer structure but remain module-focused and lack a systemic perspective. The research proposes a broader, engineering-based approach using TRIZ principles to create an integrated learning ecosystem that supports agile, flexible, and continuous skills development aligned with Industry 4.0.

First year students have heterogeneous prerequisites for acquiring competences in engineering design education caused by varying school education or vocational training. Individual support is challenging in large groups and frontal lectures. An approach for individualized support is adaptive e-learning. This paper analyses the impact of offering an adaptive e-learning environment for engineering design education (AdE-Le). Analysis results for data of the same course from three different years give insights into acceptance and impact of using AdE-Le on acquiring professional competencies.

This paper explores how sustainability is integrated into technical courses across Danish design engineering programmes. It finds a gap between program goals and technical courses, where sustainability remains absent or at best implicit. Structural barriers like outdated materials and traditional teaching hinder integration. We propose three reforms: make sustainability an explicit learning objective, update teaching materials and use design-oriented examples and exercises. This will strengthen sustainable change an integral aspect of design engineers training.

This paper presents a pedagogical tool for the co-design of systemic and resilient Nature-based Solutions for carbon removal. Tested with nine participants, it significantly improved understanding of NbS dynamics. Wilcoxon tests showed higher scores for all thinking skills derived from Bloom’s taxonomy (p < 0.05), with notable gains for higher-order skills. Participants reported that the tool was intuitive and engaging, fostering collaborative learning. Results confirm its educational value and potential to engage creative and resilient NbS design.

Digital product passports (DPP) are intended to provide reliable, interoperable information throughout the product lifecycle, enabling compliance, transparency, and circularity. This paper seeks to address the question of how a DPP can be effectively implemented in the field of engineering, developing a referenced, industry-independent architecture for this purpose. The layers for data, communication, logical architecture, functions, and potential are derived from regulation and practice; integration, interoperability, and validation requirements are discussed using a use case.

Despite the many circular economy (CE) design frameworks, implementation is limited. This study interviews six Swedish design firms (producers/consultancies, small/large) to compare CE barriers. Results show small producers face more value chain challenges, while large producers focus on design. Consultancies emphasize economic/legal factors. Organizational silos and perceived costs are universal barriers. The findings highlight the need for tailored CE approaches: SMEs require resources to influence suppliers, while large firms need better methodologies for internal organizational change.

This study presents a method to design sustainable structural parts of a Rechargeable Energy Storage Systems using Design for CE and Computational Optimization. Principle solutions are developed and combined with sustainability criteria being a key factor in the concept evaluation. The battery tray, a subsystem of REESS, serves a case study in which intrusion, mass, and LCA are optimized by varying the recyclate, UD tapes and design. The resulting Pareto front reveals trade-offs between intrusion, mass and LCA results and highlights the potential to reduce environmental impact in early design.

This study synthesises interdisciplinary research on design strategies and attributes for extended product life of furniture. Through an integrative literature review, it develops the heuristic Longevity Trinity framework, comprising technical, functional, and emotional orientations. The framework consolidates dispersed design principles and highlight how the physical and psychological properties of furniture interact with the component of time, positioning product longevity as a design problem of continuity; continuity of materials, usefulness, and meaning across multiple lifecycles.

Recent advances in machine learning (ML) offer substantial potential for product development (PD), yet adoption remains limited. A crucial step is identifying suitable ML algorithms for a given PD problem, which requires translating domain-specific formulations into appropriate ML tasks. Prior work indicates that LLMs struggle with this step due to insufficient domain knowledge. Therefore, this study investigates whether a domain-specific GraphRAG approach improves model performance by enriching prompts with structured context from a PD knowledge graph.

Dominant designs establish de facto standards for all products within an industry, shaping both competition and innovation dynamics. Studying dominant designs enables firms to make informed decisions for new product development and to anticipate technological shifts. This paper presents a computer-based method that automatically extracts the spatial configuration of components from patent drawings to support the analysis of dominant designs and anomaly detection. A case study on eyeglasses validates the approach, demonstrating its potential for data-driven design innovation.

