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.

Students, educators, and professionals find value in industrial design students participating in internships, however, there is currently no approach for evaluating the quality of internships students are participating in. This research addresses the need for a standardized metric to evaluate industrial design internships. During a two-year longitudinal study conducted at three comprehensive universities, data were collected on internship experiences. Using this data, the authors developed a weighted ranking approach, providing a valuable tool to evaluate internships’ quality and relevance. This ranking fills a critical gap, offering unique insights for students, academic programs, and internship providers to assess and enhance internship quality, currently unaddressed by existing tools.

Achieving Net Zero requires designers to have a better understanding of the product use with studies showing user behaviour, cultural norms, seasonality and product interactions concomitantly dictate energy consumption. Data on product use can support data-driven design processes that have been shown to improve the efficiency of existing products. The paper reports a method that generates data for data-driven design processes from non-intrusive load monitoring (NILM) of household energy consumption data. The method produced appliance classification accuracies of 0.9984 while reducing sample size, sampling frequency and machine learning model complexity showing potential for it to be deployed at scale across communities.

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.

Product development is a dynamic, multidisciplinary field shaped by evolving customer demands and the need for individualized products, increasing product variety. Key factors include economic performance, customer satisfaction, and sustainability. Lightweight design drives innovation by enhancing weight-specific performance, optimizing resources, and reducing CO2 emissions, especially in transportation. However, conflicts arise as lightweight design focuses on individual variants, neglecting broader product family implications, while Design for Variety strategies often exclude lightweight design. This study examines the interplay between product variety and lightweight design, proposing a measurement framework to support the development of variant products and their components within product families in the context of lightweight design.

This study aims to detect the ability of professors to distinguish design assignments generated by students with and without using AI. Ten students were recruited to undertake a conceptual design task twice, one with and one without the help of AI. 105 higher-education associate, assistant and full professors from industrial and product design programmes were recruited to assess the generated designs using a 7-point Likert Scale with nine indexes. The results indicate that assessors have moderate ability to distinguish between design assignments of students using AI and those where students did not use AI. Three cues to suggest the risk of the design assignment is made with AI instead of students who did not use AI were identified. By considering the three cues, lecturers distinguish design assignments generated by students with or without AI.

AI is increasingly used for systems and companies are integrating Machine Learning methods as well as Generative AI into modern products. For Systems Engineering this leads to new challenges, for example due to the increasing importance of data quality, data privacy or new legislation. This article highlights key challenges arising from the integration of AI components into technical systems and discusses the impact on classical role models for Systems Engineering. The paper presents results from a literature review as well as a view on how the development of AI-based systems is transforming traditional Systems Engineering from perspective of design teams. New demands on data quality assurance and legal risk management as well as establishing new roles in Systems Engineering are discussed. In addition, theses for shaping the future of Systems Engineering are presented.

Using an original method, we find the algebra of generalised symmetries of a remarkable (1+2)-dimensional ultraparabolic Fokker–Planck equation, which is also called the Kolmogorov equation and is singled out within the entire class of ultraparabolic linear second-order partial differential equations with three independent variables by its wonderful symmetry properties. It turns out that the essential subalgebra of this algebra, which consists of linear generalised symmetries, is generated by the recursion operators associated with the nilradical of the essential Lie invariance algebra of the Kolmogorov equation, and the Casimir operator of the Levi factor of the latter algebra unexpectedly arises in the consideration. We also establish an isomorphism between this algebra and the Lie algebra associated with the second Weyl algebra, which provides a dual perspective for studying their properties. After developing the theoretical background of finding exact solutions of homogeneous linear systems of differential equations using their linear generalised symmetries, we efficiently apply it to the Kolmogorov equation.