Capturing design rationale in High Value Low Volume (HVLV) capital goods is challenging due to tacit knowledge and the lack of structured modelling support. This study addresses this gap by developing an ontology for Encapsulation Architecture design rationale. The Design Research Methodology is followed, comprising a Research Clarification phase and a Descriptive Study 1 phase based on a single industry case study involving practitioner reviewed requirements related to Encapsulation Architecture design rationale modelling. The resulting ontology identifies 10 key concepts — including Variability Needs, Variability Need Possible Choices, Product Structuring Principles, and Design and Delivery Flows — and provides a basis for future information model and tool development in the HVLV capital goods context.

Many visualization systems have been developed to present kinematic data in upper-limb rehabilitation, but few are structured around clinical interpretive workflows. This study introduces a therapist-focused visualization design intended to align upper-limb kinematic data with clinical reasoning processes. The design was developed through a focused mini-review of existing systems and demonstrated using a prototype dashboard. It integrates spatial, temporal, and quantitative information into a single display. By structuring commonly used kinematic metrics and qualitative movement views into a clear format, the proposed design provides a conceptual foundation for developing more clinically informed visualization systems in rehabilitation.

Water distribution networks experience substantial losses due to leakage, while systematic inspection of small-diameter pipes remains economically unfeasible with current technologies. This paper proposes Design by Analogy (DbA) as a structured approach to transfer mature inspection principles from oil and gas pipelines to water infrastructure. Using Gentner’s structure-mapping framework, functional relationships in oil and gas in-line inspection (ILI) are analysed and mapped to the constraints of water distribution systems. A case study compares permanent magnet landmarks used in oil and gas pipelines with RFID-based landmarks adapted for water pipes. The structural similarity between the two systems demonstrates how relational transfer, rather than direct technological replication, can yield solutions better suited to branching, small-diameter networks. The study argues that DbA can accelerate development and reduce exploration costs by leapfrogging the early exploration phase, while highlighting the risks of design fixation and overengineering.

This study explores how Pattern Language (PL) can function as a design card–based support tool for non-designers engaged in addressing social issues. In contemporary society, where “wicked problems” require broad participation beyond professional designers, many non-designers operate without access to expert facilitation. To support these non-designers, we developed an original PL for Social Service Design. The pattern set was evaluated in the context of elementary and secondary education, with a focus on designing inquiry-based learning programs in Japan. The results showed that the patterns enabled participants to verbalize and structure their implicit practices, and the patterns related to “Research” and “Visioning” were prioritized. These findings highlight the context-dependence of design patterns and provide practical guidance for utilising design cards for non-designers’ environments.

Selecting suitable concepts in early product development is particularly challenging for complex product-service systems with significant lifecycle cost impact. This paper presents a technical-economic evaluation methodology based on VDI 2225, combining qualitative technical value assessment with analogy-based CAPEX and OPEX estimation. The approach is demonstrated in a case study on the concept development of a steering unit for deep drilling applications. By integrating technical performance and lifecycle costs, the method enables transparent, robust, and economically informed concept selection in early development stages.

Many organizations operate production architectures designed long ago, with the goal of optimizing performance against yesterday's goals. Over time, these become increasingly misaligned with evolved demands for volume and complexity. This paper presents insights into recognition of when such architectures are outgrown, using a theory-building single-case study of an internal asset provider in discrete manufacturing, we analyse historical and present data, we identify four observable symptoms that co-evolve before significant performance deterioration. These are: Rising orders outside established architectural boundaries, complexity overflow inside the architecture, market technical misalignment, and strategic misalignment. We propose pattern-based interpretation, emphasising broad co-movements over time. While bounded to large, data-rich, vertically integrated companies, the indicators are operationalized from available data, and can support earlier, evidence-based action.

During product development, cognitive biases in the application of Reference Systems often cause costly rework. Analyzing seven critical incidents from a mechanical design project, this study identifies specific cognitive challenges. To counteract these, a Reflective Question Set (RQS) is derived. Acting as a heuristic "mental anchor" during complex tasks, the RQS triggers proactive validation to minimize late-stage engineering errors. An initial evaluation confirms its theoretical value and outlines the need for situational integration into the workflow.

