Technology platforms spanning several scientific and technological fields hold great promise, both as future innovative tools for industry and as future experimental tools for academia. However, some of their characteristics are also still unknown and need to be designed. A classical approach to initiate their evolution dynamics is to seek funding for a subsequent design project. Using a single case study, we show that a much less costly approach is possible: adding training to the platform can play a central role in increasing the intensity of its use, with both scientific and industrial impacts. Yet, this approach requires that the training knowledge enables the exchange of ‘independent knowledge’ between platform designers and users: this demanding condition requires further research to characterise this promising training model which we propose to call “double impact training”.

Creativity is a requirement for excellence in product engineering. Despite its important role, the concept of creativity in engineering is challenging to articulate and define, both for practitioners and researchers. What makes technical creativity unique? Existing definitions often fall short of capturing the essence of creativity in technical contexts. The process of defining technical creativity is performed iteratively. In product engineering, creativity is not an abstract concept but a practical necessity, requiring motivation, imagination, expertise and experience. Therefore, two workshops with product engineers were held and the results were used to refine the definition. A shared understanding of technical creativity, that can be applied in daily engineering practice is created, enhancing both research and practical outcomes in product engineering.

This systematic review examines how generative design enhances communication and collaboration in multidisciplinary engineering teams. Using the PRISMA framework and CASP evaluation program, we analyzed 1,105 sources to assess its role in improving workflows, facilitating collaboration, and reducing communication gaps through CAD, algorithmic modeling, and AI-driven platforms. The findings show that generative design supports teamwork, optimizes design processes, and strengthens interdisciplinary collaboration. While widely used in architecture, aerospace, and automotive industries, its adoption in product design remains limited, presenting opportunities for further research and innovation. These insights contribute to a better understanding of how generative design can bridge communication barriers in engineering projects.

This paper provides a design solution to the existing problem of using eye trackers for large screens. Traditional eye trackers are limited to commercial and smaller-sized screens. However, as larger screens become increasingly popular and essential for various tasks, their impact needs further investigation in user performance and behavioral studies. This work introduces a design approach for adjustable guide rail system to make moving an eye tracker along with the user's head position possible. The testing results showcase robust, accurate and functions under varying real-world conditions, making it ideal for Human-Computer Interaction and User Experience Research. The Guide Rail design employed by this system is easy to manufacture and incorporates 3D printed parts making it easily reproducible and open for customization.

Robust Design is essential for developing high-quality products by minimizing their sensitivity to variation and uncertainties from diverse sources. Despite the wide range of approaches from industry and research, it faces challenges due to their siloed usage. Motivated to gain a better understanding of these challenges and to derive implications for how to overcome them, this paper discusses different viewpoints on Robust Design, taking into account different life cycle phases, domains, and system levels. This highlights the challenges of enhanced product complexity and shifting focuses, for instance, when it comes to cyber-physical systems or sustainable design, and the need for collaboration. Accordingly, the vision of a collaborative approach to Robust Design in a team is presented, in which the actors’ strengths are combined to further increase efficiency and significance.

This research is a first of its kind, building an understanding of the opinions of industry professionals on the imminent AI revolution. Semi-structured interviews with eight experienced engineers from a range of industries were conducted. Transcripts of interviews were coded revealing engineering practitioner’s understanding of, experience with, and vision for the use of AI technologies. The significance of the outcomes reveals the challenges industry face in realising an AI-driven design future and the actionable support that researchers and educators can provide to achieve this future.

As society and the field of engineering evolves, it is necessary for engineering tools to evolve as well. Through a co-design approach, this work explores the re-design of Pugh’s Product Design Specifications tool for engineering design courses to increase scaffolding of the tool for student learning and incorporate societal implications drawing upon design justice. This re-design was conducted in collaboration with Elizabethtown College faculty members, instructors and students. This paper details the iterative co-design process, showcasing the evolution of the tool that culminated in the latest iteration of the re-designed PDS tool. We conclude with a reflection on this co-design process and recommendations for evolving other engineering design tools to incorporate social justice concepts.

The objective of this research is to explore the use of publicly available recall data from the National Highway Transportation Safety Administration to extract Failure Modes and Effects Analysis data. This large data set was analysed using a Large Language Model chatbot. To assess the usefulness of priming the chatbot with this data, the chatbot was also asked to generate data without priming it with the recall data. This was performed on two specific products. It was found that primed-chatbot results were more specific and used technical terminology appropriate to the product being analysed. The proposed approach can be used by designers in the forward design process during new product development. The proposed approach provides designers with insight into potential failures, the associated consequences, their severity, and root causes as well.

To specify the solution principles of a design, a material selection should be performed already in the concept phase. Based on the design constraints, inappropriate materials are removed using an attribute filter. Brittle materials are often removed using fracture toughness attribute limits, but this does not take into account the strength specific stress level and incorrectly excludes entire classes of materials. We propose a novel filtering method to account for brittle failure in material selection. Based on linear elastic fracture mechanics, we establish a relationship that correctly describes the transition between brittle and ductile materials. Representing the proposed filter on an Ashby plot, we evaluate its effect on the further material selection process. Additionally, we show how different defect sizes in the materials can be incorporated into the filtering process.

