Early-stage product development plays a critical role in shaping the direction of a product and the range of design decisions that follow. While established approaches often translate structured inputs, such as predefined needs or user scenarios, into technical specifications, less attention has been given to context-embedded experiential feedback. Consumer insights collected in everyday routines are often affective, ambiguous, and shaped by lived context, making them difficult to express in formal design language. This study presents a case-based translation process that transforms context-rich experiential insights into usable design requirements in early-stage development. Drawing on a consumer tasting session of a functional food product, we outline a stepwise approach moving from raw experiential statements to categorized insights, articulated design requirements, and value-informed prioritization. By clarifying the interpretive work required to move from lived experience to requirement language, the study offers a practical bridge between consumer insight, routine-based product experience, and engineering decision-making.

Aiming to overcome the challenge that manufacturers still have trouble evaluating AM suitability of products in early phases of product development, this study evaluates the capabilities of TRIZ in supporting this identification. A reverse TRIZ-based approach is developed: by identifying which technical contradictions are present in the developed product and aligning those with the DfAM-suitable inventive principles from TRIZ, a score is developed that determines DfAM suitability. Results suggest TRIZ supports early-stage AM product identification when design solutions are required.

This paper presents a V-Model-based approach for the design of measurement systems for condition monitoring. Although data-driven methods such as machine learning and signal processing have gained increasing attention, the reliability of condition monitoring still strongly depends on the quality of the underlying measurement system. In practice, the development and integration of measurement systems are often performed in an ad-hoc and experience-driven manner. To address this issue, a structured engineering design approach is proposed. The method is based on a clear differentiation between primary measurands, which directly describe the system state, and secondary measurands, which capture operational, assembly-related and environmental influences on measurement quality. This differentiation is embedded in a V-Model structure and supports traceability from requirement definition to validation. The proposed method provides a practical guideline for engineers and supports structured decision-making throughout the development process. Its applicability is demonstrated using a bearing condition monitoring system.

This study investigates the paradox between successful agile implementations in highly regulated defence contexts and persistent industry-wide barriers. Through semi-structured expert interviews with defence practitioners four contextual themes emerge. Customer distance and extreme temporal prioritization function as barriers, contradicting agile principles of continuous collaboration. Conversely, crisis-driven transformation of the quality-cost-time triangle and VUCA conditions create enabling preconditions. Successful agile adoption in defence requires contextualized organizational interventions that amplify enablers while addressing structural constraints.

Response times have long been used to reveal the actions products naturally evoke. In a research method, stimulus-response compatibility (or SRC) method, users physically respond to various combinations of products and stimuli. Faster reaction times in some combinations, compared to others, provide quantitatively-backed evidence about users’ natural tendencies and preferences when they see a product. Despite its potential, the SRC method has been used within design research in only a very limited context such as studying affordances of objects that can be gripped. Our aim in this study is to push through these limits and discover other contexts and use-case scenarios SRC might be useful in. Through three different experiments involving 24 participants, we assess the validity of using response times to investigate perceived affordances and product-related associations.

In multilingual and cross-cultural information design, standard grid systems are primarily developed for Western scripts, which can create challenges when applied to Chinese characters. The square-shaped structure of Chinese characters and differences in reading rhythms create gaps in theoretical understanding and practical application of grid systems. In the first stage of this study, cultural grid elements were identified from the Chinese calligraphic Jiege, followed by visual feature coding and parameter configuration. The second phase implements Jiege constructs into parameterised layout rules through parameterisation, modular construction, and the proposed Chinese grid system. The third phase, design validation, applies the Chinese grid system to the UI layout. The fourth phase confirms the usability evaluation of the Chinese grid applied to UI layout through a focus group. The results indicate that culturally responsive Jiege grid parameterization improves hierarchical information recognition, thereby enhancing Chinese user experience and trust during specific tasks.

