This paper investigates the integration of player profiles and gamification elements into knowledge management practices within communities of practice engaged in engineering design. The study proposes a framework combining the MEREX method with gamification, tailored to Marczewski’s player types. The research aims to personalize knowledge sharing, promote user engagement, and structure engineering design knowledge effectively. The framework leverages MEREX sheets with a narrative format structured around phases of the engineering design process. Additionally, it features personalized knowledge maps and contributor profiles to foster collaboration, facilitate knowledge formalization, and encourage knowledge reuse. This integrated approach seeks to improve both community animation and overall knowledge management within engineering design contexts.

Products are often optimized for “most likely” conditions, but unexpected variations can render designs ineffective. Using examples from engineering systems, this paper explores the benefits of leveraging non-linear “payoff functions,” where small changes in conditions lead to disproportionate outcomes. By analyzing the direction and curvature of these functions near observed boundaries, designers could gain an understanding of behavior beyond expected ranges. Non-linear modeling can aid in assessing design margins, especially in long-lived systems. Integrating this approach into design processes can be helpful and effective in considering the “preparedness” of a system in the face of unexpected events of different natures.

This paper serves as a template for, and argument to, the engineering design research community to pre-register research studies. Pre-registering allows for a research plan to be validated and results published, no matter the findings. To support pre-registering, we propose a case study to study how individual perspectives and decision-making processes interact as design teams collaborate and reach consensus. We explore how narrative misalignments within a design team—disagreements on the best path forward—are shaped by individual perspectives. Driving requirements, requirements that reflect a designer's prime motivations, are used to shed light on individual priorities. A data collection and analysis plan are introduced to explain how the team will examine how consensus was achieved, which divergent personal interests persist, and how future decision-scenarios might be influenced.

The notion of “zero-sum” games describes situations characterized by scarcity and competition. Reframing these situations to yield more positive sum scenarios benefits from design strategies that can identify new sources of value, oriented toward out-comes that reward collective benefit over maximizing one’s individual strategy to win at others’ expense. This emergent practice, called Positive Sum Design, identifies and critiques the conditions that contribute to zero-sum bias in the interest of creatively reframing challenges and redesigning scenarios to encourage more cooperative strategies and pluralistic values. Positive Sum Design can be thus characterized as both a cultural critique and a set of creative practices applied toward that critique to help practitioners identify opportunities for transcending presumed constraints and transmuting zero sum games into non-zero-sum ones.

The ability to modify designs, personalize nutrition, and improve food sustainability makes 3D food printing (3DFP) an exciting emerging technology. Food materials’ complex chemistry and mechanics make it difficult to consistently print designs of different shapes. This research uses two methods to assess printed food fidelity: Manual and automated image analysis with custom-developed algorithm. Fidelity based on printed area was measured for three overhang designs (0°, 30°, and 60°) and three food ink mixtures. The manual method provided a baseline for analysis by comparing printed images with CAD images. Both methods showed consistent results with only ±3% differences in analyzing printed design areas. While the computational method offers advantages for efficiency and bias reduction, making it well-suited for fidelity assessment to assess designs.

We present a probabilistic method for assessing design reasoning in design problem settings using soundness and completeness as metrics. Building on how inference mechanisms are employed during latent need elicitation from product reviews, we compare human-led and Large Language Models (LLMs) via protocols, workshops, and surveys. We demonstrate that human reasoning patterns tend to leverage user opinions, achieving deeper coverage of need potential, whereas LLMs often produce narrower, categorically constrained needs. These findings highlight the importance of balancing inference mechanisms to ensure both coherent reasoning steps and comprehensive exploration of the design space. By formally framing reasoning during design problem-solving, we offer a foundation for developing design enabled AI and deepens our understanding of how complex reasoning unfolds in practice.

The objective of this research is to compare the requirements generated by human participants and large language models (LLMs). Requirements are statements that capture the needs and desires from stakeholders and organize them into design parameters. These statements are expressed in natural language which may lead to incompleteness and ambiguity. Due to the recent advancements in the natural language model such as ChatGPT and Gemini as a tool for requirement generation, this study investigates the quantity, variety and completeness of requirements generated by 66 pre-service engineers and 4 LLMs. This is because in some design projects, stakeholder access may be limited. The results show that pre-service engineers outperformed LLMs in variety, quantity and completeness. Future work could involve developing and comparing true human personas to LLMs.