Facing increasingly dynamic market environments and global challenges such as climate change and resource scarcity, companies are under constant pressure to innovate and remain competitive. As technology is a key enabler, companies need to understand the drivers of technological change. Technology Foresight systematically identifies and analyzes emerging technologies to support engineering design decisions. However, the growing volume of data is outpacing manual processing capabilities. This research explores the integration of Generative AI to enhance Technology Foresight by automating technology analysis and information synthesis. This paper presents a comprehensive problem analysis, reviews existing solutions, and proposes a framework that demonstrates the potential of Large Language Models combined with a Retrieval Augmented Generation architecture to transform Technology Foresight.

Visual-Language (VL) models offer potential for advancing Engineering Design (ED) by integrating text and visuals from technical documents. We review VL applications across ED phases, highlighting three key challenges: (i) understanding how functional and structural information is complementarily expressed by text and images, (ii) creating large-scale multimodal design datasets and (iii) improving VL models’ ability to represent ED knowledge. A dataset of 1.5 million text-image pairs and an evaluation dataset for cross-modal information retrieval were developed using patents. By Fine-tuning and testing the CLIP base model on these datasets, we identified significant limitations in VL models’ capacity to capture fine-grained technical details required for precision-driven ED tasks. Based on these findings, we propose future research directions to advance VL models for ED applications.

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 investigates the integration of Large Language Models with the TRIZ to improve problem solving and innovation in industrial product development. By combining the structured problem-solving framework of TRIZ with LLMs to process large amounts of data and generate ideas, this hybrid approach seeks to overcome the limitations of traditional TRIZ and optimize solution generation. In a case study conducted in an industrial setting, the effectiveness of this integration was investigated by comparing team-generated solutions with those derived using LLMs and TRIZ-enhanced LLMs. The results show that while LLMs accelerate idea generation and provide practical solutions, the additional structure of TRIZ can provide unique insights, however depending on the application context.

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.

Gradually transforming abstract, conceptual ideas into physical assemblies is seen as one of the key competences of design engineers. Despite the general recognition of the Embodiment Design task as essential phase of every development process, a general methodical support seems either not available or, at least, not influential. Instead, Embodiment Design is often considered an expert task. In this context, our paper offers a discussion of embodiment design from a design methodology perspective. Drawing from a review of relevant literature, we explain why embodiment might be better understood as the representation of the physical artifact rather than a design phase, provide properties and characteristics of good embodiment solution, and give initial guidance for the transition from the creative exploration of concepts to the actual search for satisfactory, or even optimal, embodiments.

Generative artificial intelligence (GenAI) has the potential to further revolutionize Computer-Aided Design (CAD) by recognizing patterns, making predictions, and generating automated design suggestions. This paper presents a systematic literature review that examines the current state of research on the use of GenAI in CAD-based product development. With a focus on 3D modelling, it provides an overview of current approaches, most used datasets and commonly used AI models. Four application areas where GenAI can enhance CAD were derived: Design generation, Design reconstruction, Design retrieval, and Design modification. In total, 47 papers were selected, analysed and categorised.

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.

This paper investigates the effectiveness of machine learning models in predicting customer-relevant functional attributes of vehicles based on selected design variables, using a limited automobile market dataset. By comparing machine learning algorithms such as Support Vector Regression, k-Nearest Neighbour Regression, and Lasso Regression, the study evaluates the models’ predictive accuracy and their potential application in automotive design. The findings highlight both the opportunities and limitations of these methods, emphasising their capacity to support data-driven decision-making despite constraints posed by dataset size, as encountered in real-world, early-stage automotive platform strategies.

This study explores the role of ChatGPT in the completeness of collaborative computer-aided design (CAD) tasks requiring varying types of engineering knowledge. In the experiment involving 22 pairs of mechanical engineering students, three different collaborative CAD tasks were undertaken with and without ChatGPT support. The findings indicate that ChatGPT support hinders completeness in collaborative CAD-specific tasks reliant on CAD knowledge but demonstrates limited potential in assisting open-ended tasks requiring domain-specific engineering expertise. While ChatGPT mitigates task-specific challenges by providing general engineering knowledge, it fails to improve overall task completeness. The results underscore the complementary role of AI and human knowledge.

In this paper, we explore the relationship between divergent thinking and stakeholder identification on 15 student engineering design teams. We examine the relationship between fluency, originality, flexibility, and elaboration on the Alternate Uses Task (AUT), a common measure of divergent thinking, and in stakeholder identification. We find fluency and originality to be positively and statistically significantly correlated between the AUT and stakeholder identification task. Flexibility was positively correlated and elaboration was negatively correlated; both lacked statistical significance. Our results suggest that divergent thinking and stakeholder identification may be correlated, and leveraging exercises to improve divergent thinking may also help improve stakeholder identification. Future work can continue to explore this relationship with larger sample sizes and additional tasks.