Design research faces growing challenges from multifaceted developments, which traditional methods and lab settings often struggle to address. New approaches are needed to bridge the gap between controlled lab settings, field studies, and these complexities. Exhibition spaces offer opportunities for dynamic, real-world studies beyond lab-based research’s limitations. This study explores a hybrid ‘exhibition-experiment’ format by examining a design exhibition on biophilic workspace design. Participants visited different design exhibits (experimental conditions) within the experiment while a suite of passive measurement devices measured their emotional and physiological responses. The findings highlight the strengths and limitations of ‘exhibition-experiments’, provide insights into the usage of technology-driven tools, and discuss them as a hybrid approach between lab and field studies.

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 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.

It is necessary to pass on design knowledge through links between product models to efficiently utilise the design knowledge built up throughout a design process. Yet, researchers lack support for deriving new links between product models. Based on the findings from analysing publications that present links, a systematic approach to deriving links between product models in engineering design research is developed and subsequently demonstrated in an illustrative case linking two product models. The approach enables researchers to derive new links between different product models in a systematic and traceable way. This offers the potential to increase the density of known links within the body of product models. Further, this facilitates the integration of previously unlinked product models into design processes and their efficient combination through the passing on of design knowledge.

Most innovation performance measurement approaches focus on ex-post outcome data, leaving decision-makers without timely guidance during the early phases of new product development (NPD). This gap is particularly critical in high-risk, high-regulation industries such as Urban Air Mobility (UAM), where long development cycles, regulatory hurdles, and uncertain user adoption demand real-time, in-process innovation metrics. In this paper, we propose a Desirability-Feasibility-Viability (DFV) framework that links key innovation phases (Discovery, Development, and Commercialization) to leading indicators that track innovation progress before market entry. Using UAM as an illustrative case study, we demonstrate how our framework enables stakeholders to navigate uncertainty, optimize resource allocation, and make data-driven innovation decisions.

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.

Previous studies found ChatGPT-assisted ideation produced lower fluency, flexibility, and originality with shorter ideation sessions. This research hypothesized that a longer 24-minute session would improve ideation outcomes for the ChatGPT-assisted approach and enhance team engagement. Undergraduate students participated in two design workshops: one using a ChatGPT-assisted approach (n=22), the other using only analogue methods (n=17). Results showed that while the analogue group slightly outperformed the ChatGPT group in flexibility and originality, the fluency difference was larger, with the analogue group producing over twice the number of ideas. Evidence suggests team-based ideation behavior has more impact on ideation outcomes. Future research will explore a hybrid individual-to-team approach that combines individual contributions with team collaboration.

Parametric modeling and generative design hold promise for architecture, yet their reliance on scripting and predefined constraints has often discouraged early-stage exploration. This paper proposes a conversational AI framework to address these challenges, integrating ChatGPT into two workflows: user-driven (Revit+Dynamo) and AI-driven (Grasshopper). By transforming natural-language prompts into Python scripts or Grasshopper definitions, designers can iterate on geometry, materials, and forms without extensive coding. AI-based visualization tools such as Veras provide near-instant feedback, accelerating the loop from concept to refinement. Rather than evaluating a single software tool, this exploration highlights collaboration between architect and AI, demonstrating how large language models can augment design intent, expand the parameter space, and adapt to contextual needs.

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.

Bio-inspired designs offer innovative solutions for optimizing heat exchangers, though their complexity often exceeds the capabilities of traditional manufacturing methods. Additive manufacturing (AM) enables intricate geometries with enhanced surface areas for improved heat transfer. This study presents a modular algorithm to integrate internal structures into heat exchanger designs, balancing thermal performance and manufacturability. A case study demonstrates the design, simulation, and production of internal structures, identifying the “Diamond Radial” structure as the optimal choice due to its high R-factor and potential to improve efficiency. Future work includes exploring multi-material components and designs for hydrogen storage and fuel cell applications, paving the way for more efficient, application-specific systems.