Defense companies are increasingly shifting from an Engineer-to-Order (ETO) to Make-to-Order (MTO) order fulfillment strategy to meet high demand by improving scalability and reducing lead times. Simultaneously, companies developing complex physical products are adopting agile methodologies to respond to dynamic market conditions. However, the interaction between order fulfillment strategies and agility remains poorly understood, creating uncertainty for organizations navigating this dual transformation. This study explores synergies and frictions between agile principles and the ETO-to-MTO transition by identifying and evaluating design-specific changes in the development of physical products. Through a narrative literature review, five changes were identified and their implications were subsequently evaluated using a qualitative assessment framework. The assessment reveals that the transition creates both structural enablers and barriers for agile principles. Our findings support organizations in strategically leveraging agile principles exhibiting structural synergies while managing those exhibiting frictions. This enables more effective integration of agile practices in MTO environments.

The proliferation of Text-to-Image Generative AI (GenAI) shifts design from manual representation to linguistic specification. However, mainstream models trained on low-context Western data conflict with high-context Eastern aesthetics, causing "Aesthetic Aphasia." This study proposes the "Double Semantic Gap" model to analyze this cultural friction. Integrating Hall’s cultural context and Kalyuga’s expertise reversal theories, we investigate cognitive costs in AI collaboration. A pilot study on mood board creation reveals an expertise reversal effect: experts suffer higher cognitive load due to schema conflicts, while novices benefit from AI as semantic scaffolding. This research identifies mechanisms of cultural friction and advocates for localized prompting guidelines to preserve cultural diversity.

Artificial intelligence is increasingly discussed as a transformative technology in product development, yet its industrial adoption remains uneven. This study examines how academic and industrial experts perceive its applications, limitations and needs for action. Based on a qualitative survey of 26 participants, responses were systematically coded and clustered. The results reveal a partial divergence in perceived application areas. While both groups highlight data analysis, decision support and design automation, academia places stronger emphasis on simulation and modelling, whereas industry prioritizes knowledge management, routine automation and quality assurance. Despite these differences, both groups converge on key barriers, particularly data availability and quality, user trust, hallucinations and integration into existing toolchains. Several industrial participants report no active usage of artificial intelligence, indicating an implementation gap between research and practice. Overall, artificial intelligence in product development is currently positioned primarily as an efficiency-enhancing support tool rather than an autonomous design agent.

The influence of ChatGPT support on sequential reasoning in synchronous collaborative CAD activity was examined. Twenty-two student pairs redesigned a crankshaft for forging with or without ChatGPT support. Verbal protocols were coded and analysed using first-order transition probabilities between the Problem and Solution space and Analysis, Synthesis and Evaluation (ASE) design operations. With ChatGPT support, transitions from Analysis to Synthesis and from Evaluation to Synthesis were increased, within Analysis and returns to Analysis were reduced, fewer returns to Problem were observed, and coordination around the ChatGPT was indicated.

Parametric CAD models encode engineering design knowledge through feature-based representations, yet generative AI approaches to design automation treat these models as geometric shapes, discarding functional semantics and manufacturing constraints. Text-to-CAD methods rely on vision-language models to generate descriptions from renders, producing annotations that capture visual appearance but lack engineering content, limiting their ability to generate functionally valid outputs. We present a methodology for capturing machining feature knowledge in parametric CAD representations. Our approach enriches models with structured metadata linking geometric features to functional purposes and manufacturing requirements while maintaining traceability to design operations. We validate this on 1,770 shaft components with dual annotations—VLM-only baseline versus feature-augmented. Training generative models on feature-augmented data yields substantial improvements in geometric accuracy and functional correctness, demonstrating that structured engineering metadata enables better capture of design intent. We release our code for reproducibility.

