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.

This study addresses the challenges of applying traditional Life Cycle Assessment (LCA) during early-stage product development by proposing a Streamlined Life Cycle Assessment (SLCA) approach. Traditional LCA, while robust, is often inaccessible due to its complexity, time requirements, and cost, making it impractical for many industries. The developed SLCA approach offers a simplified alternative by leveraging tools like artificial intelligence, 3D modeling, and secondary databases. The SLCA methodology was validated through a case study on an electronic device, demonstrating a 69.77% reduction in input requirements, a 91% decrease in time spent, and an average accuracy of 90.05% compared to traditional LCA. These results highlight the potential of SLCA to enable designers to identify environmental hotspots early in the design process, fostering sustainable product development.

This study highlights the importance of interface design in sustainable product development within a circular economy. By focusing on the end-of-life (EOL) phase, the research emphasizes modular product architectures’ role in improving component separability, reusability, and recyclability. An extended Module Interface Graph (MIG) was developed to assess interface variance, detachability, and material pairings, enabling the identification of critical interfaces that significantly influence EOL outcomes. The approach was successfully applied to a portal milling machine, demonstrating its ability to highlight key areas for design improvements, such as transitioning from non-detachable to standardized, detachable interfaces. This method showcases the potential for early interface considerations to enhance both environmental sustainability and product lifecycle management.

Estimating consumer impressions of a product’s appearance is essential. However, this is not easy because of the variety in consumers’ tastes and differences in how consumers and designers experience design. Multimodal foundation models trained on datasets from the internet could be applicable for the estimation; however, it remains unclear if the models’ tastes are similar to those of consumers or experts like designers. Therefore, we conducted surveys in which consumers and designers rated the appearance of car wheels. In addition, a foundation model estimated the visual impression of the wheels. The model’s ratings were more similar to those provided by designers than consumers. Therefore, the models could have tastes similar to those of experts because the datasets could contain advertisements and reviews written by experts or product owners who have opinions on product appearance.

Incorporation of emotional interactivity into the design framework can help strengthen the connection between user perception and designers intent with product as its medium. The method involved in this qualitative literature review is analysis of journal articles, conference papers and other literary sources. With the help of thematic analysis parallel assessment of journals was done to figure out the main highlights of the themes patterns and theories that stood out. The paper’s main objective is to analyze, role of emotional interactivity in user experience and product design. The study examines and collectivizes the current knowledge of emotional interactivity and its applications in various domains, including sensorial design elements, storytelling and marketing, user personalization, and AI-driven product adaptation and emotional recognition.

Designing products for diverse stakeholders and environments requires understanding contextual factors, as neglecting them often leads to design failures. However, guidance on integrating context during back-end design phases is limited. To address this gap, we developed the Contextual Product Testing (CPT) protocol, which involves testing prototypes in stakeholders’ contexts of use, gathering data through observations and interviews, and analyzing insights based on contextual factor categories. To evaluate the protocol, we conducted a case study using an interactive toy chest prototype that encourages children to clean up after playtime. Results from ten families revealed contextual barriers, enablers, and actionable recommendations. Our findings suggest the protocol offers a structured approach for incorporating context into back-end design, improving products for real-world use.

This paper serves as a template for, and argument to, the engineering design research community to pre-register research studies. Pre-registering allows for a research plan to be validated and results published, no matter the findings. To support pre-registering, we propose a case study to study how individual perspectives and decision-making processes interact as design teams collaborate and reach consensus. We explore how narrative misalignments within a design team—disagreements on the best path forward—are shaped by individual perspectives. Driving requirements, requirements that reflect a designer's prime motivations, are used to shed light on individual priorities. A data collection and analysis plan are introduced to explain how the team will examine how consensus was achieved, which divergent personal interests persist, and how future decision-scenarios might be influenced.

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.

A highly compact microstrip low-pass filter (LPF) integrated, and meander-line-loaded substrate integrated waveguide (SIW) cavity-backed wideband filtenna with multiple radiation nulls (RN) is presented in this paper. A closed-loop quadrilateral slot is removed from the cavity to generate the first RN in the lower edge of the frequency band. Furthermore, an LPF is integrated with the feedline, which creates another RN at the upper edge of the pass band. The incorporation of the meander-line slot in the radiator and elimination of the C-shaped open loop slot from the bottom of the cavity improves the selectivity of filtenna significantly by generating two more RNs at each side of the pass band. The presented filtenna appears to have low profile and compact structure with wideband response of 17% fractional bandwidth (11–13.1 GHz) and 8.56 dBi peak gain in the pass band. In total, four RNs with more than 15 dB out-of-band rejection level and good lower and upper selectivities of 0.65 and 0.05, respectively, are reported, with close agreement between simulation and measurement. These profound properties make the proposed filtenna suitable for short-range radar and satellite communications, modern Radio Frequency (RF) front-end and wireless communication systems.