The overall quality of final Digital Twin (DT) solutions and their ability to produce useful insights are key considerations for researchers and for the industry to readily adopt them. However, validation of DTs is often neglected in existing research dedicated to their development. Further, there is a lack of methodologies for building bi-directional information exchanges between virtual and real spaces, potentially hindering effective decision-making. This work presents a comparative analysis of several quantitative metrics by implementing them on the Digital Twin of a railway braking system as a use case. Their suitability as performance measures for validation and as thresholds to support decision-making is assessed. Their integration into a novel DT structure is shown to contribute to a well-rounded validation procedure and a practical decision-making framework.

eHealth systems, such as digital care applications or remote monitoring devices, can improve health outcomes using user-centered design principles to create medical devices that adapt to users’ needs and contexts. Data-enabled design (DED) builds on these principles by leveraging user-generated data to iteratively refine systems based on real-world use, enabling adaptive and context-sensitive solutions. However, its exploratory and iterative nature conflicts with the rigid protocols required in clinical trials to evaluate safety and effectiveness. This study revises DED in alignment with clinical trial requirements, identifying four key challenges and proposing a four-phase Clinical Data-Enabled Design (C-DED) framework. This framework reconciles exploratory design with trial methodological demands, supporting the development of safe, effective, and user-centered eHealth medical devices.

This paper proposes a redefinition of interfaces as dynamic, adaptive systems crucial for managing the increasing complexity of modern systems. Drawing on diverse domains, the paper identifies key interface properties such as adaptability, cost-efficiency, and error response. The paper introduces a novel Generic Interface (GI) architecture, utilizing a model-based systems engineering approach. The GI architecture features modular components, designed to handle integration, data management, and error resolution. A case study of smart grids demonstrates the effectiveness of the GI architecture in addressing challenges like integrating diverse energy sources, ensuring grid reliability, and enabling demand response. The proposed GI architecture provides a robust framework for integrating complex systems, emphasizing adaptability, cost optimization, and error response.

To support the transition towards a circular economy in hospitals, this qualitative study aimed at understanding how the adoption of reusable surgical gown can be facilitated. It investigates design features that enhance usability and promote sustained (re)use. A wearing test identified difficulties in wearing reusable gowns. Data collection included observations of 34 surgeries and a survey completed by 73 respondents. Thematic analysis revealed opportunities to improve usability, such as optimising packaging to speed up donning, a wider neck opening to reduce discomfort, and incorporating ‘tearable’ closures to simplify doffing. Innovation strategies relevant to the users involve thermal regulation, monitoring gown performance, and including reusable gowns in custom procedure tray packaging. These findings are discussed in relation to design adjustments and value-chain partners.

The evolving needs of customers and stakeholders necessitate the collaboration of diverse system elements within a cyber-physical, socio-technical network. Such Sociotechnical systems are characterized by numerous complex interdependencies as well as by endogenous and exogenous influences. A key issue that developers must address is the mitigation of data and information uncertainties. This paper introduces an ontological approach to facilitate the identification and mitigation of uncertainties in data and information within a model-based methodology for satellite development projects. The work outlines the results of preliminary studies forming the foundation for this ontological concept. The proposed approach comprises an overarching General Ontology, complemented by a Uncertainty and Structure Ontology, creating a framework for uncertainty management in satellite development.

Research is presented on the development of student confidence in design through the use of design exercises in a non-design (materials engineering) course. This work revisits a prior study incorporating over three times the number of subjects, substantially expanding the statistical robustness of the analysis. Four distinct design exercises, covering topics like tensile failure, creep, impact, and fatigue, are integrated into the course, each employing structured pre- and post-assessment surveys to gauge confidence levels. Results consistently show significant improvements in student confidence, with post-exercise scores rising by 2 points on a 9 point Likert scale. This work underscores the efficacy of design exercises in bridging engineering science with practical design application of the topical knowledge, with implications for optimizing engineering education strategies.