Technology-driven companies often express a strategic ambition toward customer-centricity, but struggle to translate it into operational practices such as solution discovery. In business-to-business-to-consumer (B2B2C) ecosystems, where organizations reach end-customers indirectly through business partners, commonly used design tools such as the Strategyzer Value Proposition Canvas offer limited guidance. While solutions in these contexts should be shaped by customer insights, internal limitations and partner-imposed pressures often override them. This paper presents a practice-based case study of how the Value Proposition Canvas was extended within a cybersecurity organization undergoing a customer-centric transformation. Following a research-through-design approach, the canvas was iteratively refined through co-creation workshops and reflection. The final version incorporates internal and partner pressures, desired customer behaviours, explicit opportunity framing, a user story element, and early acceptance criteria. The findings show how the extended canvas helps teams move from strategic intent to concrete, customer-informed solution definition in complex B2B2C environments.

In Requirements Engineering, information is often captured in unstructured natural language, limiting its systematic use across product development phases. Ontologies offer a machine-interpretable framework that integrates domain-specific knowledge. This study presents a text-to-ontology pipeline combining a rule-based NLP approach using BERT and a generative LLM (GPT), demonstrated on an electrified micro-mobility solution. Both enable semi-automated semantic integration, with GPT offering stronger contextual abstraction and BERT providing stable results for syntactically simple input.

3D Computer-Aided Design (CAD) reverse engineering aims to recover the parametric command sequences required to reconstruct 3D geometries from point clouds, exchange formats like STEP, or triangular meshes. Current Large Language Model (LLM)-based methods that generate CAD code rely on single-shot test-time sampling, often failing on complex topologies. This paper proposes Tree-of-CAD Models (ToCAD) to empower reverse engineering LLMs with frontier multimodal LLMs for iterative visual refinement in a search-based framework. By intelligently guiding test-time compute, ToCAD significantly improves the topology of reconstructed 3D models, improves code structure, and enables the utilization of complex CAD features beyond sketch and extrude operations.

The economic evaluation of circular business models in the early development phase is complicated due to novel business logic, high uncertainty, and incomplete data. Existing approaches are either overly simplified or require significant modelling effort. Based on the design science research methodology, we developed an AI-supported software tool that systematically converts business model information into a probabilistic simulation model. This approach combines a circular business model canvas with a dynamic Monte Carlo simulation to explicitly model uncertainties and present their effects on key financial indicators transparently. An evaluation with industry representatives revealed the approach's high perceived comprehensibility, applicability, and usefulness, underscoring its suitability for early decision-making phases in the circular economy.

Growing sustainability challenges require a shift from ex-post impact mitigation to sustainability by design, embedding environmental, economic, social, and operational considerations directly into early-stage product development. Decisions taken under uncertainty in early design phases largely determine long-term system performance, highlighting the need for structured decision support. Remanufacturing has gained attention as a promising circular economy strategy; however, its sustainability cannot be assumed a priori and depends on contextual and design-related factors. This paper presents the conceptual development of a sustainability screening framework to support early-stage remanufacturing decisions. Grounded in the Design Research Methodology and informed by a Systematic Literature Review, the study structures a three-stage screening logic that enables the systematic comparison of alternative remanufacturing strategies across four sustainability dimensions, benchmarked against conventional new manufacturing. A spreadsheet-based mock-up is provided to illustrate the operationalisation of the framework.

This study examines how asymmetric avatar roles influence player experience in a cooperative virtual reality puzzle game. Two players were required to collaborate to escape a virtual house, with one controlling a large, strong avatar and the other a small, agile one. Through timed gameplay, surveys, observations, and interviews, we investigated how differences in avatar size and strength shaped immersion, agency, communication, and engagement. The qualitative data showed that the two roles produced notably different experiences. The small avatar was consistently perceived as more engaging due to its mobility, exploratory possibilities, and unusual perspective, while the large avatar felt more restricted and less capable than expected. Role switching intensified players’ focus on comparing perspectives rather than attending to their partner, which reduced opportunities for empathy and mutual awareness. These findings highlight the importance of meaningful affordances, clear avatar visibility, and balanced role design when creating asymmetric collaborative VR experiences.

