Additive Manufacturing (AM) design projects often fail when feasibility and practicality are unclear during product development. To address this, we developed a dual design for additive manufacturing (dual DfAM) worksheet to support users with novice to intermediate DfAM competence. The worksheet incorporates restrictive and opportunistic criteria and calculates a feasibility and practicality index for quick evaluations. Verified through a workshop with 73 engineering students, all participants found the worksheet helpful, and 71 expressed willingness to reuse it in future design projects. Furthermore, we found indications that repeated use of the worksheet could enhance dual DfAM competence, as designs became more feasible and practical. These results highlight the worksheet’s potential as a structured tool for improving dual DfAM assessment and decision-making in product development.

Mixed reality prototypes are used for applications like design, analysis, and training. They combine high-fidelity overlays on low-fidelity tangible prototypes, giving users physical interactions in virtual environments. Suitable virtual environments are crucial in taking full advantage of these prototypes. However, there is a lack of guidance in the literature on choosing environment reconstruction methods for various applications. The rapid advancements in this area necessitate the characterisation of the reconstruction methods. This paper thus presents a novel knowledge framework for mapping the reconstruction methods with the requirements of MR prototype applications. The aim of the proposed framework is to help designers and engineers make informed decisions. The effectiveness of the framework has been illustrated using five reconstruction methods and testing via four case studies.

This research examines, during the human-AI interaction process, how generative AI’s depiction of human bodies reflects and perpetuates able-bodied norms, positioning disabled or grotesque bodies as “errors.” Through a feminist and disability studies lens and employing archival research and visual analysis, this research challenges traditional notions of bodily normativity, advocating for inclusivity in AI-generated imagery. It underscores how labeling nonconformity as an error perpetuates able-bodied standards while erasing the visibility and autonomy of disabled bodies. By critiquing generative AI’s role in reinforcing societal norms, this study calls for reimagining human-AI interactions with a shift in perception and advocates for an approach that neither devalues nor excludes disabled bodies.

Food production systems are shaped by external factors, such as social events and economic shifts, which influence and are influenced by labour dynamics—e.g., workforce availability—and human factors—e.g., worker skills. Using a systems approach, this paper explores how labour shortages impacting worker teams—such as in terms of mixture of availability, skills, and human behaviours—affect production and quality. UK apple harvesting is chosen as a case study due to its reliance on skilled seasonal migrant workers. Findings highlight the need for strategies such as upskilling local workers, enhancing training programmes, and adopting new technologies to mitigate labour shortages and enable high-performance collaborative worker groups.

Engineering of lightweight and robust structures is significant in mechanical engineering. Nevertheless, weight optimization of such structures leads to undesirable vibrations. Modal analysis is a common technique used in industry to investigate vibration behaviour. The classification of the mode shapes resulting from the analysis is conducted through human visual inspection, which can be time-consuming and susceptible to error. This paper presents an exploratory study investigating the potential of ML methods to classify three-dimensional vibration modes of truck frame structures. The aim is to evaluate the potential of such an approach to automate the modal analysis process to streamline the development process. As a result, the developed ML model can classify the vibration modes with high performance and additionally demonstrates flexibility regarding changes in geometry topology.

This study explores user engagement and strategic interaction with a newly designed tangible game board- a 3x3x3 cube frame with 27 voids and 27 game pieces. 15 teams, each with 2 players, were provided with only the game set to develop their own game rules and strategies, encouraging participants to engage in the spatial and experiential aspects that the game board offers. Researchers observed how players approached the 3D structure and developed gameplay tactics without predefined rules, fostering creativity and exploration. Importantly, the study captured feedback on the structure’s versatility, with many participants developing new game rules, which implies its potential as a game platform. The experiments revealed that one of the emergences resulting from the affordances of the game platform is a game strategy for 3D Tic-Tac-Toe, amongst the many other possible games identified.

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.

