Design education prepares novice designers to solve complex and challenging problems requiring diverse skill sets and an interdisciplinary approach. Hackathons, for example, offer a hands-on, collaborative learning approach in a limited time frame to gain practical experience and develop problem-solving skills quickly. They enable collaboration, prototyping and testing among interdisciplinary teams. Typically, hackathons strongly focus on the solution, assuming that this will support learning. However, building the best product and achieving a strong learning effect may not be related. This paper presents the results of an empirical study that examines the relationship between product quality, learning effect and effort spent in an academic 2-week hackathon. Thirty teams identified user problems in this course and developed hardware and mechatronic products. This study collected the following data: (1) effort spent during the hackathon through task tracking, (2) learning effect through self-assessment by the participants and (3) product quality after the hackathon by an external jury. The study found that the team effort spent has a statistically significant but moderate correlation with product quality. The correlation between product quality and learning effect is statistically insignificant, suggesting that for this setting, there is no relevant association.

The ongoing servitization journey of the manufacturing industries instills a through-life perspective of value, where a combination of products and services is delivered to meet expectations. Often described as a product-service system (PSS), these systems are poised with many complexity aspects, introducing uncertainties during the design phase. Incorporating changeability is one of the known strategies to deal with such uncertainties, where the system changes in the face of uncertainty to sustain value, thereby achieving value robustness. While the theme of dealing with multiple uncertainties has been discussed since the inception of PSS, changeability is still poorly addressed. To bridge this gap, an integrative literature review is performed to outline various complexities aspects and their link to uncertainty from a PSS perspective. Also, the state-of-the-art approach to achieving value robustness is presented via changeability incorporation. Subsequently, a reference framework is proposed to guide decision-makers in changeability incorporation in PSS, especially during the early design stages.

Design engineering education is increasingly challenge-based, which requires educators to form cohesive student teams capable of delivering desired outcomes while fostering learning and collaboration. An example is an international network in which students from different global universities collaborate. Student teams work on researching the problem space, re-framing their challenge and producing multiple prototypes. The challenge for the teaching teams is to be able to form multiple cohesive teams out of a pre-selected group of highly motivated students. Because of the exclusive nature of this educational program, it is a suitable case study for exploring student design team formation practices. The aim is to identify the methods, tools, theoretical underpinnings, challenges and limitations of student team formation. We interviewed teachers from seven universities about their practices. The interviewees had several years of experience in team building. The interviews were analyzed to contrast practices across universities as well as to the team formation literature. Our findings show that mixed methods that combine self-assessments and observer-assessment methods are the preferred means of forming teams. Our findings also show that current practices have evolved over time through trial and error, and are only partially grounded in different literatures and not necessarily in team formation literature.

The use of system models within model-based systems engineering (MBSE) is essential for improved communication or system documentation. Previous publications have investigated further reuse of these system models, for example, transforming them directly into discipline-specific models for reuse. The authors refer to this as the term “Utilization” of system models. It aims the compensation of modelling efforts and a further integration of linked models within MBSE. Motivated by a lack of common understanding of this term, a systematic literature review of the state of the art is presented. With this systematic overview, a definition and classification system for different use cases and system life cycle stages are created. These are key results to support engineers and researchers in adopting existing or discovering new utilization approaches. This supports the mission of advanced systems engineering and aims the identification of new research directions coming along with SysML v2 and the advanced systems engineering methods.

Assistive technology (AT) is any artefact that enables participation in activities usually limited by disability. Frequently, AT suffers from poor design engagement and utilisation. Moreover, up to 30% of all AT is abandoned within a year, negatively impacting users. This presents an ongoing challenge for occupational therapists (OTs) who work with assistive technologies. A literature review was conducted using a Preferred Reporting Items for Systematic Reviews and Meta-Analysis protocol to understand this issue and its implications for the design community. This study explores current themes of AT abandonment and the role of OT within the lens of design thinking. Studies, including design intervention in AT, were subsequently highlighted. This led to comparing this literature with more traditional health literature, exploring the potential enablers and barriers for design in engaging with AT. This evidenced the benefits of collaboration between design and OT disciplines to improve the product and reduce abandonment issues.

