External sources of inspiration can promote the discovery of new ideas as designers ideate on a design task. Data-driven techniques can increasingly enable the retrieval of inspirational stimuli based on nontext-based representations, beyond semantic features of stimuli. However, there is a lack of fundamental understanding regarding how humans evaluate similarity between non-semantic design stimuli (e.g., visual). Toward this aim, this work examines human-evaluated and computationally derived representations of visual and functional similarities of 3D-model parts. A study was conducted where participants (n=36) assessed triplet ratings of parts and categorized these parts into groups. Similarity is defined by distances within embedding spaces constructed using triplet ratings and deep-learning methods, representing human and computational representations. Distances between stimuli that are grouped together (or not) are determined to understand how various methods and criteria used to define non-text-based similarity align with perceptions of 'near' and 'far'. Distinct boundaries in computed distances separating stimuli that are 'too far' were observed, which include farther stimuli when modeling visual vs. functional attributes.

Design neurocognition is an emerging research area that can provide insights into the black box of designers’ cognitive processes. However, work to date has focused on neurocognition on its own, without integrating this with other design measures. This paper presents the results of a pilot study which brings together designer neurocognition with design output and assessment of the design process followed in a constrained prototyping activity comparing use of physical and digital Lego. This was achieved via EEG data capture, a TLX survey and measures of design output variance. Differences between physical and digital prototyping methods were found with respect to Task Related Powers of EEG signals and the design process followed with digital prototyping methods found to take longer, require more effort and cause more frustration. No differences were found with regard to design output. Whilst the sample size used (n=12) was small, future studies will use large sample sizes to increase their statistical power and will consider alternative EEG or fNIRS to capture brain activity due to challenges with the headset used in this study.

With the paradigm shift towards Industry 4.0 and digitalisation, manufacturing engineers face several unexplored challenges; in the products for which they are designing production, in the equipment they are designing to realise production systems and in the digitalisation impact on engineering processes. Today's manufacturing system design processes are still based on traditional engineering methods and have difficulties to cope with increased complexity. The aim of this systematic literature review is to explore drivers and barriers to implement digitalisation in engineering processes from a socio-technical perspective. The identified general barriers were cyber security, lack of competence, lack of standards, large investments and resistance to change. For the engineering processes the main drivers were increased product complexity, servitisation, data driven design and engineering productivity, with the main barriers culture, excess amount of data, integration of tools. cyber security and data quality. The study shows the complexity of the challenge, and that it is not only the technology that is the top barrier. Further research is recommended to develop approaches of successful engineering digitalisation implementations.

When a design team faces the problem of designing a complex system, they are required to make several decisions. Because such design problems are difficult to solve all at once, teams often decompose the design problem into several smaller subproblems. This paper discusses the results of a study designed to understand how design teams decompose a factory redesign problem into sets of related subproblems and compare the subproblems obtained for each design team. This exploratory study analyzed the design activities of eight teams of professionals and used clustering to group the variables that the design teams considered. We found that the design teams used different decomposition strategies and different subproblems, but they more often considered subproblems with design variables of the same type, and some teams followed a top-down design process.

Hybrid manufacturing, a combination of additive and subtractive manufacturing capabilities in one system, has recently become a more viable production option across several industries. Although current hybrid manufacturing research covers a broad range of topics, there is a lack of focus on how this new technology impacts both the designer and the operator of hybrid systems. This paper identifies areas of literature across design theory and Industry/Operator 4.0 research efforts and presents a path for applying this research to hybrid manufacturing users. The unique relationship between operator and designer is highlighted as they learn new strategies and develop new intuitive judgements over time to become the first experienced/expert users of hybrid manufacturing. The potential impact of excessive cognitive workload due to the novel combination of processes is discussed. This paper begins a critical discussion about proper knowledge transfer to other hybrid designers and operators, as well as towards efforts of monitoring, inspecting, and automating hybrid manufacturing processes.

This paper presents an EEG (Electroencephalography) study that explores the correlation between the EEG variation across design stages and the quality of the design outcomes. The brain activations of 33 volunteers with engineering backgrounds were recorded while performing a design task using a morphological table to develop an amphibious bike. The EEG variations from the analysing/selecting stage to the illustrating stage were analysed based on the EEG frequency band and channel sets. A significant correlation between the detail level of the design outcome and the power variation mode was observed in theta, alpha and gamma bands, each involving different channel sets. Compared to the assessment results from two evaluators, using EEG variations as a proxy of the detail level of the design outcome could reach a maximum accuracy of 0.727, precision of 0.765, and recall of 0.889. These results also provide suggestions on the selection of the frequency bands and channel sets to achieve better prediction performance according to each metric.