This systematic literature review comprehensively assesses the risks associated with implementing Industry 4.0/5.0 technologies. It clusters these risks into six groups (strategic, financial, operational, technological, environmental, and sociocultural). Using a PRISMA-guided approach, the analysis of 83 peer-reviewed papers identified 36 unique risks out of a total of 811. The findings reveal critical challenges, including in cybersecurity threats, financial burdens, technological obsolescence, and workforce adaptation. These results provide a structured risk categorization that can assist enterprises, in effectively mitigating risks and aligning their strategies with Industry 4.0/5.0 transitions. This framework closes knowledge gaps and offers actionable insights for a robust and sustainable implementation.

Recent advancements in machine learning (ML) offer substantial potential for enhancing product development. However, adoption in companies remains limited due to challenges in framing domain-specific problems as ML tasks and selecting suitable ML algorithms, requiring expertise often lacking. This study investigates the use of large language models (LLMs) as recommender systems for facilitating ML implementation. Using a dataset derived from peer-reviewed publications, the LLMs were evaluated for their ability to recommend ML algorithms for product development-related problems. The results indicate moderate success, with GPT-4o achieving the highest accuracy by recommending suitable ML algorithms in 61% of cases. Key limitations include inaccurate recommendations and challenges in identifying multiple sub-problems. Future research will explore prompt engineering to improve performance.

In the context of volatile markets, characterised by a need for continuous product development involving module-wise product modifications, the importance of flexibility as an attribute of products and their production system has been increasing. This paper presents a methodological approach focusing on the flexibility evaluation of modules regarding their interfaces. The subject encourages engineers and researchers to analyse and rethink the interface design and the location of module boundaries regarding change propagation. The method was validated using the Design Method Validation System (DMVS) to determine its usefulness, applicability and acceptability. The design workshop for validation was applied to a product family of trunk lids by employees of a German car manufacturer.

This study examines the integration of values into design methodologies, essential for guiding value-driven design processes. Values, spanning ethical, economic, and functional dimensions, influence decision-making and project outcomes. Through Principal Component Analysis (PCA), five clusters of design methodologies were identified, each addressing distinct aspects of value integration. Interviews with designers highlighted challenges in defining, formalizing, and adapting values due to their inherent subjectivity and volatility. This study, by adopting a values-centered perspective, enriches our understanding of design methodologies and paves the way for more informed methodological choices across various contexts.

Assumption-making is a critical cognitive process in design, where incomplete information is ever-present. Understanding how assumptions are formed, maintained, and adapted can offer key insights into decision-making. While theoretical explorations of assumptions exist, empirical research remains limited. This pilot study investigates how varying temporal constraints influence assumption-making while solving ill-structured problems. The challenge lies in isolating the temporal and cognitive factors at play. The early insights reveal that task ambiguity, contextual framing, and time constraints play significant roles in shaping responses, highlighting the dual nature of assumption-making as both adaptable and resistant to change. The insights highlight the importance of strategic task design that balances ambiguity and structure to deepen our understanding of assumption-making.

Design decision-making under competition is a critical challenge in real-world engineering design. These challenges are compounded by bounded rationality, where cognitive limitations and imperfect information influence decision-making strategies. To address these issues, we develop a game-theoretic research platform to investigate team-based design under competition. This platform abstracts and simulates real-world competitive design scenarios through controlled experiments. It features a user-friendly interface to collect behavioral data, which supports the analysis of team and individual strategies. Additionally, we validated the platform through a pilot study, demonstrating its ability to capture realistic design features and generate meaningful insights into competitive design behaviors.

The study investigates the cognitive aspects of aesthetic taste, which is a subjective quality linked to individuals’ ability to make superior aesthetic judgments. It explores how evaluation modes during product choice decision-making relate to aesthetic taste. We defined taste through two dimensions: expertise (professional experience) and acumen (consumption experiences). By comparing research participants in a consumer study across these dimensions, we analyzed decision-making patterns using both quantitative and qualitative methods. Our results show that participants with low aesthetic taste (across both dimensions) express their product choice in terms of product attributes they dislike. We also find that the expression of personal preferences is associated with low aesthetic taste for the expertise dimension but is associated with high aesthetic taste for the acumen dimension.

