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.

The authors investigated the prototyping and iteration of three prototype workflows in an operational engineering organization. Data were collected from an ethnographic field study that included observations, interviews, and participatory design workshops. The data were triangulated and synthesized thematically, yielding the following key findings: (1) The value of having an ethnographic field study in improving engineering teams' prototyping (2) Prototyping digital engineering capabilities in realistic operational settings offers enhanced opportunities for buy-in and technology infusion (3) Experienced professionals identified and rtined new use cases through prototyped workflows. The findings and the context-rich details from the field study have been instrumental in furthering digital engineering applied research and in defining follow-on efforts to advance engineering practice.

A roadmap for advancing sustainable and circular designs within the automotive industry is proposed. The emphasis is on the critical role of collaborative ecosystems following the increased transparency and traceability underway in regulations. Emerging Digital Product Passports are central means in Europe’s Green Deal and expects to drive transformation of practices in the automotive ecosystem. The study, conducted by researchers in collaboration with a global truck manufacturer, identifies key areas for action, including data quality, stakeholder value, and communication strategies, to facilitate the circular and sustainable transformation. The vision and actions proposed were refined in workshops with automotive suppliers and service providers. By addressing these challenges, the automotive industry can leverage from data accessibility and accelerate its shift towards sustainability.

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.

While prototype testing with stakeholders is key for valuable feedback in iterative design, there is limited research on how novice designers, who lack the relevant experience, solicit meaningful feedback. This paper analyzes 30 prototype testing sessions from five student design teams to understand how novices structure their testing time by identifying and reporting the instances of testing interactions and types of questions within different contexts. Initial findings show that novices effectively set up testing, engage in active listening, and ask more close-ended follow-up questions. However, they rarely conclude sessions, seek stakeholder questions, and use fewer leading questions in later testing sessions. This preliminary understanding highlights opportunities to strengthen novices’ skills in prototype testing and how testing approaches affect stakeholder feedback quality.

Digital Twins are digital representations of products or product-service systems comprising a Digital Master, which consists of product description models, and a Digital Shadow, which encompasses data collected throughout the product’s life cycle. To create a Digital Twin, the Digital Master and Digital Shadow must be interlinked. The Digital Master, Digital Shadow, and thus their twinning can vary in complexity and analytical capabilities. This paper introduces a systematic description of six twinning levels ranging from simple data exchange based on generic models to more complex forms targeting model parameter and Digital Twin goal optimization. The example of a valve is used for illustration. The presented description aids in understanding the potential of Digital Twins and serves as a guide to select appropriate twinning levels based on specific product requirements and use cases.

In this article, I consider two arguments concerning the status of holobionts as evolutionary individuals—one rejects their status by privileging the “stability of lineages” and the other supports their status by privileging the “stability of traits.” I argue that the tension between these two arguments arises from two fundamentally different accounts of natural selection. I suggest that each account of selection corresponds to a unique account of evolutionary individuality. This strategy entitles us to a modest pluralism: Holobionts are evolutionary individuals on one account of selection but not on the other.

Pruning and nutrient supply after pruning are crucial to restore growth and productivity of old, unproductive coffee trees. The effect of pruning type (stumping, heavy pruning and light pruning) and fertiliser rate (100, 140, 180 and 220 g nitrogen, phosphorus and sulphur (NPS) mixed fertiliser per tree per year) on coffee yield and yield components and fertiliser agronomic efficiency (AE) was studied in southwest Ethiopia to identify the best pruning type and fertiliser rate combination for high crop productivity and AE. The experiment was conducted in a split-plot design with three replicates, where pruning type was the whole-plot factor and fertiliser rate was the subplot factor. Both main and interaction effects of pruning type and fertiliser rate on response variables were significant. Stumping and heavy pruning showed a much higher number of primary branches and fruiting nodes per tree than did light pruning. The 100 g fertiliser rate showed a significantly higher number of verticals and fruiting nodes per tree, yield and AE than did the other rates. Besides, the combination of heavy pruning and 100 g, stumping and 220 g, and stumping and 100 g provided a much higher number of fruiting nodes per tree, yield and AE; number of fruiting nodes per tree, canopy diameter and yield; and yield and AE, respectively than others. These findings show the importance of stumping and heavy pruning each combined with 100 g NPS fertiliser for renewing coffee productivity and maximizing AE in the study area.

Using data from a large-scale behavioral experiment in Beijing, we investigate how social efficiency orientation may relate to rice culture proxied by the rice farming ratio in the subject’s birth province. We find that the observed behavior in several behavioral games that enhances social efficiency is positively associated with the rice farming ratio. This is corroborated by a further analysis of data related to giving help from the China Family Panel Studies. The overall finding supports our hypothesis that rice culture fosters the individual’s intrinsic preference toward greater social efficiency.

Additive manufacturing (AM) enables the creation of complex internal geometries, including cooling channels. Yet, the impact of AM-induced surface roughness on their fluid dynamics remains underexplored. The goal of this study is to provide insight into the effects of surface roughness on the fluid dynamics of AM channels. A parametric surface roughness model and computational fluid dynamics (CFD) simulations were employed to examine three representative AM channel cross-sections: diamond, droplet, and circular. The findings indicate that diamond profiles result in higher pressure losses and turbulence intensity compared to the other cross-sections. In contrast, droplet profiles exhibit lower pressure losses and turbulence intensity compared to diamond profiles, while circular channels remain optimal in non-overhang areas.

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.