Calcium phosphate cement (CPC) is a resorbable, biocompatible implant material used to treat cranial defects. However, challenges in additive manufacturing limit the production of large, patient-specific, single-piece CPC lattice structures. This paper introduces a novel, reproducible, parametric process for designing modular neurocranial implants for patients aged three years and older. The process employs scalable Voronoi diagrams to divide the implants into central segments and edge segments while incorporating z-gap profiles and pore distributions to promote bone growth. Starting from an implant base body in nTop, a design process generates individual segments according to specified design requirements. A preprocessing workflow in the internally developed software SliceIt! prepares the modular implant for additive 3D printing. The approach was evaluated through twelve case studies and two prototypes, one for handling validation and one manufactured from CPC.

Product variants are designed to be configured under technical constraints, yet many configurators provide limited recommendations. This paper investigates support for product configuration by analyzing Recommender Systems and Large Language Models and their complementary use within the configuration process. Based on a literature review, a functional allocation along the configuration pipeline is derived to support hybrid recommendations based on product knowledge, historical configuration data and to enable natural-language interaction. These components are complemented into a conceptual framework that uses a formal constraint solver to consolidate technical feasibility.

Successful utilization of AI to support product development processes often depends on the early articulation of domain-specific AI use cases by domain experts, yet empirical insights into this procedure remain limited. This contribution presents an artifact-based analysis of 33 structured AI Idea Sheets applied by domain experts across different temporal and collaborative settings. The findings reveal strong problem and data specificity but systematic under-specification of AI framing and evaluation, indicating where interdisciplinary scaffolding may be required in early-stage AI system conception.

Design for X (DfX) methods are widely used to integrate manufacturability, performance, cost, and sustainability considerations into early design phases. However, in highly complex engineering systems, these requirements frequently contradict each other and are commonly evaluated in isolation using static decision schemes. This paper addresses this limitation by proposing a conceptual framework for resolving conflicting DfX requirements through dynamic, agent-based negotiation. A five-layer multi-agent system architecture is introduced, combining contextual requirement definition, graph-based design representation, domain-specific DfX agents, structured negotiation mechanisms, and knowledge integration with learning capabilities. Instead of statically scoring design alternatives, conflicting requirements are treated as negotiable entities that are iteratively reconciled by interacting agents. The approach is illustrated using the example of an additively manufactured high-pressure mini-channel heat exchanger, which exhibits tightly coupled thermomechanical and manufacturing constraints. While additive manufacturing serves as an exemplary application domain, the proposed concept is designed to be generalizable to other engineering fields characterized by high conflict density. The contribution establishes a foundation for adaptive, transparent, and learning-capable DfX decision support systems.

Integrating LLM-based agents into Model-Based Systems Engineering (MBSE) promises to reduce coordination overhead across heterogeneous toolchains and data silos, but unsystematic integration can result in agent sprawl, loss of human control, and opaque decision processes. This paper presents an event-driven reference architecture for Human–AI Collaboration in MBSE comprising three pillars: an Event-Driven Information Backbone (EIB) that combines pub/sub coordination with an immutable event log, Domain-Specific Agents (DSAs) that act as intelligent intermediaries between engineering tools and the backbone, and Personalized Interaction Agents (PIAs) that filter event streams into role-relevant decision packages and operationalize human-in-the-loop governance through a Decision Inbox pattern. All participants interact exclusively through explicit event contracts, enabling modular integration without bilateral coupling. Correlation identifiers link causally related events across tools and actors, enabling end-to-end reconstruction of decision provenance, including rejected alternatives and their rationale. A proof-of-concept prototype provides initial evidence of technical feasibility.