Design, like any social activity, greatly depends on human relationships for efficiency and sustainability. Collaborative design (co-design) in particular relies on strong interactions between members, as ideas and concepts become shared, going from personal (creation) to interpersonal (co-creation). There is, then, a need to understand how interpersonal factors influence interactions in co-design, and this understanding can be achieved by using the insights gleaned from research on intersubjectivity, the field of social interactions. This literature study was conducted using a systematic literature review to identify and classify the different methods used to measure intersubjectivity and see how this knowledge could explain the influence of interpersonal factors on interactions in co-design. The review identified 66 methods, out of which 4 main categories were determined. Furthermore, 115 articles were analysed and systematized in an online database, leading to a new understanding of the role of interpersonal factors in measuring the interactive levels in co-design. They reveal a positive correlation, where a rising level of interactivity is made possible by the formation and maintenance of co-creation, leading to a state of resonance where the experiences of individuals are closely related. This paper presents a state-of-the-art report on trends in the study of intersubjectivity through interpersonal factors and proposes some directions for designers and researchers interested in taking these factors into consideration for their next co-design situation.

Research Through Design (RTD) needs to reconsider the meaning of “designing” in the research process of “through design.” We propose Research Through Co-design (RTC) as a new application of Control System Theory (CST) that includes a research problem assigned to a co-design process in RTD. It embeds the participatory paradigm through collaborative design practice and makes the research a collaborative process for learning from all the participants. To sustain the RTC theory, we present a cognitive model of RTC. It is a “model for” – rather than a “model of” – describing how the co-design, as a neural network process, works through its nodes’ collaboration to find co-designed solutions and the research answer. Diversity increases as non-experts and non-designers with different backgrounds participate. This is valuable for the RTC learning system. The discussions highlight the possibility of considering (i) the RTC model as useful for describing a robust RTD process through CST; (ii) RTC as a cognitive model for explaining the value of co-design in research processes; and (iii) RTC as a strategy for applying the participative paradigm in formal research. Finally, new insights and implications are highlighted, including using RTC as a predictive tool through artificial intelligence.

Ilmenau is a relatively small town in a beautiful landscape, close to the centre of Germany. Since 1894 it has been the home of a Technical College which after World War II and through some permutations became today’s Technische Universität Ilmenau. For 70 years the university has contributed to Design Science. It is interesting to note that the fundamentals were developed in practice, at the Carl Zeiss company in Jena; it was only later that the new ideas were further developed for academic research and teaching in Ilmenau. The origins at Zeiss Jena still account for the main application area at Technische Universität Ilmenau today: Precision Engineering which, in addition to mechanical, has always included electric, electronic, control, software, and even optical components (“mechatronics” before the term was coined). This article – written by three (out of four in total) of the professors who were and are, respectively, in charge over almost 50 out of the 70 years – tells the story of Design Science in Ilmenau: background, beginnings, development, contributions to research, teaching, and transfer to industry. As Ilmenau was situated in the German Democratic Republic (“East Germany”) between 1949 and 1990, the story is not free of political and societal implications, some of them quite surprising.