Design typologies strongly influence engineering outcomes by determining the range of feasible solutions long before detailed design begins. However, contemporary generative design methods primarily optimise parameters within predetermined concepts, providing limited support for exploring fundamentally different behavioural principles and structural organisations. This paper proposes Typology Optimisation (TypO) as a formalisation of a conceptual search-and-selection problem for representing, exploring, and evaluating typological design alternatives prior to prototyping or detailed embodiment. Grounded in the Function-Behaviour-Structure (FBS) ontology, TypO formulates conceptual design in engineering as a heuristic-guided multi-objective combinatorial optimisation (MOCO) problem operating over discrete behaviour-structure configurations satisfying a given functional context. Candidate concepts are represented as many-to-many mappings between function (F), behaviour (B), and structure (S). Constraint satisfaction, morphological reasoning, admissibility predicates and compatibility evaluation are employed to enumerate, traverse, and evaluate the resulting combinatorial search space. The methodology supports systematic comparison and selection of alternative typologies capable of satisfying shared functional requirements through distinct behavioural and structural organisations. By supporting computational completeness and iterative assistance during early-stages of design processes, where decisions exert greatest influence on utility, feasibility, performance, manufacturability and system identity. TypO establishes a methodological foundation for concept-level exploration in generative design workflows.

Extended reality (XR) offers highly immersive experiences. At the same time, users are still embedded in everyday life. Designers need to consider the context of immersive experiences, e.g. being situated within shared physical and social environments. In this paper, we propose the concept of the digital periphery—the hybrid space where immersive XR activity intersects with its physical surroundings. Further, we introduce a scenario-based, embodied co-design method as part of a workshop frame. The method allows for an ethical exploration of this space at an early, pre-solution stage of the design process to surface latent frictions, asymmetries, and ethical tensions between immersed users and those around them. As an output, it generates transferable design knowledge by making dynamics observable, discussable, and negotiable among designers and stakeholders.

The rhetoric of collaborative robotics hides a paradox: cobots rarely collaborate. Despite the language of partnership, implementation practices overwhelmingly substitute or deskill workers while portraying humans as “operators” or “human factors.” Beneath talk of partnership lies a logic of replacement. Drawing on feminist HCI and theories of tacit and embodied knowledge, this paper exposes how an efficiency discourse dehumanizes labour. Reframing cobot design as ethical and relational restores the “co” in cobot and reduces the risk of losing valuable knowledge in the implementation process.

Challenges in engineering design require more efficient and effective approaches for the development of new product generations based on references. The combination of the structural conceptual view of SGE and the data-driven, geometry-focused view of technical inheritance (TI) in cross-generational product development offers great potential. This contribution presents a detailed analysis of both approaches regarding similarities, differ-ences, and limitations with resulting interfaces as a theoretical foundation to systematically exploit the synergies of SGE and TI in the future.

Falls among older adults are commonly addressed through hazard reduction and clinical risk management. However, such approaches often overlook how instability emerges within everyday domestic interaction and how values such as autonomy and environmental coherence shape the acceptance of interventions. This study reframes fall risk as an emergent socio-technical phenomenon arising from the coupling between bodily perception, routine practice, and spatial configuration.

A cross-sectional survey was conducted with 184 community-dwelling older adults in Chile to explore perceptions of bodily vulnerability (pain, instability, visual difficulty), behavioural adaptations in daily activities, prior fall experiences, and attitudes toward assistive solutions. Exploratory factor analysis identified two interrelated dimensions: bodily vulnerability and behavioural adaptation. Cluster analysis revealed two interaction profiles: a transparent mode characterized by implicit bodily trust, and a negotiated mode involving systematic adaptive strategies. Logistic regression indicated that profile membership was primarily associated with functional perceptions rather than demographic variables.

The findings suggest that behavioural adjustments tend to emerge in relation to perceived instability, highlighting fall risk as a dynamic interactional condition rather than a static environmental hazard. For design and engineering, this implies shifting from device-centered prevention toward value-based product and service development that enhances routine fluency, anticipates transitional bodily states, and integrates safety without undermining autonomy or environmental coherence. The study contributes an empirically grounded framework for responsible and context-sensitive design in aging societies.