Small and medium-sized enterprises often lack the time, expertise, and tools for effective scenario management. This paper proposes a modular, AI-enabled scenario architecture integrating a guided wizard and expert environment on a shared knowledge backbone. The design aims to reduce effort and tool fragmentation while preserving human judgment, structural quality, explainability, and traceability. The proposed pattern outlines a provenance-aware foresight pipeline with human-in-the-loop capabilities that aims to transform one-off projects into reusable organizational knowledge.

Variant management faces increasing complexity that challenges traditional rule-based configuration approaches. This contribution explores how AI can support the generation of configuration rules (1) by comparing two solution concepts – a deterministic Python-based and an LLM-based approach. Following a structured early-stage AI system development methodology, the research investigates (2) how AI can be methodically integrated into variant management and (3) how implementation factors differ between both approaches. The results reveal distinct trade-offs between transparency and efficiency.

Data-driven design increasingly relies on sensor data, yet these thin measurements often lack the experiential context needed to explain why events occur or what users feel and need. This limits their value for human-centred design. Passive and active contextualisation are introduced to describe how meaning is produced through inference and user participation. A real-world case study using See.Sense cycling data from Melbourne shows how combining thin and thick data produces more situated understanding and actionable design insight.

Collaboration is crucial in design and management, fostering innovation, problem-solving, and decision-making. We explore the use of vision-language models (VLMs) for analyzing collaboration, focusing on detecting social behavior and group affect. By fusing multimodal cues, VLMs enable more context-aware reasoning beyond surface-level perception. We develop a pipeline, a structured prompt and an interactive visualization for integrating VLMs into the analysis workflow. Comparing VLM and human analysis results, we discuss how VLMs can advance collaboration analysis and the remaining challenges.

This paper introduces Dignity-Centered Design (DCD), a sociotechnical framework for AI-mediated systems. While AI ethics often focuses on concepts such as fairness and transparency, DCD evaluates how systems shape lived experience, power dynamics, and human agency. Drawing on healthcare traditions and the Dignity Index, the framework articulates three dimensions (individual, relational, and systemic) alongside five core principles. It includes a Dignity Spectrum in AI System Design to assess design choices and applies these to healthcare AI to support reflective practice.

This study investigates how large language models (LLMs) support extracting technical requirements from early product pitches. Mechanical engineering students worked under three conditions: manual, LLM-assisted, and LLM combined with a QFD interface. Both AI-assisted conditions improved requirement quality and lowered perceived difficulty. Thematic analysis showed cognitive effort shifted from generating requirements to evaluating and verifying AI outputs, while the LLM-only group reported the most positive attitudes.

Early integration of circularity into complex system architectures remains ad hoc and weakly verifiable. Model-based systems engineering (MBSE) can address this issue with precise modeling and early verification. This paper presents an artificial intelligence supported MBSE method to integrate circular strategies based on SysML v2 models, highlighting potential changes and enhancements. Generative suggestions are integrated via a regulated workflow with traceable justifications and context-enhanced queries. A use case modeling the print head of a 3D printer illustrates this approach.

This literature review paper analyses recurring challenges associated with products designed for remanufacturing and links them to areas in which Model-Based Systems Engineering (MBSE) can provide targeted support through the utilisation of SysML models. The paper proposes SysML-based strategies for enhancing requirement traceability, improving lifecycle data and simulation support, enabling compatibility assessment, and facilitating stakeholder data exchange. The conclusion highlights the implications of these insights for future research on MBSE supported Circular Economy (CE) strategies.

The paper presents a simulation framework for evaluating fast charging and battery swapping strategies in battery-electric construction machinery. Developed using discrete-event and agent-based modeling, the framework supports scenario analysis in mining and road construction contexts. Case studies demonstrate how charging strategies impact productivity, energy costs, and battery degradation. Results highlight trade-offs between operational efficiency and long-term sustainability, offering a decision-support tool for electromobility transition in construction machinery.