The marine industry is increasingly adopting platform and modular design strategies while facing growing sustainability regulations and emission constraints. This paper proposes an approach that integrates scope 3 upstream CO2 emissions (i.e., procurement) into a Decision Support Environment (DSE) for design space exploration of alternative modular ship design concepts. The DSE, deployed in the conceptual design stage, enables simultaneous testing of various cruise ship configurations regarding CO2 emissions using a bottom-up approach with parametric CO2 models. It leverages data-driven models from existing databases or AI-generated data exemplified in a case study on the hotel system of a cruise ship illustrates how parametric design variables influence CO2 emissions, demonstrating a preliminary result of a prescriptive study in collaboration with a major international ship manufacturer

The Consensual Assessment Technique (CAT) is one of the most effective and commonly used design evaluation methods. However, it fails to capture implicit cognitive processes and has mainly been studied in a homogenous design modality. To bridge this gap, the present study investigates the impact of design ideas represented in different modalities (i.e., text-only, sketch-only, text + sketch) on design evaluations for creativity, novelty, and usefulness, and examine human gaze patterns during the evaluation process. Our findings showed that novice raters exhibit higher interrater reliability and greater convergence in visual attention when rating ideas containing sketches compared to text-only design modality, highlighting the value of visual elements in design evaluations.

This paper explores how creative preservation, affected by a regulatory framework, unfolds in the design of complex systems. Based on a case study of the Boeing 737 aircraft, it focuses on the role of grandfather rights, as part of the regulatory framework of aircraft design, as a precursor for creative preservation. The paper analyzes three design decisions related tot the evolving Boeing 737 aircraft models over a period of six decades and highlight the changing logic of creative preservation in relation to technology maturity, increasing complexity of design decisions, and expanded stakeholder involvement. Overall, the paper demonstrates that the management of design heritage is a ‘living system’ and that foundational practices may slowly become ineffective.

Developing methodical approaches, from methods and concepts to algorithms and comprehensive methodologies, requires application-specific expertise and a structured procedure to ensure both workflow efficiency and validity. This contribution introduces a conceptual model for assessing the maturity of methodical approaches through ten predefined readiness levels. By achieving level-specific sub-goals, the model aims to systematize the development process while progressively increasing maturity, ultimately yielding more effective approaches. This novel concept not only supports the structured development of methodical approaches, but also facilitates their comparability and evaluation. The necessity of the proposed concept is substantiated through a systematic literature review, while its functionality is critically evaluated and validated by experts and using multiple examples.

A major cause of diagnostic errors is the underlying complexity caused by patient presentations and the context in which diagnosis is being undertaken. This is especially true for settings like emergency medicine and disease spectrums like infectious diseases. To design artefacts that counter such errors, it is essential to map the factors contributing to diagnostic complexity. However, existing complexity assessment methods in healthcare are limited in scope. Addressing this gap, our work operationalises a complexity estimation tool to identify factors contributing to the diagnostic complexity of 10 infectious disease cases in an emergency medicine setting. Our objective findings are further validated by a strong correlation with the difficulty perceived by attending doctors. The work provides a basis for the design of targeted interventions aiming to mitigate complexity in diagnosis.

The predominant adoption of artificial intelligence (AI) in the design process is constantly evolving with the continuous upgradation of generative AI tools. Current studies emphasised generative AI’s role in individual disciplines, with limited understanding of its use across diverse design disciplines like product, fashion, and UX design. Therefore, the importance of this review is to explore the latest trends in utilisation, commonalities, and differences of generative AI tools and tasks, and AI types across design disciplines. With the assistance of Google Scholar, relevant papers were identified based on alignment with the review’s scope. The study highlights the transformative role of tools like ChatGPT and DALL-E in enhancing creativity, ideation, and decision-making. The outcomes of the review offer insights for future systematic reviews and practical guidance for designers.