Recent drone technology development has shown increased technological agility through the use of commercial-off-the-shelf components. It is therefore important to design drone detection technology using the same agility and price. This study presents the design and development of a multi-modal drone detection system using cost-effective sensors and machine learning, while exemplifying a technology design methodology. The system can replace a monolithic central detector with a distributed network of small, enabling flexible deployment and lower costs than traditional solutions.

The paradigm of technical inheritance describes cross-generational product development based on product lifecycle information obtained from data collected by strain-gauge sensor networks. To minimise the sensor quantity regarding costs and installation effort while maintaining a max. accuracy of information, an optimal sensor placement (OSP) is required. Two approaches are compared in this study, where the modal method is used to reconstruct a whole deformation field from local strain measurements. A newly developed approach proved significant benefits in contrast to a pre-existing approach.

With an increasing technology maturity, the additive manufacturing (AM) of copper components becomes a viable option if lot sizes, costs, and technological advantages match the peculiarities of AM processes. Beyond AM for standard components, the manufacturing of multi-material components experiences rising research interest. In this study, the current state of multi-material powder bed-based AM with copper and other metallic materials is analyzed. An approach is presented, which allows the combination of AM and conventional processes by manufacturing onto conventionally manufactured substrates made from different materials, as well as the utilization of tailored material properties through AM.

This paper presents a modular design workflow for personalised sports equipment balancing athlete-specific customisation and system-oriented standardisation for existing equipment platforms. A case study of a customised rowing seat for a paralympic athlete forms the foundation of the approach. Integrating athlete 3D-scanning, rapid prototyping, production through 3D-printing and composite reinforcement allow designers to benefit from insights made throughout the design process. The proposed approach offers a replicable framework for inclusive design in adaptive sports and rehabilitation.

This paper applies a dynamic requirements approach to develop a low-cost test machine for Triboelectric Nanogenerators (TENGs). TENGs are an emerging and conceptually elegant energy-harvesting technology that converts mechanical motion into electrical energy, but they lack standardized and accessible test methods. They are sensitive to actuation conditions and the measurement setup, limiting comparability across studies. A dynamic requirements approach treats requirements as provisional and refines them through prototyping, enabling early identification of unknown constraints. Four actuation architectures were built and tested: a modified Prusa MK3 platform, a rack-and-pinion mechanism, a hydraulically transmitted actuator, and a belt-and-pulley rig. Each prototype was evaluated using performance and noise measurements. The resulting requirement updates clarified trade-offs among actuation performance, electrical/mechanical decoupling, and practical complexity.

Individualised additive manufacturing requires designers to operate under user-specific variability that cannot be fully resolved at the outset of development. In engineering design literature, iteration is often treated as a cost to be reduced rather than as a mechanism to structure uncertainty. This paper examines iteration as a component of design reasoning in the context of individualised additive manufacturing. Using the development of a combat helmet liner as an empirical basis, successive iterations are analysed with respect to geometry definition, manufacturability assessment, and energy-absorption characterisation. The findings indicate that iteration does not merely refine solutions but enables the progressive clarification of requirements under variable boundary conditions. The paper contributes an empirical account of how iteration functions as a structured response to design uncertainty in individualised additive manufacturing.

AI assistants are increasingly used in Systems Engineering supporting activities such as requirements engineering, system modelling, and documentation. Existing research has predominantly focused on technical capabilities and instantiation of isolated use cases. Far less attention has been given to the design of human-AI interaction in engineering contexts leaving the design rationale of interactions between system engineers and assistants opaque. Yet effective interaction design is decisive for developing assistants that produce real impact. This paper analyses how systems engineers currently work with AI-based assistants. Through a systematic review of existing solutions and real-world applications, six main, recurring interaction patterns are identified, providing directions for the design of human-AI interactions in Systems Engineering.

Smart product–service systems (sPSS) combine physical products with digital services to deliver lifecycle value, increasingly leveraging data to improve offerings, optimize operations, and enable new business models. This paper examines a SysML-based, data-centric modeling approach for designing sPSS, implemented in a real development project. The study builds on a merged dataset from literature, standards, and five industry workshops, enabling the modeling of a multi-stakeholder network and several smart services. The approach is validated through 16 industry interviews.

