This study presents a methodology for leveraging an LLM to generate user-centered recommendations in design for sustainable behavior. A survey of 50 users captured reasonings for evaluating thermostats’ eco-friendliness and sustainable design features. Through in-context learning, GPT-4o learned to take user perspectives for similar evaluations. The model classified 196 thermostats by eco-friendliness and design intervention types—persuasive, decisive, or both. Analysis of user sentiment and ratings of these thermostats’ reviews showed persuasive designs, which offer users behavioral control, received higher satisfaction. GPT-4o extracted features from these classifications to generate design recommendations. This method is a scalable approach for identifying user preferences and informing sustainable design decisions.

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.

The healthcare sector is a large contributor to climate change, due to their size, resource use and extensive use of single-use devices (SUDs). Despite the European Medical Device Regulation (MDR) permitting the resetting of SUDs, healthcare professionals are hesitant and seek evidence-based guidelines. This demonstration study investigates how design engineering can contribute to the feasibility of resetting SUDs that are theoretically suitable for reuse, contributing to the broader discussion on medical device sustainability. The research focuses on the quality evalualtion of reset SUDs through a detailed protocol ensuring that reused devices meet safety and performance standards. Results reveal a discrepancy between the theoretical feasibility of resetting SUD and its actual practicability. This finding highlights the necessity for more practically oriented protocols.

In this paper, I stress the need to broaden the scope of diversity in value-laden ideals of science to include geographic diversity. I argue that egalitarian and normic ideals have conceptual limitations when considering this dimension. While egalitarian frameworks advocate for a placeless science, normic frameworks predominantly locate scientific knowledge within the “Global North,” highlighting the importance of including “non-Western” perspectives from the “Global South.” These limitations have negative and unjust epistemic consequences: they risk perpetuating cultural imperialism, reproducing a colonial epistemic norming of space, and committing epistemic exoticization towards scientific communities in subaltern regions.

There is a growing consensus among philosophers that quantifying value-laden concepts can be epistemically successful and politically legitimate if all value-laden choices in the process of quantification are aligned with stakeholder values. I argue that proponents of this view have failed to argue for its basic premise: successful quantification is sufficiently unconstrained to be achievable along multiple, stakeholder-specific pathways. I then challenge this premise by considering a rare example of successful value-laden quantification in seismology. Seismologists quantified earthquake size precisely by excluding stakeholder values from measure design and testing.

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.

Large-scale spanwise motions in shock wave–turbulent boundary-layer interactions over a $ 25^{\circ }$ compression ramp at Mach 2.95 are investigated using large-eddy simulations. Spectral proper orthogonal decomposition (SPOD) identifies coherent structures characterised by low-frequency features and a large-scale spanwise wavelength of $ O(15\delta _{0})$, where $ \delta _{0}$ is the incoming boundary-layer thickness. The dominant frequency is at least one order of magnitude lower than that of the shock motions. These large-scale spanwise structures are excited near the shock foot and are sustained along the separation shock. Global stability analysis (GSA) is then employed to investigate the potential mechanisms driving these structures. The GSA identifies a stationary three-dimensional (3-D) mode at a wavelength of $ 15\delta _{0}$ with a similar perturbation field, particularly near the separation shock. Good agreement is achieved between the leading SPOD mode and the 3-D GSA mode both qualitatively and quantitatively, which indicates that global instability is primarily responsible for the large-scale spanwise structures surrounding the shock. The reconstructed turbulent separation bubble (TSB) using the 3-D global mode manifests as spanwise undulations, which directly induce the spanwise rippling of the separation shock. Furthermore, the coupled TSB motions in the streamwise and spanwise directions are examined. The TSB oscillates in the streamwise direction while simultaneously exhibiting spanwise undulations. The filtered wall-pressure signals indicate the dominant role of the streamwise motions.

Well-designed products are crucial to a company's business success. Management support is a critical success factor in ensuring that design-related aspects are given appropriate attention during product development. Despite the importance of management, the literature doesn't provide a clear picture of what characterizes a competent manager in product design. This gap impedes competence development and explains why organizations struggle to leverage the benefits of well-designed products. This research aims to address this gap by synthesizing important findings from the literature into a model of managerial competence. The model provides initial insight into the individual competencies managers need to meet their responsibility for good product design in organizations.

The objective of this research is to identify and synthesize metrics to assess virtual prototypes in product design. The metrics are identified from literature and practitioners (novice/experienced designers and design faculty members), and evaluation categories are constituted. The identified metrics and constituted evaluation categories from: (a) literature and practitioners, and (b) across various practitioner groups, are compared. 144 and 29 distinct metrics are identified from literature and practitioners, resulting in 15 and 9 evaluations categories, respectively. The metrics from the practitioners is a subset of the metrics from the literature. The differences between: (a) literature and practitioners, and (b) across various practitioner groups, suggest the need for support to help practitioners choose relevant metrics for their prototyping context from an encompassing list.

Biodesign is an emerging disciplinary field that, in its multifaceted nature, finds in transdisciplinarity a promising pathway to address the complex challenges posed by contemporary scenarios. However, specific methodologies that connect the design mindset with the epistemological framework of scientific methods are still lacking. How can we grow the next generation of biodesigners in this scenario? Transdisciplinary dialogue provides a foundation for merging design thinking with scientific reasoning, leading to the development of methodologies and educational strategies aimed at creating shared languages and codes that promote synergy between design and science. This study presents the results of a methodological evolution—from multi and interdisciplinary approaches to transdisciplinary ones—through a workshop focused on material design, a course designed to train future biodesigners. This workshop engaged students in collaborative material tinkering activities, working side by side with scientists in an active laboratory setting. The study demonstrates that combining a material-driven design approach with scientific methodologies fosters iterative dialogical relationships, ultimately enriching and substantiating the final design outcomes.

