The advent of multidisciplinary product development may require a corresponding evolution or adaptation of product development practices within companies. To support this, researchers have developed various groupings of concepts and techniques, such as “toolboxes” or “maps”, which can be assimilated to static databases. Consequently, this article presents a first step towards a community-driven database for the dynamic representation of links between approaches, processes, methods and tools in research documents. Following a comparative analysis of different representations, a preliminary design of a dynamic database is presented using Unified Modeling Language models to define its architecture. A use case diagram paired with screenshots of the dynamic database presents the core functionalities, which include real-time data filtering, visualisation, navigation and modification.

This research aimed to explore the challenges designers face when using asynchronous collaboration methods across different time zones. A literature review revealed a knowledge gap in comparing synchronous and asynchronous collaboration methods and in comparing design students and professional practice. To fill this gap, a study was conducted with a group of engineering design students and practitioners asking them to conduct two design exercises, one synchronously and one asynchronously. The results highlighted unique challenges faced and that experience of design process had little effect on performance when using unfamiliar design tasks. The study contributes new insights and firsthand recommendations for design teams, educators and software developers.

The emergence of new technologies, such as additive manufacturing, and places to promote access to these equipment’s, such as fablabs and makers space, has supported the development of new methodologies based on prototyping. From problem definition to customer validation, prototypes can support the different phases of the innovation process. The biggest challenge being to design the right prototype to address the objective of each phase. Here, we propose to transpose and develop a model from human-computer interaction (Houde & Hill, 1997) (Yang, 2005) to the field of design sciences. The model intends to separate design issues into the “role”, the “look and feel” and the “implementation” axes. Next, we illustrate its potential through the characterization of different prototypes fabricated within the product development process of a tool design to unbend electric pylons.

The usage of micromobility devices is growing to promote sustainable transportation, prompting manufacturers and regulators to enable its safe integration into urban environments. This has created the need for a tool to evaluate such devices. This paper presents the development of a versatile dynamometer design for verification and validation of micromobility devices by emulating real-world conditions while capturing vehicle and battery performance in real-time. A custom Graphical User Interface (GUI) is used to control and configure the system, as well as enabling the user to analyze and save incoming data. Six devices were chosen from distinct categories to collect data and demonstrate the capabilities and modularity of the dynamometer. The results reflect the ability of the dynamometer to be used for standardized testing of various micromobility devices.

This paper presents a hybrid framework that integrates physical and virtual testing to enhance cross-sectional studies in the field of engineering design. The framework addresses the critical challenge that valid inferences in realistic cross-sectional studies are often hampered by the manufacturing constraints of physical prototypes and the limitations of virtual prototypes. Using the example of a snap-fit system, the framework shows how predictive modelling and parametric design enable efficient iterations for building design knowledge. By combining the empirical accuracy of physical testing with the scalability of virtual simulations, the framework reduces iteration times, improves resource efficiency and adapts to different study conditions.

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.

Many developments, such as the Amazon Fire Phone and Microsoft Zune, fail in the market, often due to addressing non-existent needs or providing no added value. Therefore, it is necessary to validate these needs and benefits in the early phases of development projects. One way to do this is by using a product profile that models needs and benefits and makes them accessible for validation. According to the literature, there are nine challenges and four fields of action for developing a design support in validating these product profiles. These fields of action range from stakeholder integration, method selection, and prototyping to the interpretation of results. This publication evaluates and describes the challenges and fields of action derived through expert interviews and literature research. A total of 28 publications were analyzed, and eight expert interviews were conducted.

The underrepresentation of women and gender minorities in certain STEM fields remains a persistent issue, despite decades of research and outreach. Existing research has explored this disparity through lenses such as barriers to participation, whether there are differences in ability or competence, and the misalignment of individual goals with the affordances of STEM fields. This framework introduces a novel perspective by investigating how gender differences may influence the nature of research itself. We propose a coding protocol for systematically analyzing stated goal alignment through the lenses of social relevance, goal type (communal or agentic), and goal function (advancing or fortifying). The protocol was iteratively developed through a coding analysis of research papers from a major design engineering conference and journal (N = 297). The protocol is demonstrated through coding two papers, including one from the International Conference on Engineering Design. Use of this protocol will help researchers demonstrate how published research portrays social relevance and communal focus and thus improve understanding of the participation of women in STEM.

Carbon fiber-reinforced plastics (CFRP) have a great lightweight potential due to their high strength-to-weight ratio. However, new challenges arise due to complex production processes and the large number of design parameters that are subject to variations. This study advances simulation methodologies to address these challenges by modeling the entire CFRP production process while accounting for fiber angle variations at each step. The approach enables prediction of assembly stresses and deformations by utilizing surrogate models, and supports further approaches, such as tolerance optimization and process refinement. Two case studies demonstrate the effectiveness of the method and illustrate its potential to support the optimization of the production process.