Generative AI (GenAI) is rapidly entering design practice still its concrete impact on human ideation (for example using it as inspirational stimuli and design fixation) remains insufficiently understood. This research investigates how GenAI influences creative performance and experience in early-stage design and how it can be integrated with traditional ideation techniques rather than using it as a stand-alone idea generator. The study employs both quantitative and Qualitative methods to collect data from a structured design exercise conducted with 14 students from a course on the use of AI in design creativity in university settings. Techniques such as Brainstorming, Brainwriting, Negative Ideation, and Prompt Engineering were employed in the design process to investigate how AI tools function as team members during collaborative design ideation. Creativity outcomes were evaluated on fluency, novelty, feasibility, surprise and then it was complemented by fixation scores. The results show that GenAI consistently increased perceived inspiration and helped participants reframe problems by providing new and fresh perspectives. The GenAI was widely experienced as an active “team-mate” rather than a simple tool. However, human-only conditions often produced comparable or higher idea fluency and AI-generated examples sometimes introduced new anchors that shifted the focus of design fixation rather than removing it. Negative Ideation coupled with decorated prompts, helped participants transform AI suggestions instead of copying them directly. The insights offer strategies for integrating GenAI into professional work and design education so that it grows without restricting the range of potential solutions.

This paper presents an empirical case study on a pedagogical and co-creative paradigm shift designed to revitalize indigenous craft traditions within a university design program. The project addresses the challenge of transmitting tacit cultural knowledge in a formal educational setting, where conventional design pedagogies often fail to engage indigenous students. The study documents a collaboration between indigenous design students from National United University and the Seediq community in Taiwan. The intervention centered on the co-creation of a “cross-woven” bamboo chair, a novel artifact that integrates the students’ bamboo-weaving skills with the community’s traditional textile weaving techniques. This process inverted the typical design thinking model by prioritizing hands-on practice (“labor before ideation”), fostering a reciprocal learning environment where students and community artisans acted as both teachers and learners. The findings demonstrate that this co-creative model not only facilitates the revival of endangered craft skills but also empowers indigenous youth to act as cultural innovators, re-contextualizing traditional materials and techniques to create artifacts with contemporary relevance and cultural value. The project serves as a replicable framework for value-based design education that aligns technological innovation with cultural heritage and community empowerment.

This paper introduces a design-oriented method for building a project “Sustainability Profile” that makes a product’s contribution to sustainable development visible and discussable through a UN SDG–inspired 5P lens: People, Planet, Prosperity, Peace, and Partnership. Using a grounded, bottom-up approach, we coded 30 Master of Design project abstracts to extract design concerns, motivations, values, and intended impacts, then mapped these codes to the 5Ps.

Cross-project consolidation and clustering yielded a reusable set of 5P indicators assembled into a sustainability rating matrix. Projects are assessed with a binary rule (indicator present/absent) and normalised within each pillar to account for differing indicator counts, then visualised as radar charts. A pilot on five projects shows how profiles reveal “spikes” and “slumps,” enabling reflection and earlier integration of sustainability beyond environmental optimisation, including Peace and Partnership and a broadened view of Prosperity. Results show skew toward People and Prosperity; Planet is often neglected overall.

In this paper, we address the use of Virtual Reality (VR) as a tool in the service design process. We present a framework for using VR in the service design process, in which the VR-based and physical interaction phases are conducted sequentially to leverage the strengths of each method. VR is applied in contextualization, visualizing the potential, affirmation, and futurization phases, integrated into the arc of the service design process. The presented VRSD framework is evaluated in a service design case study that addresses the design of a recycling center, where VR was applied throughout the service design process. VR can be a powerful tool at all stages of service design, including ideating, iterating, and presenting, especially for saving time and enabling fast switching between designs. However, face-to-face meetings also remain important and play a key role in facilitating discussions and supporting the dynamics among designers

This study develops a data-driven Proof of Performance to quantify productivity gains enabled by Remote Operations Support in complex mechatronic systems during operation. Remote Operations Support provides centralised, data-based monitoring and remote troubleshooting of machines, allowing error cases to be addressed without immediate on-site presence and thereby reducing downtime. The Proof of Performance operationalises productivity as additional running-state time between remote error resolution and the next error or the end of the shift, complemented by resolution-time segmentation to explain performance effects. Iterative refinement of report structure and KPI logic results in a robust measurement framework that supports consistent, comparable reporting. The developed Proof of Performance provides confirmation that Remote Operations Support can substantially enhance productivity improvement by accelerating recovery from disturbances and increasing productive machine time.