As Generative Artificial Intelligence (GenAI) gets integrated in design processes, building trust in these systems is critical for effective human-AI collaboration. This study introduces a framework aimed at translating the abstract concept of trust into practical strategies for design teams, focusing on four trust factors: transparency, accountability, similarity, and performance. We tested the framework’s impact on trust-building and trust learning using a mixed-methods approach, incorporating design tasks and structured workshops involving university students. The results highlight the framework’s ability to enhance participants’ understanding of trust in AI. Insights from this study contribute to advancing educational approaches for embedding trust in AI-driven design, revealing that design activities alone are not enough to impact trust learning.

This paper presents a novel framework for Artificial Creativity (AC) in design, emphasizing the co-development of problem and solution spaces. Grounded in cognitive psychology and design theories, the framework leverages advancements in artificial intelligence (AI), particularly generative AI models, to augment human creativity in design. The study identifies four key design spaces—Solution-Knowledge, Solution-Concept, Problem-Knowledge, and Problem-Concept—and defines operators that automate reasonings within and across these spaces. By enabling simultaneous divergence and convergence of problem and solution spaces, it fosters creativity while balancing novelty and effectiveness. This work bridges AI capabilities with cognitive processes of design creativity, laying a foundation for advancing artificial creativity and human-AI collaboration in design.

Artificial Intelligence (AI) provides a unique opportunity to enhance and augment Model-Based / Systems Engineering (SE and MBSE). Through a systematic literature review, this paper explores current and potential uses of AI in SE across the V-model and analyses barriers of AI adoption in SE/MBSE. The results show that despite a significant potential of AI to enhance SE, several barriers exist, such as unavailability of data, trust and explainability issues, and technical limitations of AI systems. Based on the findings, this paper suggests future research directions, focussing on increasing the availability of high-quality datasets, integrating explainable AI techniques into SE, investigating Human-AI team dynamics, exploring MBSE roles for facilitating AI and how to address technical limitations of current AI models.

As modern technical systems grow in complexity, ensuring the quality of these systems during early development phases becomes more challenging. This is particularly evident in the development of modern passenger vehicles, where non-functional requirements (NFRs) play a critical role in ensuring that a vehicle operates according to specified standards and expectations, especially across different vehicle configurations and environmental conditions. The introduction of Artificial Intelligence (AI) in automotive engineering has transformed the approach to vehicle system design and development. This paper presents a pipeline for analyzing and generating NFRs for vehicle systems using generative AI-based methods. The pipeline categorizes NFRs, explores their interdependencies with vehicle configurations and environmental conditions, and addresses the completeness of NFRs in relation to specific vehicle use cases. The paper focuses on selecting appropriate NFR types for various use cases, taking into account diverse configurations and environmental factors. Examples of NFRs with varying parameters are provided for an electric vehicle under development at a leading car manufacturer, illustrating the benefit as well as the challenges of applying generative AI to automotive engineering.

Developing products with diverse features presents challenges, especially when involving multidisciplinary teams and managing extensive Compliance Requirements (CRs). Ineffective handling of CRs can lead to inconsistencies in subsystem designs or failures. This study introduces an application of Quality Function Deployment to integrate CRs systematically in design lifecycle. The proposed approach utilizes a multi-layered matrix to translate CRs to specific design parameters, cascading requirements to subsystems and engineering directives. A case study on Sunswift Racing, UNSW solar car team, demonstrates the method’s efficacy in embedding compliance in iterative design, enhancing cross-disciplinary collaboration, ensuring adherence to CRs. Findings present a robust traceability model linking CRs to design parameters, offering a replicable solution for multidisciplinary design challenges.

With the increase of service robots, understanding how people perceive their human-likeness and capabilities in use contexts is crucial. Advancements in generative AI offer the potential to create realistic, dynamic video representations of robots in motion. This study introduces an AI-assisted workflow for creating video representations of robots for evaluation studies. As a comparative study, it explores the effect of AI-generated videos on people's perceptions of robot designs in three service contexts. Nine video clips depicting robots in motion were created and presented in an online survey. Videos increased human-likeness perceptions for supermarket robots but had the same effect on restaurant and delivery robots as images. Perceptions of capabilities showed negligible differences between media types. No significant differences in the effectiveness of communication were found.

Designing sustainable technologies is challenging, as established technology is often more cost-effective than new, sustainable options. This study shows how a design-driven approach can advance Soluble Gas Stabilization (SGS) beyond low Technology Readiness Levels. SGS is a CO2-based method extending muscle food shelf life. A CO2 flow chamber prototype, developed from previous simulations and research, identified key parameters and adjustments for improved performance. Initial tests revealed issues such as heat build-up and meeting flow targets but also offered insights for better configurations. This paper illustrates how iterative, hypothesis-driven experimentation links theory and practice by integrating virtual simulations with hands-on prototyping. This workflow supports emerging sustainable technologies progressing from proof-of-concept to industrial-scale demonstration.

The study investigates the integration of artificial intelligence (AI) into the product development process (PDP). It addresses two key research questions: which AI technologies exists to support designers across different phases of the PDP and which specific design activities these technologies enhance. Employing a systematic literature review, the research identifies AI technologies utilised in the design process and categorises them across the various phases of PDP. The findings emphasise a predominant focus on early-stage phases and the support of single activities within the PDP. Notable challenges include the lack of comprehensive end-to-end integration and limited compatibility in later phases. The study underscores the potential of AI while drawing attention to existing gaps in its adoption and the necessity for further research into cross-phase integration.