While performing design tasks, engineers rely heavily on their knowledge. However, the expanding knowledge space makes it impractical to perform the design tasks without external inputs. This study explores how AI can bridge the knowledge space expansion gap in design. The study introduces the AICED framework implemented as a web tool Pro-Explora, leveraging advanced multi-agent LLM technology to accelerate early-stage design tasks. Pro-Explora generates professional problem definitions, PDS documents, and unique solution concept images within five minutes, maintaining creative flow. Its effectiveness was validated in a real-life project, with outputs deemed highly relevant by experienced designers. The study highlights the AICED framework’s industry implications, addressing required knowledge. This pioneering study opens new avenues for specific LLM applications in engineering design.

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 growing demand for responsible resource use presents a significant challenge in today’s time-, cost-, and quality-driven product development. Therefore, this paper explores integrating creativity techniques into early development phases to achieve innovative, lightweight, and sustainable designs. Using a case study on extending the useful life of a bicycle trailer, a generic framework is introduced, aligning lightweighting and sustainability objectives in the idea generation process. Lessons learned highlight the critical role of the moderator, the importance of an iterative process, and the need for guidelines on method selection. The findings provide actionable recommendations for fostering sustainable innovation in lightweight design and form a basis for further research on adapting creativity techniques to sustainability goals.

This study explores a graph-theoretic approach to assess the alignment of R-imperatives with the integrated product development and supply chain design decisions in the transition toward a circular economy. By modeling interdependencies as a multi-layer graph, our framework quantifies alignment levels, identifies gaps, and provides strategic insights for improving circularity. The methodology employs a hierarchical matrix representation and scenario-based analysis to assess integration performance under different conditions. Numerical results from a case study in the lighting systems industry illustrate the approach’s practical applicability. Findings highlight that repair and remanufacturing exhibit the highest alignment potential, while repurposing shows limited viability. This research offers a structured assessment tool for companies to enhance circularity in supply chain management.

Robust Design (RD) is crucial in product development to ensure that products maintain reliable performance under varying conditions. Design knowledge is fundamental to RD. However, current methods lack a systematic approach to support design engineers in building design knowledge for RD. This paper addresses this gap by introducing a hypothesis-based method for systematically building design knowledge for RD. RD hypotheses are specifically developed for this purpose and are tested through a five-step method. The application of this method is demonstrated in a case study involving a hand-operated coining machine. The results show that the proposed method supports building specific design knowledge through two RD hypotheses. By employing this method, design engineers are systematically supported in making design decisions, leading to more robust product concepts.

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.

Anatomical variations in the upper airway significantly impact the effectiveness of video laryngoscope blades. Existing literature on upper airway dynamics and blade design lacks a comprehensive framework to address these variations. The proposed model uses the extent of mouth opening with three demographic features and three anatomical features in the closed-mouth state to predict the anatomical features in the open-mouth state, which can support the design of a laryngoscope blade. Pearson’s correlation was studied to understand the correlation between the features, and the ordinary least square method was used to develop a model. For all three outputs, a separate model was developed, which gave R-squares of 0.98,0.74 and 0.94. The findings highlight the potential of data-driven approaches to optimize laryngoscope blade designs.

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.

With the increasing amount of data in collaborative engineering research, the need for effective and efficient data management is growing. This paper uses a maturity-based process model to examine the implementation of research data management (RDM) in engineering projects. A process model visualizes a research-supported implementation of RDM and helps researchers evaluate their data management strategies through maturity level assessment. For this approach, activities are assigned to different maturity levels based on a maturity level characteristic providing a differentiated view of the implementation of RDM. An example from an ongoing project shows the application and support of the developed maturity-based process model. The work emphasizes the importance of standardized and quality-assured data management for the success of research projects and their contribution to the scientific community

How we gather individual data to inform product design is changing. In ergonomics, methodologies are rooted in qualitative approaches, providing a holistic approach but can lack objectivity and precision. In this work, we explore novel quantitative techniques, involving machine vision and muscle sensing, to create personalized data dashboards that enrich qualitative practices in a mixed-method design. We conducted a pilot study (n=10), evaluating participants’ motion in a simple ergonomic task, followed by interviews discussing the dashboards. A thematic analysis showed that all participants agreed the dashboards affirmed their experience. Furthermore, the order of data presentation influenced their language, affecting subjectivity and specificity. This study highlights participants’ roles as stakeholders, underscoring the need for their engagement to achieve meaningful design outcomes.

This paper presents a motion-based taxonomy for classifying lattice structures in additive manufacturing (AM) based on their geometric suitability for linear, oscillating, reciprocating, and rotary motions. While existing classification frameworks primarily focus on static load-bearing performance, this study develops a geometry-driven taxonomy, classifying 51 lattice variations based on how tessellation patterns and wall thickness influence motion-driven deformation. The taxonomy provides a framework independent of materials, aiding the selection of lattices for compliant structures, and energy-absorbing applications, by isolating geometric tessellations to assess their role in dynamic deformation and motion suitability. This approach links lattice geometry to motion-driven behaviour, offering a predictive framework for AM design while emphasising its role in motion applications.