This paper provides a design solution to the existing problem of using eye trackers for large screens. Traditional eye trackers are limited to commercial and smaller-sized screens. However, as larger screens become increasingly popular and essential for various tasks, their impact needs further investigation in user performance and behavioral studies. This work introduces a design approach for adjustable guide rail system to make moving an eye tracker along with the user's head position possible. The testing results showcase robust, accurate and functions under varying real-world conditions, making it ideal for Human-Computer Interaction and User Experience Research. The Guide Rail design employed by this system is easy to manufacture and incorporates 3D printed parts making it easily reproducible and open for customization.

This paper presents a case study within a small manufacturing company, engaging in the early phases of co-designing Mixed Reality assembly instructions. Using generative tools through two co-design workshops, we engaged the participants in reflecting, visualising and defining their processes, needs, challenges and future ways of working in relation to new technological applications. We gained insights into the participants’ current practices and identified areas where new technologies could improve these practices. We co-designed a lock assembly instruction paper prototype to use as support for future MR development, focused on their apprenticeship training. We also uncovered other areas of technological implementation, setting the framework for co-designing a customised production system.

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.

Today, Manufacturing companies are adopting a servitization strategy and Product-Service System model to enhance value and remain competitive. Often, this transition also means to embrace a System-of-Systems (SoS) perspective. Concurrently, companies face challenges with volatile, uncertain, complex, and ambiguous (VUCA) environments. One way to tackle VUCA is to utilize simulation modeling. However, developing SoS simulations can be complex and cumbersome. This paper extracts lessons learned from six case studies to identify effective and ineffective practices in developing simulation models. The analysis has led to nine design principles for more effective simulation modeling. Furthermore, the paper explores simulation techniques for modeling SoS and discusses effective VUCA management. Finally, the paper proposes four future research directions to advance SoS simulation research.

This study explores the impact of new work practices on product development in an Engineering Simulator by comparing traditional practices with new ways of working in a compressed product development process. By introducing flexibility, digital tools, and autonomy, the study highlights improvements in individual productivity and innovation. For example, teams employing new work practices developed their first prototypes 30% faster than control groups. However, challenges in communication and team dynamics emerge, underscoring the need for structured support systems. The findings further suggest that while these modern practices foster creativity and efficiency, successful implementation at the organizational level requires balancing autonomy with clear guidelines and effective management. This study provides actionable insights for adapting new work methods to engineering environments.

Managing high-variant product portfolios effectively is a crucial competitive advantage in offering mass customized products on saturated markets. Association Rule Mining (ARM) is a field of data mining determining frequent itemsets from historic transactions and deriving patterns of conclusion. This paper introduces a new approach to transfer ARM to feature-based configuration e.g. in the German automotive industry. Combined, existing apriori product knowledge is used in constraints to effectively lowering runtime by reducing the number of candidate-sets through introduction of a Boolean satisfiability check. For an efficient implementation, three different Apriori algorithms are tested and benchmarked on a generic dataset for different parameters. Results show a significant improvement in using SAT-based pre-screening while efficiency of the implementation depends on the given example.

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.

The properties of the external environment such as colour, light, sound and scent, have been shown to influence the emotional responses of the people in those spaces. However, these findings are typically drawn from studies using stimuli designed by researchers. It remains unclear whether workspace designers can intentionally elicit specific emotional responses in the occupants of those spaces. To address this, we evaluate two workspaces designed by students to ‘activate’ and ‘relax’ their occupants. The spaces were used as stimuli in a controlled experiment conducted during a design exhibition. Self-report measures of emotions showed that the activating room energised its occupants and the relaxing room both calmed and reduced the tension perceived by its occupants. Future analyses will determine whether physiological and behavioural measures are consistent with these findings

This study proposed a framework to visualize research trends and create methods to forecast future directions in the design research methodology field from 2018 to 2022. A case study is conducted using a dataset of abstracts from conference proceedings included in the American Society of Mechanical Engineers (ASME) International Design Theory and Methodology Conference track from 2018 to 2022. The proposed method involves extracting keywords from research articles, transforming them into vectors, determining the similarity between keyword pairs to form a keyword network, and constructing a Sankey diagram to show the topic evolution pathways. The resulting Sankey diagrams provide insight into relationships between research topics.

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 transition to renewable energy and the urgent need to reduce greenhouse gas emissions highlight green hydrogen’s role in decarbonizing various sectors. To address the increasing demand, the research initiative H2Giga FertiRob focuses on automating the production of hydrogen electrolyzers, emphasizing PEM stack assembly. Existing stack designs are often incompatible with automation and hinder scalable production. This paper introduces an adapted Design for Automation approach for PEM stacks. Through the evaluation of a reference stack, key design limitations are identified, leading to the development of an optimized stack with reduced part diversity, improved handling, and enhanced automation compatibility. The methodology provides a systematic framework to advance the automated production of PEM stacks, supporting the scalability of green hydrogen in the global energy transition.