This study presents a simulation-based framework to analyze resource consumption and cost effects of product family design strategies. Drawing on Extended Axiomatic Design (EAD) and 53 documented design cases, we simulate empirically grounded patterns that reveal denser, more homogeneous resource use than benchmarks from cost accounting literature. The findings (1) provide a reusable dataset; (2) demonstrate the value of EAD for standardized product family design and enhanced cost transparency; and (3) support broader generalization of cost accounting insights.

This paper explored the Double Diamond design methodology through a low-reliance pacifier case study that reframes parents’ needs into a child-led weaning solution. The project integrates artistic design research with engineering design innovation methodology to create an unique aesthetic floral pacifier idea with functional forms. It was found that beyond academic theory, effective real-world design requires dedicated testing and refinement, positioning this work as a practice-led research approach that strengthens both process and outcome for a successful and modular design process.

Bridging the intention-action gap is key to driving sustainable consumer behavior. In this paper, we introduce the Change Factory, a methodology that applies behavioral science to design sustainable product experiences. We detail its four-step CODE framework - Change, Obstacles, Design, and Experimentation - and illustrate its application on fragrance refill. After identifying behavioral barriers, 33 gentle intervention ideas were generated to promote refill adoption, validating the method's efficacy in translating behavioral insights into concrete, behavior-driven design solutions.

To design effective behaviour changing interventions, behavioural designers need to generate ideas that combine both technical and behavioural aspects. However, little is still known about the creative output of ideation in behavioural design. Taking an exploratory approach, this study examines the creative characteristics of brainstormed behavioural design ideas using the Behavioural Design Space (BDS) as creative assessment framework. The findings show uneven distributions across all BDS parameters indicating fixation and lost creative opportunities.

This study examines how residents perceive and negotiate flexibility in their residential spaces across various life stages through a participatory workshop involving 80 participants. Using modular blocks, participants reconfigured layouts for scenarios such as shared living, marriage, family growth, work, and ageing. Visual and thematic analyses revealed flexibility priorities, trade-offs, and transformation patterns discussed, showing it peaks in early and mid-life and centres on bedrooms and workspaces as a temporal, user-defined phenomenon of adaptable housing.

Engineering education faces growing pressure from rapid technological change, which reshapes professional competencies. Effective knowledge transfer between academia and industry is therefore essential for innovation, yet remains insufficiently integrated. The Digital Lifecycle Lab (DLL) at TU Graz addresses this gap through an industry-aligned, lifecycle-oriented environment for systems engineering education. By integrating academic learning with industrial practice, the DLL functions as both an educational concept and transfer platform preparing the future engineering workforce.

Do short design exercises boost confidence equally for males and females? In a 7-semester study (n=426; 201F, 225M), females reported lower initial confidence (p<.001, g=0.31) but matched males in final confidence and gains after four applied exercises (yield, creep, impact, fatigue). Regression showed no sex differences in final or Δ confidence (p=.42, g=0.03). Active design integration in engineering science courses may reduce sex-based disparities in engineering confidence and self-efficacy. Sex balance in the cohort may suppress typical gaps, highlighting context as key to equity.

This paper investigates the relationship between Big5 scores (individual and team level) and team design project performance in SUTD's “Introduction to Design” course. Study reveals that team's final project grades have statistically significant positive correlation with higher team’s mean “Conscientiousness”, but negative correlation with within-team variety of “Neuroticism” and "Conscientiousness". At individual level, “Conscientiousness” shown positive correlation with the students’ grades. However, further qualitative study is needed to understand the reasoning behind the correlation.

Barriers limit laboratory participation for students with study-impairing conditions. A mixed-methods study via survey (n=43) and interviews (n=9) identified two student clusters with distinct accessibility needs: Cluster 1 requiring flexible scheduling and modular structure for chronic illness and caregiving; Cluster 2 requiring explicit guidance and multi-modal content for visual impairment and neurodivergence. Both required institutional support infrastructure. Results establish evidence-based requirements for inclusive remote laboratory development applicable beyond target populations.

