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.

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.

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.

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.

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.

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.

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 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 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.

Developing an artificial design agent that mimics human design behaviors through the integration of heuristics is pivotal for various purposes, including advancing design automation, fostering human-AI collaboration, and enhancing design education. However, this endeavor necessitates abundant behavioral data from human designers, posing a challenge due to data scarcity for many design problems. One potential solution lies in transferring learned design knowledge from one problem domain to another. This article aims to gather empirical evidence and computationally evaluate the transferability of design knowledge represented at a high level of abstraction across different design problems. Initially, a design agent grounded in reinforcement learning (RL) is developed to emulate human design behaviors. A data-driven reward mechanism, informed by the Markov chain model, is introduced to reinforce prominent sequential design patterns. Subsequently, the design agent transfers the acquired knowledge from a source task to a target task using a problem-agnostic high-level representation. Through a case study involving two solar system designs, one dataset trains the design agent to mimic human behaviors, while another evaluates the transferability of these learned behaviors to a distinct problem. Results demonstrate that the RL-based agent outperforms a baseline model utilizing the first-order Markov chain model in both the source task without knowledge transfer and the target task with knowledge transfer. However, the model’s performance is comparatively lower in predicting the decisions of low-performing designers, suggesting caution in its application, as it may yield unsatisfactory results when mimicking such behaviors.

Innovation, typically spurred by reusing, recombining and synthesizing existing concepts, is expected to result in an exponential growth of the concept space over time. However, our statistical analysis of TechNet, which is a comprehensive technology semantic network encompassing over 4 million concepts derived from patent texts, reveals a linear rather than exponential expansion of the overall technological concept space. Moreover, there is a notable decline in the originality of newly created concepts. These trends can be attributed to the constraints of human cognitive abilities to innovate beyond an ever-growing space of prior art, among other factors. Integrating creative artificial intelligence into the innovation process holds the potential to overcome these limitations and alter the observed trends in the future.