Rural healthcare delivery often relies on Primary Healthcare Centres (PHCs) and Mobile Health Units (MHUs) that operate as parallel systems, resulting in fragmented infrastructure and inefficient resource use. This paper presents a design-oriented approach for integrating these systems through structure sharing. Using functional decomposition and overlap analysis, service redundancies and complementarities between PHCs and MHUs are identified. The study then examines how resources can be co-utilised to support multiple functions. The findings reveal reciprocal infrastructural complementarities: PHCs offer stable spatial infrastructure, staff, and essential amenities, while MHUs provide mobility and flexible service capabilities. Based on these insights, the paper synthesises an integrated healthcare architecture that links PHCs and MHUs into complementary assets rather than independent components of rural healthcare delivery system (RHDS).

The Live‑Lab LAT establishes a validated framework for investigating sociotechnical systems in agile product de-velopment. Integrating the dimensions of People, Organization, and Technology, it enables authentic yet controlled observation of agile teamwork and competence development. Evaluation results show measurable improvements in self‑organization, reflection, and the practical application of agile principles. The findings demonstrate that the Live‑Lab LAT provides a reproducible environment for systematically measuring and fostering agile action compe-tence under realistic conditions. Moreover, the Live‑Lab successfully reproduces the dynamic interplay between managerial objectives and operational conditions characteristic of industrial sociotechnical systems, confirming its ecological validity. Overall, the study highlights the Live‑Lab LAT’s dual function as a learning architecture and re-search infrastructure, advancing empirical research on agile product development within sociotechnical contexts.

Cross-university development of teaching and learning materials has advantages for lecturers and students. This paper investigates potential and challenges of the development and the use of materials in Engineering Design Education courses on an exemplary project. For the investigation, lecturers were interviewed about today’s benefits of cross-university developed materials five years ago. These findings allow generalization for future cross-university development of learning materials, for example the importance of a high modularity which allows easy selection of individual materials.

The fundamental education of engineering students in the field of machine elements and engineering design is essential for future lectures and for a successful career. These fundamentals include technical drawings, standardizations, and machine components, along with their applications in the field. Students, particularly those in earlier semesters, show weaknesses in the fundamental understanding of the components employed in the domain of mechanical engineering. New learning methods and technologies, such as virtual reality and artificial intelligence, can be employed to assist these students. This paper analyzes the mistakes students made in their gearbox design assignments and proposes methods to minimize these errors. Despite the substantial initial time and energy investment, it is shown that courses using new technologies such as virtual reality enhance the student’s study while providing a fun way to further develop their skills.

Digital fabrication courses require tools that support iterative, process-oriented documentation and team collaboration. This paper presents the evolution of DesignEDU, a web-based platform for documenting learning progress in university-level digital fabrication education. Across two course iterations (248 students, n=226 survey responses), user feedback systematically drove platform changes: from cloud-hosted to self-hosted infrastructure, from a basic block editor to a feature-rich collaborative editor with real-time collaboration via Conflict-free Replicated Data Types (CRDTs), and from no AI integration to an embedded AI teaching assistant with speech-to-text and text improvement capabilities. Surveys in both cohorts confirmed consistent usability (ease of use: 3.97–4.03/5, learnability: 4.09/5) alongside persistent critical gaps, namely no autosave, no simultaneous editing, and cloud dependency, that motivated the platform's redesign. We trace each user-identified issue to a specific engineering solution, demonstrating a feedback-driven methodology for evolving educational technology in design education contexts.