Life cycle assessments identify environmental hotspots, yet translating these insights into design actions remains slow and expert-dependent. Existing AI tools lack a dynamic link to current research. Here, we present an LLM-driven pipeline that interprets LCA hotspots, mines recent literature, and extracts feasible, research-backed design alternatives. In a case study on a headlamp control unit, the method produced relevant and applicable improvements, indicating its value for accelerating sustainable product design.

This study examines how AI can support the development of Leading Sustainability Criteria in sustainable product development, comparing AI-generated outputs with human-facilitated workshop results from four Swedish companies. Results highlight AI’s ability to accelerate and broaden sustainability framing, but emphasize that contextual relevance and legitimacy depend on participatory inputs. The findings suggest that AI is most effective when integrated into hybrid workflows that preserve human insight and stakeholder engagement—offering practical guidance for future implementation.

Barriers such as limited repair literacy and design-for-disposability continue to reinforce replacement cultures. This paper introduces AIFixer, an AI-powered interactive tool that guides consumers through electronic repair, promoting sustainable product lifecycles. Using a mixed-methods, user-centred approach, the study evaluates AIFixer’s usability and behavioural impact across real-world repair tasks. Findings show that conversational AI lowers barriers, builds confidence, and generates data for circular design, highlighting opportunities for multimodal and community-integrated development.

In a circular economy, repurposing extends product lifecycles and reduces resource use. However, identifying feasible repurposing opportunities remains challenging. This study therefore evaluates the capability of large language models to identify such repurposing scenarios and their relevant properties, using documented repurposing cases from peer-reviewed literature. Three models were tested, revealing potential in identifying repurposing scenarios, but also highlighting the need for structured methods and further research due to systematic limitations in property identification.

Current approaches for the generative design of sheet metal parts only take singular optimization goals into account. This paper presents a concept for a deep reinforcement learning approach to train an agent to generate sheet metal parts by combining segments from a predefined library. Through a weighted reward function, agents can be trained for different or combined optimization goals, such as weight, cost, or sustainability. The resulting agents enable the creation of a pareto front of optimal solutions, supporting efficient exploration of the design space for diverse design objectives.

We present FORGE (Framework for Optimization and Reinforcement-driven Generative Engineering), a probabilistic programming framework for generative design that unifies declarative, symbolic modeling and reinforcement learning (RL). FORGE can learn and refine a design generator through RL based on simulator-derived rewards. We demonstrate FORGE across several vehicle domains. FORGE creates an extensible, interpretable foundation for generative engineering. It can act as both a data generator for machine learning and a design optimizer, offering a practical alternative to purely neural methods.

This work explores Reinforcement Learning (RL) for the circular design of planar truss linkages using available bars and pins. A bipartite graph representation and elementary action formulation enable agents to assemble mechanisms in a physics-based environment. Results for a force-inverter design problem show 98.5% success for fixed-stock training and 66.0% for shuffled stocks. The method demonstrates RL’s potential for inventory-constrained mechanism synthesis, with future work targeting scalable, indexing-invariant architectures and more flexible connection actions.

Trade-off studies often use the design of experiments approach, while simulation models enable data-based product optimization by AI. This paper presents a comparison of evolutionary algorithms, reinforcement learning as well as active learning for design space exploration. Based on a real-world case study and hypervolume analysis, the performance of selected algorithms is assessed. The results highlight their ability to identify pareto fronts and provide insights to deepen the understanding of AI-driven design space exploration.

This study systematically characterizes product development processes (PDPs) in hardware-oriented SMEs. A PRISMA-based literature review identifies twelve empirical case studies and eight development frameworks. The analysis derives key PDP influence factors, synthesizes stage-wise SME PDP activities, and consolidates characteristic challenges. A gap analysis reveals the absence of SME-specific PDP development frameworks and missing integration with technology management. The results establish a structured empirical basis for future research.