Regenerative Responsibility (RR) emerges as a transformative framework for design education, addressing the urgent need for sustainability and ethical practices in the field. By integrating principles of ethics, regeneration, and pedagogy, RR redefines the role of designers as agents of systemic change. It incorporates methodologies such as project-based learning, systems thinking, and ethical reflection to align design practices with social, environmental, and economic considerations. Regeneration thinking empowers future designers to adopt innovative and responsible approaches, positioning design education as a catalyst for addressing global challenges and fostering regenerative practices across disciplines.

Effective product development relies on creating a requirements document that defines the product’s technical specifications, yet traditional methods are labor-intensive and depend heavily on expert input. Large language models (LLMs) offer the potential for automation but struggle with limitations in prompt engineering and contextual sensitivity. To overcome these challenges, we developed ReqGPT, a domain-specific LLM fine-tuned on Mistral-7B-Instruct-v0.2 using 107 curated requirements lists. ReqGPT employs a standardized prompt to generate high-quality documents and demonstrated superior performance over GPT-4 and Mistral in multiple criteria based on ISO 29148. Our results underscore ReqGPT’s efficiency, accuracy, cost-effectiveness, and alignment with industry standards, making it an ideal choice for localized use and safeguarding data privacy in technical product development.

This paper presents two studies with packaging design engineers and quality and risk professionals in the pharmaceutical packaging industry, addressing the critical need for design support. The studies contribute to the development of a framework aimed at balancing risk management and multi-user experience in the context of product support. A review of prior work highlights the gap in tailored support for designers in this field. Using structured interviews and thematic analysis, seven key requirements were identified to guide the framework’s creation. A user persona was also developed, capturing the core responsibilities, challenges, and motivations of quality and risk professionals. These findings provide actionable insights to aid designers address complex regulatory and user-centric challenges, paving the way for innovation and improved outcomes in pharmaceutical packaging design.

This study applies Artificial Intelligence-Generated Content (AIGC) to design cultural products inspired by Sanxingdui, an ancient Chinese civilization famed for mystical bronze artifacts. Addressing the challenge of merging tradition with modernity, an AIGC framework automates cultural element extraction, generates design concepts, and optimizes aesthetics using generative models. Comparative analysis via Quality Function Deployment (QFD) shows AIGC products achieve higher user satisfaction in aesthetics, symbolism, and engagement. The research highlights the significance of AI in enhancing creativity, efficiency, and cultural preservation, despite algorithmic limitations. It provides actionable strategies for integrating AI into cultural industries, bridging heritage and technology to drive sustainable innovation.

Exploring patterns in large text corpus is essential for effective knowledge discovery in research domains. However, machine-driven methods often introduce noise and rely heavily on parameter thresholds. Human expertise is therefore essential for ensuring reliable outcomes. This study conducts a comparative analysis of a classification task performed by both human and computer algorithms. During the task, human experts are asked to categorize a list of abstracts based on their semantic contents, where computer algorithms perform computations, including network analysis and document embeddings, to group the abstracts. The findings show a significant level of disagreement between human and computer-generated clusters, indicating the need for further investigation into the factors influencing community categorization and incorporating more advanced techniques to improve the results.

The increasing complexity and connectivity of the mobility system and modern automotive systems, particularly connected autonomous vehicles, demand a paradigm shift toward resilience-by-design to address disruptions in dynamic environments. Unlike established safety and cybersecurity engineering in automotive, resilience engineering has yet to be systematically integrated into development processes. This paper defines resilience using a standard-based definition method, emphasizing disruption tolerance, adaptability, and recoverability. We identify action fields to advance the topic and propose a resilience-by-design framework extending safety and cybersecurity perspectives. Resilience-by-design offers strategies and methods to design robust, adaptive systems, ensuring reliability and availability of automotive systems, functions, and components in operation.

This paper presents a systematic literature review to figure out challenges of integration of used components into new product generations. Reuse of components is an essential strategy of circularity and is becoming highly relevant as resources are limited and sustainability requirements have to be meet across industries. The reuse process was examined from a constructive perspective. It was found that the reuse process is not uniformly defined and that there is a divergent understanding of it. This divergent understanding continues through the Reuse process steps and the added value of using Reuse. Various technical challenges of reuse were identified. These challenges were translated into requirements that are intended to enable reuse for used components. An initial concept for solving the design problem of integrating used components is proposed with the help of these requirements.