Improving designers’ ability to identify manufacturing constraints during design can help reduce the time and cost involved in the development of new products. Different design for additive manufacturing (DfAM) tools exist, but the design outcomes produced using such tools are often evaluated without comparison to existing tools. This study addresses the research gap by directly comparing design performance using two design support tools: a worksheet listing DfAM principles and a manufacturability analysis software tool that analyzes compliance with the same principles. In a randomized-controlled study, 49 nonexpert designers completed a design task to improve the manufacturability of a 3D-printed part using either the software tool or the worksheet tool. In this study, design outcome data (creativity and manufacturability) and design process data (task load and time taken) were measured. We identified statistically significant differences in the number of manufacturability violations in the software and worksheet groups and the creativity of the designs with novel build orientations. Results demonstrated limitations associated with lists of principles and highlighted the potential of software in promoting creativity by encouraging the exploration of alternative build orientations. This study provides support for using software to help designers, particularly nonexpert designers who rely on trial and error during design, evaluate the manufacturability of their designs more effectively, thereby promoting concurrent engineering design practices.

This study aimed to evaluate the effectiveness of using a social science mixed-methods approach to stimulate creativity and improve the attainment of creative outcomes in teaching design. In a focused study framed through a design collaborative experiment within a classroom context, sketches produced by a sample of 351 students were analyzed and the impact of stimuli was categorized by visual, physical, quantitative and contextual information on creative processes and outcomes in product design. Sixteen combinations of these stimuli were integrated as parameters of design briefs (DBs) given to the participating students. This research was augmented with a survey to understand participants’ perceptions and reactions and was rated by expert judges. The results demonstrate that certain combinations of quantitative and qualitative stimuli have a positive impact on creative processes and outcomes. These findings will inform new techniques for engaging and inspiring students in design studies.

Product graphics interchange formats (GIFs) employ this format to show the features of the product and make up for the lack of physical experience online. These GIFs have been widely applied in domains such as e-shopping and social media, aiming to interest and impress viewers. Contrary to this wide application, most designers in this domain lack expertise and produce GIFs of varied quality. Moreover, the knowledge of techniques to enhance viewers’ engagement with product GIFs is also lacking. To bridge the gap, we conducted a series of studies. First, we collected and summarized seven design factors referring to existing literature and semi-structured interviews. Then, the impacts of these design factors were revealed through an online study with 106 product GIFs among 307 participants. The results showed that visual-related factors such as color contrast and moving intensity mainly impact viewers’ interest, while content-related factors such as scenario and style matching impact viewers’ impressions. The simplicity of GIFs also impressed viewers with a quick viewing mode. Finally, we conducted a workshop and verified that these results support large-scale production of product GIFs. Our studies might support the codesign methods of product GIFs and enhance their quality in design practice.

Design representations play a crucial role in facilitating communication between individuals in design. Sketches and physical prototypes are frequently used to communicate design concepts in early-stage design. However, we lack an understanding of the communicative benefits each representation provides and how these benefits relate to the effort and resources required to create each representation. A mixed-methods study was conducted with 44 participants to identify whether sketches and physical prototypes led to different levels of cognitive load perceived by a communicator and listener and the characteristics that shape their cognitive load during communication. Results showed that listeners perceived higher levels of mental and physical demands when understanding ideas as low-fidelity physical prototypes, as compared to sketches. No significant differences were found in the cognitive load levels of communicators between the two conditions. Qualitative analyses of post-task semi-structured interviews identified five themes relating to verbal explanations and visual representations that shape designers’ cognitive load when understanding and communicating ideas through design representations. Results indicate that designers should be aware of the specific objectives they seek to accomplish when selecting the design representation used to communicate. This work contributes to the knowledge base needed for designers to use design representations more effectively as tools for communication.

Caused by the progressing digitalization in mechanical engineering, a significant demand for information about characteristic process and state variables of technical systems arises. However, since it is oftentimes neither obvious what nor how to measure, the integration of measuring functions, in particular in terms of a retrofit, represents a current challenge in mechanical engineering. In order to overcome this challenge, an approach for the systematic identification of potential measurands is provided in this contribution. For this purpose, the approach of physical effect catalogs is taken up and used for the systematic identification of potential measurands, starting from a physical variable to be determined. Existing catalog systems have two major limitations with respect to the intended identification of cause–effect relationships: They assume an effect to be realized and a consideration of design parameters of a technical system is not intended. These limitations are overcome by linking the fundamental idea of existing catalog systems with the basics of multipole-based modeling. In this way, a multipole-based effect catalog system is developed. It creates the foundation to systematically include the changes and transformations of a process or state variable to be determined into the identification of potential measurands.