This paper presents the development and validation of a new information structure for design methods for an enhanced repository of design methods to support design practice and pedagogy. The structure is based on features derived from challenges faced by design students, practitioners, and educators in understanding, using, and teaching design methods, and it is experimentally validated. Results show 81.2% of participants had no difficulty in understanding and using design methods based on the proposed information structure. Participants reported 8 improvements in the structure.

This paper proposes a conceptual framework for integrating design methods into Human–AI Co-Creation, redefining methods as mediating structures between task context, procedural logic and results. Two examples illustrate how AI supports both method execution and method selection under varying autonomy levels. The framework highlights implications for transparency, traceability, responsibility and method education, and offers a structured basis for analysing, teaching and developing method-based design practice in AI-rich environments.

To enhance multifaceted and critical reflection, we developed the Dual-Advocate Reflection Cards tool and carried out an empirical study. The eighteen student participants worked in pairs to reflect on their experiences. Transcripts of their discussions were analyzed, and we counted the frequency with which each card and each level of reflection intensity were discussed. The results indicated a strong link between card usage and utterances of multifaceted and critical reflection, as well as the effect of the cards on the reframing of the evaluation or understanding of the design process.

Current flying-car designs lack scalability for diverse missions. This paper presents a modular design platform for developing reconfigurable flying-cars, embedding modularity across structural, electrical, and flight control domains. A full-scale sightseeing prototype demonstrates the platform’s feasibility and flexibility. The work contributes to design methodology by illustrating how modular architectures improve cross-mission adaptability, scalability, and lifecycle efficiency in complex mechatronic systems. (project introduction video available at https://www.aidilab.ai/flying-car)

Autonomous driving is a promising technology for public transportation to solve two main challenges: The driver shortage and the reduction of environmental impact. This contribution investigates if already existing requirements in standards, laws, regulations and guidelines for accessibility, safety, security and bus drivers’ tasks of transit buses in Europe and Germany can also be complied to with an autonomous transit bus or if the requirements need an adaption. 54 impactful requirements on autonomous transit buses have been found and their impact and opening design space will be discussed.

The pilot project initiatives using Knowledge-Based Engineering (KBE), Design Automation (DA), and visual modelling techniques, at Saab Aeronautics, are presented in this work. The aim is to evaluate their practical applicability and outline how organisations can implement accelerated product development methodologies. By integrating organisational knowledge, parametric models, standardized workflows and automation tools, design lead times are significantly reduced, allowing design expertise to focus on innovation, quality, and strategic problem-solving.

This paper addresses the lack of empirical knowledge on which demographic groups are most likely to use autonomous buses in the Munich Metropolitan Region. We analyze this question through a large-scale online survey capturing demographics, mobility behavior, and accessibility needs. Results show that younger, multimodal, and well-educated individuals form the core of potential users, while older and car-dependent groups remain hesitant. The findings highlight that successful deployment requires inclusive design, improved accessibility, and targeted communication strategies.

This paper addresses the increasing complexity of interfaces in multi-brand product development processes (PDPs). By conducting a systematic literature review and validating findings through industry expert workshops, we assess and compare existing modeling approaches. Seven criteria for effective interface management are derived and applied to model clusters. The results reveal that only integrated, multi-level modeling strategies can adequately capture technical, organizational, and governance challenges in multi-brand PDPs, highlighting key gaps and future research needs.

This study investigates the challenges of dynamic value integration in complex innovation projects. Difficulties stem not from a lack of willingness, but from a persistent lack of re-openability in design governance systems. This study identifies a triple closure mechanism that filters out evolving stakeholder values. The paper reframes value integration from a technical problem to a political-institutional process, arguing for an Adaptive Governance Infrastructure designed to manage the necessary tension between static and re-opening.

Social robots increasingly interact with humans in diverse contexts. In this study, a systematic framework is proposed for selecting stakeholders based on the user requirements in participatory design of social robots. Matching social robot design dimensions with stakeholder fields in the framework, is achieved using Quality Function Deployment (QFD) and Design Structure Matrix (DSM) methodologies. A case study is presented to demonstrate utilization of the framework. The contribution of this paper is to develop an infrastructure towards formalization of participatory design of social robots.