The development of interdisciplinary Smart Products involves complex architectures and processes, which results in new challenges like managing heterogeneous and unstructured data causing inefficiencies. Model-Based Systems Engineering (MBSE) addresses these issues through precise system modeling but encounters obstacles like a lack of model reuse and complexity. This paper introduces a novel framework integrating Artificial Intelligence into MBSE to enhance sustainability and circularity by automating model generation and reusing existing system models. Using ontology-based knowledge management and large language models, model creation, interoperability, and decision-making can be enhanced and automated and visualized in real-time. The framework's capabilities and benefits are demonstrated through the instantiation of a wireless charger system example.

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 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.

Research into the foundational theories and management of software design remains limited. A 2010 workshop was convened to explore professional software development practices. The workshop sought to foster collaboration between the software engineering and design communities by examining foundational aspects of software design. Building on that workshop’s objectives, this paper investigates contemporary professional software design practice and its management within organizational contexts. It is informed by findings from three interviews with experienced software design managers. This work addresses an important gap by examining software design management through the lens of design rather than solely from a project management perspective. Additionally, it contributes to the development of a general theory of software design by integrating diverse theoretical frameworks.

Understanding how Shared Mental Models (SMMs) develop within design teams has sparked interest in the design community of decades. But to date, there is still a lack of understanding of the factors that influence the development of these structures. This review examines the literature related to SMMs and the factors that impact collaborative efforts. Aiming to bring these two research fields together, this review proposes a new framework to help researchers better understand how SMMs develop and provide a foundation for new research and empirical evidence to establish the factors that influence the development of SMMs

Retrofitting aircraft cabins is characterized by a large number of documents created and required, most of which are currently processed manually. Engineers need to identify which documents include the information that is required for a specific task. This paper proposes an approach that builds upon a digital knowledge base and moves towards automatically processing the quantity-on-hand documents to reduce the work required to identify the required documents without the labour-intensive creation of the knowledge base in beforehand. After describing the scenario this work faces, comparable approaches and promising techniques are discussed. A process-chain that builds upon these fundamentals is presented, including a selection of feasible techniques and algorithms. Finally, the steps towards an implementation as part of the transformation towards a data-driven value chain are presented.

The humanitarian sector requires innovation to enhance emergency response efficiency and effectiveness. This paper examines the sector’s unique challenges, including complex emergencies, specialized products, diverse actors, and barriers to innovation. To address these, UNHRD has revamped its approach, blending accelerator initiatives with design-driven activities via its Innovation Lab. A task force of academics and experts is developing a tailored workflow integrating product design and business process management to improve decision-making. Efforts like market scouting, innovation contests, and in-house R&D aim to overcome current limitations, fostering collaboration among stakeholders. This approach offers a more adaptive and inclusive innovation ecosystem.

The importance of the circular economy as an alternative to today’s prevailing linear economy is recognised in both industry and research. Product designers are having a major influence on this transition by adapting the characteristics of physical products in the early phases of the product development process. However, most products follow a linear approach and are far from being circular. This paper aims to identify the challenges that product designers face when designing circular products. Building on a developed understanding of related terms in circular product design, an exploratory literature review is conducted. The results help to gain an overview and understanding of the challenges that need to be addressed. Therefore, further research directions are derived to support the transition from linear to circular products in the long term.

The transition to Industry 5.0 (I5.0) marks a shift toward human-centric, sustainable, and resilient manufacturing, leveraging technologies like collaborative robots (cobots) and AR/VR to enhance inclusivity, empowerment, and safety. This study investigates how Learning Factories (LFs) can effectively convey I5.0 principles to students and professionals. A simulated production line using AR/VR allowed participants to interact with virtual cobots, assessing key pillars of safety, inclusivity, and empowerment. A survey was used to assess the impact of this immersive environment on participants' perceptions and unconscious reactions. The findings demonstrate LFs' potential to prepare a workforce that integrates human creativity with technological innovation.

This paper presents an Integrated VDD Approach, formulated to address the lack of, and limitations associated with, work concerning the application of VDD to product families. The focus of the results obtained from the application of the Integrated VDD Approach, and the subsequent discussion, will be on the identification of break points to aid objective decision making early in the design process. Results include the identification of the most valuable common wingspan across three conventionally powered aircraft and the identification of the additional system mass which would render an aggressive electrification strategy to facilitate earlier electrification of an initially conventionally powered aircraft futile in comparison to a nominal electrification strategy.