This article explores the ability of ChatGPT to function as a virtual colleague in helping to design materials for higher education design students. Using a self-study methodology, two university educators attempted to collaborate with ChatGPT to create course materials targeted at higher education design students, before reflecting on its strengths and weaknesses during the process. Contextualising ChatGPT as the latest acute example of digital disruptors that design practices and processes have faced, the authors evaluated its current and potential threats and opportunities for the creation of design-focused learning content. The authors found that ChatGPT was a competent partner with regard to saving time, structuring textual content and documentation, and as a brainstorming tool. However, ChatGPT’s weaknesses included content generation that was often generic, usually requiring much human prompting, cajoling, and manual editing to produce desirable outcomes. Overall, ChatGPT was found to excel at its stated functionality as a language model, with some potentially useful functionality for the creation of higher education design course materials and outlines, as well as limitations. The reflections discussed can be used to inform design educators who may want to work with ChatGPT when designing course materials. However, acknowledging limitations and potential ethical challenges, the authors’ caution that educators may have to evaluate for themselves whether ChatGPT’s potential advantages outweigh its disadvantages.

Virtual reality (VR) sketching has many advantages for product design and tends to be more and more used among designers and non-designers (end-users). Nevertheless, few studies have focused on the skills needed to use VR sketching for non-designers especially VR novices in VR software. This study focuses on identifying the cognitive impact of VR sketching compared to traditional sketching on VR expert and VR novice in an experimental setting. Thirty-one participants composed of VR experts (N = 15) and VR novices (N = 16) completed a mental rotation test and then performed one traditional paper and pencil sketching task and two VR sketching tasks. We also measured the participants’ movements when using the VR sketching. Results show that VR experts perform better than VR novices in VR sketching because training is an essential element for the quality of traditional and VR sketching. Nevertheless, VR novices with previous training in traditional drawing and/or high mental rotation skills will be able to produce good-quality sketches. In addition, the results show that users moving more in the immersive environment performed better quality sketches if the drawing requires more complex shapes. Our results suggest that VR sketching can be complex to use for a part of the population that may be end-users, especially for those with little experience in traditional and VR sketching and with poor visuospatial abilities. We, therefore, advise to check the non-designers’ prior skills, otherwise, it will be necessary to train these users in VR sketching.

Developing effective design solutions requires successful idea generation, development and selection. Studies have demonstrated that engineering students face challenges in these idea phases and may struggle to implement recommended practices, hindering the potential for an innovative outcome. The first part of the study investigated student practices in idea generation, development and selection through think-aloud experimental sessions and post-session interviews. Data analysis from mechanical engineering students’ sessions, with think-aloud and interview data, revealed that students focused on existing ideas, assumed requirements that constrained their divergence, limited their development of ideas and did not engage much in idea selection. Then, in the second phase of the study, we implemented a learning intervention that leveraged research-based education practices to examine student adoption of recommended practices. After engaging with the learning blocks, students generated unconventional ideas, abstained from requirement assumptions early in ideation, generated a larger quantity of ideas, developed ideas intentionally and used more rigorous idea selection methods. These outcomes demonstrated that a relatively short and targeted intervention can support students in leveraging recommended approaches to idea generation, development and selection.