This paper conceptualizes data-driven product management (DDPM) as an organisational capability requiring clearly defined roles for effective coordination across technical, analytical, and managerial domains. Based on a systematic literature review and industry workshops, twelve interdependent roles were identified and specified through standardized role profiles. The resulting role architecture provides a structured foundation for organisational design, enabling companies to assign responsibilities, align competences, and operationalise DDPM in practice.

Daily stand-ups often deviate from their intended efficiency. This study shows that challenges like unequal participation, recurring blockers, and lack of goal orientation can be made visible through an AI-based analysis method. Validated in several iterations with real company data, the method provides transparent and data-protection-compliant results. By identifying specific improvement potentials, the approach creates a data-driven foundation for teams to optimize their meetings and their collaboration.

This study explores the alignment of production system development processes (PSDP) with agile principles in the automotive industry. A multiple case study of eight companies reveals low overall alignment, with OEMs and subcontractors constrained by sequential, stage-gate structures, while engineering consultants show higher agility in early phases. Findings suggest gradual adoption of hybrid models, culural changes and iterative planning to enhance flexibility and responsiveness.

This paper presents a controlled laboratory study investigating how environmental factors influence team resilience and teamflow in product development contexts. It examines which factors shape teams’ adaptive responses and collective engagement. In the study, one factor per People–Organization–Technology dimension was systematically manipulated: member unavailability, time autonomy, and material quality. Results reveal distinct effects on performance and collaborative dynamics, providing empirical foundations for designing resilient team environments in engineering work.

This paper examines how spatial arrangements affect digitally supported individual and group decision-making. In a controlled within-subjects study, 24 participants completed the NASA Moon Survival Task across three spatial conditions: standing at an interactive table, sitting at the same table with personal zones, and using laptops. By analysing decision quality, speed, and perceived collaboration, the study shows that spatial design meaningfully shapes decision performance, interaction dynamics, and user experience.

To design for sustainability requires systemic change which cannot be carried by design teams alone. Using a case study approach in aerospace, this study investigates stakeholder influence in sustainable product development. It identifies main internal and external stakeholders and discusses how design teams should engage with them. Findings support practitioners to navigate structural barriers and plan design interventions, and highlight the research need to consider organizational structures, decision-making processes, and cross-functional collaboration for sustainable product development.

This review synthesizes the current state-of-the-art knowledge on pure mycelium materials (PMMs) as sustainable design solutions, mapping their essential structural, chemical, and mechanical characteristics, and the factors that drive or hinder their performance in design contexts, while also identifying application fields. Finally, this paper points to gaps in taxonomy and standardized characterization, resulting in a duality between scientists and designers and industry. Therefor, future research is derived to reinforce the synergy between design and material science for PMM adoption.

This study proposes a territorial-scale model to estimate flows of reusable building components by sequentially evaluating technical, logistical, and economic feasibility. It translates reuse barriers—such as disassembly potential, residual performance assessment, transportability, storage conditions, and costs—into measurable indicators. By aggregating component-level data into territorial indicators, the model links component-scale characteristics to overall territorial material flows, providing a framework to assess and compare reuse potential across territories.

This study examines how product development activities influence the environmental sustainability of complex mechatronic systems using a 2D-flatbed laser cutting system as a case study. Three levels are identified, the machine, operation and part level, at which design changes can affect environmental sustainability during machine operation. Utilizing operational machine data, nine design changes are derived indicating that ~36% of the environmental impact in the use phase can be reduced through technical design solutions, enabling EcoDesign principles supported by data-driven approaches.