AI technologies are expanding rapidly into product development processes (PDP). This study examines the role and potential of AI-based object detection (AIOD) within the PDP framework as defined by Pahl and Beitz. A literature review reveals that, while AIOD is currently applied primarily in production-proximate quality-assurance settings, its integration into the development workflow remains limited. Initial efforts have focused on CAD- and drawing-based methods that incorporate AIOD during the concept and embodiment phases. A complementary survey of practitioners confirms that AIOD is perceived as especially useful in the concept phase, offering benefits such as error detection, time savings and shorter development cycles. However, respondents also express concerns about reliability and potential negative impacts on technical competence. Overall, the findings reveal a distinct discrepancy: While AIOD is technologically mature in manufacturing, its adoption in the PDP remains limited.

In today's research, the transparency of data and their provenance is of particular importance. The paradigm shift toward open data poses numerous challenges for researchers. They are currently confronted with a plethora of different tools and services for research data management (RDM). At the same time, institutions and funding agencies demand compliance with policies and guidelines regarding RDM and reusable data. This uncertainty about RDM implementation contributes to the lack of reusable data in the engineering domain. We investigate different mutual relationships and dependencies for RDM and present an approach to support cultural change for RDM by applying a method for change management.

Early design phases are increasingly shaped by deep uncertainty, especially in mobility innovation. This paper reframes requirement elicitation as a design activity influenced by framing, biases, and collective sensemaking. It introduces a typology of technical, semantic, and interpretive uncertainty and discusses how a human-centered perspective can complement Systems Engineering by supporting reflection, alignment, and shared understanding in early design decisions.

In a volatile and complex (VUCA) environment, companies must design product development processes that are both flexible and goal-oriented. To combat this agile methods are increasingly used in cyber-physical systems (CPS) development, which pose challenges due to complexity and long cycles. In order to implement and further develop the process in a targeted manner, it is therefore necessary to measure the success of the agile implementation. Existing Performance Measurement Systems (PMS) focus on the output of development processes, focusing on “hard” metrics like time-to-market, neglecting “soft” factors that describe the process in itself, e.g. communication or transparency. This work develops a process model for agile CPS success measurement, integrating classical and agile factors for continuous, learning-oriented evaluation and improved transparency and innovation by using an agile impact model.

The Extended Contact and Channel Approach (C&C²-Approach) can enhance the modeling of Embodiment Function Relations (EFR). Based on empirical observations from several Live-Lab applications within one research environment, this paper identifies recurring challenges in the manual application of the Extended C&C²-Approach, particularly regarding cognitive load, consistency management, and the coordination of multiple System States and Views. Rather than claiming general validity across all C&C² application contexts, the paper uses these observations as a structured basis for deriving objectives for future digital tool support. The primary contribution of this paper is a structured gap analysis and the derivation of specific objectives for digital tool support. Based on these objectives, we outline related realization approaches from software engineering, centered on Model-Driven Software Engineering, consistency management and versioning.

Generative AI (GenAI) is reshaping UX and digital product design, signalling a paradigm shift in design practice. In this context, job postings constitute a valuable source for examining how emerging trends are articulated and legitimised in industry. This study analyses LinkedIn postings to assess how GenAI-related skills and job requirements are positioned within design roles. Using automated web scraping for data collection, Levenshtein distance for deduplication, and inductive thematic analysis, the findings reveal modes of GenAI integration in UX practice and emerging employer expectations in which GenAI is primarily framed as a means to accelerate product development cycles. Such expectations risk reinforcing short-term approaches that may conflict with context-sensitive, value-based design practices. Expected contributions include informing curriculum adaptations in design education, supporting pedagogical strategies that critically engage with GenAI, and providing a replicable protocol for studies with job postings in design research, supporting further inquiry into evolving industry paradigms.

In product development, standards govern design processes. Within ISO-GPS, they deal not only with geometry, but also with roles and responsibilities. This paper analyzes ISO/TS 21619 as a socio-technical instrument that shapes boundaries between design, manufacturing, and verification. While the technical specification separates responsibilities, engineering design practice relies on interdisciplinary, cross-organizational collaboration. By revealing tensions between normative content and collaborative reality, this study contributes to a more collaborative view on specification governance.