Prototyping is a knowledge generation activity facilitating improved understanding of problem and solution spaces. This knowledge can be generated across a range of dimensions, termed knowledge dimensions (KDs), via a range of methods and media, each with their own inherent properties. This article investigates and characterises the relationships between prototypes and knowledge generated from prototyping activities during the design process, by establishing how different methods and media contribute across KDs. In so doing, it provides insights into prototyping activity, as well as affording a means by which prototyping knowledge generation may be studied in detail. The investigation considers sets of prototypes from eight parallel 16-week design projects, with subsequent investigation of the knowledge contributions that each prototype provides and at what stage of the design process. Results showed statistical significance supporting three inferences: i) teams undertaking the same design brief create similar knowledge profiles; ii) prototyping fidelity impacts KD contribution and iii) KDs align with the different phases of the project. This article demonstrates a means to describe and potentially prescribe knowledge generation activities through prototyping. Correspondingly, the article contends that consideration of KDs offers potential to improve aspects of the design process through better prototyping method selection and sequencing.

Data-driven generative design (DDGD) methods utilize deep neural networks to create novel designs based on existing data. The structure-aware DDGD method can handle complex geometries and automate the assembly of separate components into systems, showing promise in facilitating creative designs. However, determining the appropriate vectorized design representation (VDR) to evaluate 3D shapes generated from the structure-aware DDGD model remains largely unexplored. To that end, we conducted a comparative analysis of surrogate models’ performance in predicting the engineering performance of 3D shapes using VDRs from two sources: the trained latent space of structure-aware DDGD models encoding structural and geometric information and an embedding method encoding only geometric information. We conducted two case studies: one involving 3D car models focusing on drag coefficients and the other involving 3D aircraft models considering both drag and lift coefficients. Our results demonstrate that using latent vectors as VDRs can significantly deteriorate surrogate models’ predictions. Moreover, increasing the dimensionality of the VDRs in the embedding method may not necessarily improve the prediction, especially when the VDRs contain more information irrelevant to the engineering performance. Therefore, when selecting VDRs for surrogate modeling, the latent vectors obtained from training structure-aware DDGD models must be used with caution, although they are more accessible once training is complete. The underlying physics associated with the engineering performance should be paid attention. This paper provides empirical evidence for the effectiveness of different types of VDRs of structure-aware DDGD for surrogate modeling, thus facilitating the construction of better surrogate models for AI-generated designs.

Over the past years, computational methods based on deep learning—that is, machine learning with multilayered neural networks—have become state-of-the-art in main research areas in computer-aided architectural design (CAAD). To understand current trends of CAAD with deep learning, to situate them in a broader historical context, and to identify future research challenges, this article presents a systematic review of publications that apply neural networks to CAAD problems. Research papers employing neural networks were collected, in particular, from CumInCad a major open-access repository of the CAAD community and categorized into different types of research problems. Upon analyzing the distribution of the papers in these categories, namely, the composition of research subjects, data types, and neural network models, this article suggests and discusses several historical and technical trends. Moreover, it identifies that the publications analyzed typically provide limited access to important research components used as part of their deep learning methods. The article points out the importance of sharing training experiments and data, of describing the dataset, dataset parameters, dataset samples, model, learning parameters, and learning results to support reproducibility. It proposes a guideline that aims at increasing the quality and availability of CAAD research with machine learning.

Engineers must engage project stakeholders effectively if stakeholder needs are to be met, and prototypes are key tools for communicating design form and function. Quality stakeholder engagement in the front end of design processes, in particular, is critical in the success or failure of design projects. As remote stakeholder engagement has become increasingly common as industry trends toward distributed design, there is a need to develop the theory and practices behind effective remote design processes, which have not yet been as well-studied as in-person design. This study explored the prototyping strategies for remote stakeholder engagement during front-end design used by 10 engineering practitioners and 10 senior engineering students through semi-structured interviews. Prototyping strategies were found to overlap with many of the strategies described by prior literature that are not specific to remote engagement modes, though several of these strategies were adapted to the remote context, and three emergent strategies for prototyping in remote engagements were identified. Designers’ perceptions of remote versus in-person prototyping strategies for stakeholder engagement in front-end design, including perceived advantages and limitations, were also explored, and recommendations for educators to better prepare engineering students for hybrid and remote work are provided.