This work introduces a graph-based CAD assistant that predicts the next modelling operation in parametric design sequences. Real CATIA V5 models from the automotive domain are converted into directed acyclic graphs capturing feature dependencies, enabling learning directly from structural design data. A four-layer Graph Attention Network achieved a top-5 prediction accuracy of 94%, outperforming a frequency-based non-parametric baseline. The results show that graph representations and attention-based message passing provide a strong foundation for context-aware modelling assistance.

This paper presents a characterization approach for analysing geometric variability in industrial 3D model datasets to support the preparation of synthetic datasets for machine-learning applications. By implementing pairwise Hausdorff distances and manifold-based embedding techniques, the study identifies variability ranges required for generating representative synthetic data and demonstrates how targeted augmentation can effectively reproduce real data's variability, ultimately leading to more reliable and robust NN model performance.

Imperfect CAD models with non-smooth features are common outputs of the latest digital tools. These are unsuitable for the feature recognition needed for end applications like computer-aided manufacturing. This paper proposes to recognise features from imperfect models by contributing a comprehensive dataset, a novel data surrogation method, and ML-based automated feature recognition model. Results show that the data surrogation method accurately replicates manual imperfections with voxel accuracy >0.9 and a Dice coefficient >0.6. Ultimately, feature recognition achieves 92.8% test accuracy.

Engineering organisations increasingly aim to reuse historical BOM, CAD, and requirements data to identify recurring components. A key prerequisite is Entity Matching (EM), whose performance on heterogeneous engineering data is unclear. This paper evaluates classical models, zero-shot LLMs, and hybrid EM on Amazon–Google and a multimodal engineering dataset. Random Forest and XGBoost achieve near–state-of-the-art results; LLMs perform well but are costly, hybrids add little. EM transfers under controlled conditions and forms a foundation for reference architecture reconstruction.

Manufacturing companies engineering Cyber-Physical Systems of Systems face growing challenges in maintaining consistent product-related data and processes. This paper proposes a PLM Architecture Framework that integrates concepts from Product Lifecycle Management (PLM) and Enterprise Architecture (EA) along an adapted DevOps lifecycle. The framework enables consistency and transparency across engineering and business domains. A case study in machine tool engineering illustrates its potential to enhance data traceability, stakeholder alignment, and cross-generational digital continuity.

In this work, we propose an extension of classical form-finding that incorporates non-design space requirements directly into the process. This enables numerical weight optimization of thin-walled structural components. We present a concrete implementation which relies exclusively on standard structural finite element analysis, promoting integration into existing workflows. The method is validated on benchmark problems with known optimal solutions. Finally, its practical benefits are demonstrated through a more realistic engineering case study.

Battery packs in BEVs are multidisciplinary design challenges balancing cost, weight, volume, range, and charging time. As the heaviest and most expensive component, their reliability and safety under vibration and shock is critical. This contribution presents an indicator to cluster measured vibration data of BEV battery packs based on coupling with the car body, enabling identification of representative designs and generalization of vibration behavior for future, more integrated architectures.

Generative Design (GD) offers lightweight, manufacturable solutions, but manufacturing constraints often require indirect handling. Using a deflection lever for Additive Casting, we compare PTC Creo GTO and Altair Inspire using identical load cases and mass-minimisation. Both tools achieved −26% mass and −46% inertia while meeting displacement limits. Wall-thickness control and channel integration show clear trade-offs, yet both tools provide viable designs for early development.

The applicability and scalability of design adaptations utilizing reinforcement learning can be broadened by using graph-based approaches instead of rigid vector- or grid-based ones. However, graph-based approaches often require a high number of simulations to converge. To reduce the simulation effort in the mechanical optimisation, the reinforcement learning setup is enriched with task-specific causal and physically based information. This work systematically investigates the influence of this additional information on the efficiency of design adaptations using a factorial test design.

Implementing novel interaction modalities to CAD systems is raising questions about suitability of types of thinking employed and appropriateness of existing workflows used in CAD in this new context. The study reported in this paper explores the potential for introduction of alternative activities into existing workflows, proposed by designers interacting with 3D shapes using gestural interaction. Findings propose introduction of sculpting and forming paradigms that may reduce the amount of work required to create more complex forms.