Global cost pressure and increasing competition require manufacturing companies to rethink how their systems are designed and engineered. Cross-functional collaboration between product development and production system development offers significant cost-saving potential, while emerging technologies such as Model-Based Systems Engineering (MBSE) and digital collaboration tools enable new forms of integration. However, despite their technical promise, studies show that integrative product and production system development (IPP) is still rarely implemented in practice, often due to technical, organizational, and cultural challenges. Addressing this gap, this paper presents a systematic literature review that identifies actionable recommendations for improving IPP in complex engineering contexts. In total, 42 improvement measures are derived and structured, including not only technological enablers but also collaborative practices and organizational aspects. The findings provide an overview and recommendations that support organizations in systematically improving IPP adoption and exploiting technological and organizational integration potentials.

Today’s products in the automotive industry show high variability and require complex configuration. With rising variability, both product development and cross-domain coordination become more intricate. This paper qualitatively explores the strengths and limitations with which current modeling approaches enable cross-domain representation of dependencies in complex portfolios. Based on a Volkswagen case study within a workshop, DSM, MDM, SysML, and PLE were analyzed. Results show current methods capture mainly static relations, requiring dynamic frameworks for consistent variant management.

Traditional engineering design pedagogy, built on linear, phase-gated project management models, is increasingly insufficient for preparing students for modern AI-driven design projects. This new paradigm presents a dual pedagogical challenge: 1) a process challenge, as AI projects are defined by iterative, data-dependent, and uncertain lifecycles, and 2) a social challenge, as AI shifts the engineer's role from "doer" to "orchestrator" within a complex human-AI collaboration. This paper presents an integrated 10-unit curriculum development from an Erasmus+ EU project, as a novel pedagogical solution. The framework is designed to scaffold process uncertainty, systematically bridging the gap between traditional project management and the unique, iterative lifecycles of AI projects. This scaffold then enables the teaching of new, human-centric collaborative competencies through active learning interventions, which are grounded in practice-based learning principles. These include stakeholder co-design simulations and a role-playing exercise where students must navigate the inherent conflicts between AI performance and human-centred design goals.

As generative AI increasingly permeates everyday technologies, UI/UX design must shift from optimizing usability toward shaping human–AI relationships. This paper examines how relational UI/UX for human–AI coexistence can be conceptualized through a futures-oriented design project. Rather than treating AI solely as a functional tool, the project positioned AI as both design medium and relational counterpart, influencing how future interfaces were imagined and structured. Analysis of the design process and resulting concepts reveals a shift from interface design centered on task efficiency to interface design oriented toward coexistence and mutual influence. In this reframing, UI/UX operates not merely as a surface for interaction but as a condition that configures human–AI relations. The study argues that designing for human–AI coexistence requires reconceiving UI/UX as relational infrastructure, expanding its role from functional mediation to the construction of shared futures.

This study investigates how generative artificial intelligence (AI) operates in educational spaces to determine its ability to develop educational feedback systems. This study situates AI-supported feedback within the broader paradigm shift toward hybrid human–AI socio-technical systems in design education. Qualitative methods were used to conduct semi-structured interviews with 25 students and 6 professors. The results demonstrate that students and teachers face ongoing difficulties with their current feedback system, mainly due to time constraints to provide excellent feedback. The participants envision a Human–AI collaborative process model to solve learning and teaching problems.

The rapid advancement of Generative AI is reshaping industrial design practices, calling for informed responses within design education. This study examines industrial designers' perceptions, attitudes, and behavioral intentions regarding the integration of GenAI across the stages of the Double Diamond design model, exploring its implications for design pedagogy. Using a mixed-methods approach, survey data were gathered from 70 participants, supplemented by ten semi-structured interviews with educators and practitioners in South Korea who actively use GenAI tools. Thematic analysis revealed that participants identified both significant opportunities and limitations when employing GenAI within different design stages. Key factors were identified for effectively integrating GenAI into industrial design education, including foundational design competencies, AI literacy and understanding, AI operational fluency, and ethical governance to support reflective and responsible practice. This research contributes to ongoing discussions about how industrial design education might evolve to address the challenges and harness the potential of generative technologies.