Generative artificial intelligence (GenAI) has the potential to further revolutionize Computer-Aided Design (CAD) by recognizing patterns, making predictions, and generating automated design suggestions. This paper presents a systematic literature review that examines the current state of research on the use of GenAI in CAD-based product development. With a focus on 3D modelling, it provides an overview of current approaches, most used datasets and commonly used AI models. Four application areas where GenAI can enhance CAD were derived: Design generation, Design reconstruction, Design retrieval, and Design modification. In total, 47 papers were selected, analysed and categorised.

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.

Large Language Models offer a novel approach with low barriers to entry to potentially improve knowledge transfer in product development. After identifying knowledge barriers from literature that are potentially addressable through LLM-based applications, we analyze two GDPR-compliant LLM applications - ChatGPT Enterprise and Langdock - examining their key features: assistants and chatbots for both, and prompt libraries and LLM-based file search for Langdock. Then, we evaluate each feature’s potential to mitigate each barrier. Our findings show that assistants and chatbots provide wide-ranging support across many barriers, whereas prompt libraries and file search deliver targeted solutions for a narrower set of specific challenges. Given the numerous influencing factors and the rapidly evolving field of LLMs, the study concludes with a research agenda to validate the theoretical findings.

Substantial engineering efforts are dedicated to reducing injury risks in crash scenarios during the development of new vehicles. This is achieved by performing crash simulations to optimize the nonlinear behavior of systems. However, the complexity makes their behavior difficult and time-consuming for engineers to understand. To reduce the analysis time, this study introduces a modular framework combining Explainable Artificial Intelligence and Large Language Models (LLM). Shapley Additive Explanation values allow for simulation-wise feature importance attribution and generate a data-driven understanding. An LLM assists by making result data interactively accessible and supports technical report generation. Validated through a real-world vehicle side crash optimization use case, the framework demonstrates enhanced and accessible insights into system behavior within virtual engineering.

Products need to be developed faster and more efficiently, which is why companies are seeking to leverage the benefits of digitalization. A current trend is the digital twin (DT), which offers many advantages but also involves high development efforts. Research has addressed the use of the DT along the product life cycle (PLC) to compensate for the development effort, but these approaches are often imprecise and not directly applicable in industry. This paper therefore describes how the individual components of the DT can be utilized along the PLC beyond the manufacturing and use phase with a focus on product design. The resulting framework is then illustrated using a case study of a product service system. This article aims to facilitate the use of the DT in industry to improve product design across product generations.

The environmental impacts generated by manufacturing processes have become a concern, as underlined by regulation controls. Studies tend to focus on optimization of the processes through process parameter refinement to try to reduce energy consumption and raw material consumption. However, a thorough assessment of the building of a component linked to its use should be performed to help decision making. The focus of this paper is to define a methodology that helps the choice of the process parameters since the first design steps, by assessing this choice on the mechanical properties and thus the global environmental impact of the manufactured component. To do so, a case study is applied to a given additive manufacturing technology combining metal injection molding and fused filament fabrication. This combination is part of the additive manufacturing processes involving material extrusion.

This paper presents a systematic method and coding scheme to convert concept maps into bi-partite graphs that can be computationally evaluated for topological complexity measurements. The coding scheme is focused on splitting concepts with multiple elements embedded and linking these objectively. The guidance for this is established and the method presented with examples. The motivation for this work is to establish a means to objectively compare concept maps generated by individuals at the beginning and the end of an intervention to measure the impact of the intervention. The reliability of the coding scheme is presented in separate work.

This study explores the role of ChatGPT in the completeness of collaborative computer-aided design (CAD) tasks requiring varying types of engineering knowledge. In the experiment involving 22 pairs of mechanical engineering students, three different collaborative CAD tasks were undertaken with and without ChatGPT support. The findings indicate that ChatGPT support hinders completeness in collaborative CAD-specific tasks reliant on CAD knowledge but demonstrates limited potential in assisting open-ended tasks requiring domain-specific engineering expertise. While ChatGPT mitigates task-specific challenges by providing general engineering knowledge, it fails to improve overall task completeness. The results underscore the complementary role of AI and human knowledge.

Prototyping is an important component of the engineering design process and has become a frequently studied topic in engineering education. The iterative strategy of creating prototypes, where a single design is refined with repeated improvements, is widely taught and considered to be the default approach to prototyping. However, research has shown that a parallel approach to prototyping, where multiple concepts are tested simultaneously, has potential benefits when exploring a complex design space. Recent studies on parallel prototyping in first-year engineering classrooms have shown that students required to use a parallel strategy produced higher performing final designs than students who used an iterative strategy. This work places the parallel and iterative prototyping strategies in a typical classroom setting where first-year engineering students have control over their strategy.