Conceptual design methods rarely optimize both requirement fit and cross-principle compatibility, leaving a gap in generating coherent early-stage solutions. Here, we introduce a mixed-integer linear programming formulation that selects one solution principle per function while jointly minimizing local requirement mismatch and system-level incompatibility. Using a small case study, we show how a trade-off parameter controls the balance between functional quality and integration robustness. The results demonstrate that the approach enables transparent, compatibility-aware conceptual synthesis.

Functional decomposition shapes early design decisions but is largely qualitative, leaving units and measures implicit. This work introduces the Quantitative Functional Decomposition Problem, which formalizes functions and interfaces with measurable quantities, making decomposition solvable as a quantified planning problem. Two case studies show that the approach gives immediate feedback on the admissibility of functions and their connections. Design engineers get consistent quantified structures, which speed up iteration, reduce work and set targets for subsequent steps in the design process.

Collaborative design futuring has gained attention as an inclusive approach for envisioning future societies where external stimuli play a crucial role in stimulating imagination. Building on literature on design stimuli, psychological distance, and co-design, we propose and evaluate a five-layer framework through a multiple-case analysis of fifteen workshops and exhibitions. Through comparative analysis of different workshops, we explored how stimulus characteristics, such as modality, richness, and scenario, influence participant engagement and perceived psychological distance.

The search for effective strategies to support mental well-being has become increasingly pressing in contemporary societies, where stress, anxiety, and cognitive overload are widespread. In this paper, we present a wearable-supported VR system designed to enhance mindfulness through the integration of visual, auditory, and respiratory cues. Drawing on evidence from color therapy, binaural beats, and biofeedback, the system delivers a multisensory environment that supports emotional regulation. We describe the system’s design and discuss its potential to improve technology-mediated well-being.

This paper presents ADT, a digital card-based toolkit designed to integrate AI into the Design Thinking process. A survey of 204 designers examined AI literacy, usage patterns, and adoption barriers. Results indicated uneven familiarity, with higher use in Prototyping and Testing stages. Key challenges included prompting, trust, ethics, and training gaps. ADT, thus, structures four professional roles across five design-thinking stages, providing reusable prompts, recommended AI tools, exemplar outputs, and ethical reminders to promote informed and responsible human–AI collaboration.

This paper argues that disagreements about what counts as an ideal prosthesis arise from tacit framings of disability. Drawing on Wittgenstein’s language games within an abductive–deductive–inductive approach, we identify four prosthetic language games: medical, social, relational, and critical. By rendering their distinct grammars explicit, the framework reframes interdisciplinary disagreement as epistemic plurality and supports boundary work, translation, and epistemic fluency, enabling more reflective and dialogical design research without collapsing plural standards of prosthetic success.

Digital user tests utilizing musculoskeletal human models facilitate ergonomic assessments in the early phases of the product development process. In the underlying posture prediction models, the various movement strategies of the users need to be represented. Behavior cards are an evaluated tool for the representation of such movement strategies; however, a standardized determination of behavior cards is lacking so far. This study explores a cluster-based and a regression-based method for standardized behavior card determination, demonstrating the applicability of both methods.

This paper explores how design fiction and generative AI (GAI) were used in a master’s course addressing food consumption issues. Data from two course iterations include student outputs, reflections, and prompt records. GAI expanded speculative exploration, supporting rich, detailed futures. Two approaches emerged, exploratory and goal-oriented, highlighting the value of prompt literacy, iteration, and critical curation to sustain creative control and adaptability.

Divergence and convergence are central to design processes, but can these patterns be made visible, and what might they reveal? This paper examines design team dynamics through NLP by tracing cognitive trajectories in weekly reflections. Textual data are transformed into numerical values and plotted in a Synergy Diagram, enabling instructors to monitor team bands. This paper consists of a four year study covers 48 project teams and 250 participants, comparing team bands across multiple bandwidth measures and showing how they vary against a design outcome, such as total grades (TG).