Industry 5.0 re-centres manufacturing around people, yet many shop-floor tasks remain resistant to automation because they demand dexterity, judgement, and adaptability, while imposing simultaneous physical and cognitive strain. Existing exoskeleton adoption approaches under-address this “dual load” reality and rarely treat integration with DMSs as a first-class design and implementation concern through an IPD lens. This study combines a state-of-the-art review and semi-structured interviews with two contrasting case studies (toolroom maintenance and food production), applying IDEF0 modelling, feasibility analysis, and Quality Function Deployment to translate evidence into requirements, integration points, and design direction. This resulted in a novel IPDEXO framework: a phased, three-pillar (business–design–production) prescriptive model, refined through evaluation feedback, and demonstrated through a modular smart exoskeleton concept and proof-of-concept scenarios, such as AR-guided industrial work and ERP-linked traceability.

Society is in the midst of a transformation process that can be attributed to climate change and rapid technological progress. It is anticipated that a new technological leap will be made toward biointelligent value creation. This increasing integration of biological components into technical systems is fundamentally changing development practices and creating new challenges, particularly in product development. Therefore, this paper proposes a theoretical framework for the classification of biointelligent systems based on a maturity model to determine the current state of development.

Automotive development in historically grown organizations faces significant challenges: fragmented processes, component-level optimization, and high early-phase effort. This paper proposes an end-to-end development strategy that leverages automation and AI to improve data generation, transparency, and decision-making. The approach is demonstrated through High-Voltage Wiring Harness development, illustrating its potential to enhance process efficiency and support future data-driven engineering.

This paper introduces an approach for LLM-supported requirements extraction from different data sources within SGE – System Generation Engineering. Developing new mechatronic system generations involves gathering requirements from heterogeneous artifacts, such as specifications, standards, competitor materials. Manual extraction is often time-consuming and error-prone. The proposed approach, developed through industry collaboration and academic projects, incorporates a five-step workflow comprising source identification, preparation, prompting, pre-check, and final check, as well as methodological support, such as a standardized prompt template and INCOSE criteria, to enable requirements extraction. Key features include applicability across different data sources, traceability, domain adaptability, reproducibility, expert validation, and scalability. An initial exploratory evaluation during a semester-long, practice-oriented live-lab indicates the approach’s applicability and potential benefits, such as reduced manual effort, traceability, and the possibility of using it as sparring partner. However, the evaluation also highlights challenges, such as the importance of human involvement to ensure context awareness and requirements quality.

This paper presents a proof of concept approach for predicting geometric deviations of CAD designs after their production. This could help designers to recognize possible quality issues or deviations as early as possible. Several machine learning models are trained on designs and CT scanned as manufactured parts produced by different AM processes. Results show that the used models and features have minor impact compared to the composition of the training data. The approach demonstrates the feasibility of ML based deviation prediction to support earlier and more informed design decisions.

This paper presents exploration of early-stage gripper designs for robotic grasping of fish and evaluate prototype performance through load capacity benchmark tests. The research is motivated by the need for automation within the Norwegian fishing industry, and designing for Physical-AI based control methods. We explore several gripping technologies, including compliant fingers, pneumatic soft robotic fingers, standard suction, Bernoulli suction and freezing, and present four hybrid-design prototypes combining suction with compliant and pneumatic soft robotic fingers. We find that a hybrid design with suction and compliant fingers meet industrial design requirements by grasping a polyurethane fish model with a normal vertical load capacity of 215 N, and a lateral horizontal load capacity of 65 N.

This study explores how patent drawings, combined with image processing and Vision-Language Models (VLMs), can be used to analyse dominant designs and product variants. Using eyeglasses as a case study, the workflow collects, classifies, and crops drawings, colours functional components with SORA, and estimates areas from pixel counts. Validated by a mechanical engineer, the results show an average relative error below 10%, highlighting both the potential and limitations of using patent drawings and VLMs for modularity analysis, dominant design studies, and parametric CAD generation.