Computational design tools allow the generation of vast numbers of possible designs, entrusting the human designer with describing constraints or specifications to guide exploration of the design space. Designers can have many different decision considerations when conducting this type of exploration, including form, function, users, or context. In this work, we investigate strategies that emerge when people are tasked with exploring a large design space within either a non-immersive (2D) or immersive (VR) interface and equipped with action-based interactions to set or envision specifications related to their considerations. Results from a 28 participant user study uncovers that people have varying strategies to enact their decision considerations that are not unique to the type of interface. However, the interfaces differ in perceptions of enabling breadth or depth of exploration holistically, with preference towards 2D interfaces to compare options, and VR to understand single designs. These results have implications for the user experience of systems that allow designers to explore the outputs of large design spaces, both at the interaction and interface levels.

Developments in artificial intelligence (AI) are opening the possibilities for the development of more advanced design tools. An example of these innovations are generative design tools, in which the generation of complex and high performing products is possible. This study investigates the use of generative design tools and how they may influence the design process and designer behaviour. Six interviews of interdisciplinary designers were conducted to understand the implications of using generative design tools. It was observed that generative design tools primarily allow for quantitative inputs to the tool while qualitative metrics, such as aesthetics, are considered indirectly by designers. The subjectivity of the designer and how they incorporate the quantitative and qualitative metrics in the generative design tool can lead to differing outcomes between designers. Notable differences in tool usage are also observed between expert and novice computational designers. Additional studies should be conducted to further understand the extent generative design tools impact the design process, designer behaviour, and design outcomes.

Small scale fabrication spaces have shown their potential to support our local supply chains during the collapse of many global supply chain networks at the onset of the COVID-19 pandemic. In anticipation of these spaces becoming more significant in local supply chains, it is increasingly important to reduce their environmental impacts. This work investigates the life cycle of small scale fabrication spaces by interviewing 18 participants from these spaces in the United States. Key insights from the interviews include the following: a) material selection, robust inventory management, and user support for material disposal are factors influencing optimal flow of materials and equipment through a fabrication space; b) lack of information from manufacturers and suppliers is a critical obstacle to achieve optimal use of materials and equipment, and informed decision making related to environmental sustainability and ethical labor practices; c) there are opportunities to take advantage of where financial and sustainability goals align; d) individual motivators for fabrication influences sustainable behaviors; and e) effective education about material and equipment use helps fabrication space users with more sustainable decision making.

Context and problem: Reuse is positioned as a strategy capable of countering single-use overconsumption and disposal. For refill at home FMCGs, consumers are responsible for carrying out behaviours that enable this, such as keeping and using products for a prolonged period. However, it is not known if consumers actually fulfil these responsibilities. Aim: This research aims to understand the extent to which consumer reuse behaviours supports the intended reduction in impact and, if not, consider how best to improve it. Method: In-depth interviews with 15 consumers were conducted where the behaviour chain method was used to map resource journeys for 31 refill at home offerings. Results: Five models of consumer behaviour increased the impact of reuse. The critical moments which led consumers to carry out these behaviours were identified, uncovering intervention areas. Conclusions: The behaviour models and critical moments offer a first attempt to systematically analyse how and where actual consumer behaviour can increase the impact of refill at home FMCGs. The results call for focussed interventions across the consumer journey that support reuse components as part of a system.

The challenges of sustainable development require a consistent transformation of decision-making practices in society and in the industry. In this regard, Virtual Reality (VR) is an effective tool, providing experiences that are not accessible in the real world. The overview effect is a feeling of interconnectedness and responsibility towards our planet and its inhabitants described by astronauts after seeing the Earth during spaceflights. We present a VR application merging the overview effect with data visualization. We illustrate the design process and perform a pilot test to assess the emotions raised by the VR experience. Furthermore, we report feedback from design engineering and sustainability experts discussing the applications' potential in decision-making contexts, including product development processes.

During engineering design, different representations are used to convey information about a systems' components, functionality, spatial layout, and interdependencies. These varying representations may have an impact on the interpretation of a system and consequently the decision-making process. This paper presents a research study that tries to capture these different interpretations by investigating how designers divide a system into subsystem clusters. These subsystem clusters can be considered partitions of a set-in combinatorial mathematics. Given designers' subsystem clusters for three products across three representation modalities, three different analysis methods for finding the most likely partition from observed data are presented. Analysis shows that the Variation of Information analysis method gives the most coherent and consistent results for the search of a most likely cluster. In addition, differences in clustering behaviour are observed based on representation modality. These results show that the way an engineer or designer chooses to represent a system impacts how that system is interpreted, which has implications for the decision-making process during engineering design.

The ambidextrous balance of combining quantitative and qualitative approach is crucial to achieve business innovation in our ever-changing society. While exploring the early stages of a problem space, profound and iterative observations of human behaviors allow designers to discover unusual insights and users’ needs. In this regard, the purpose of this study is to introduce the Needfinding Practice course at KAIST and emphasize the importance of observational research to frame new business opportunities. Main learning goal of this course is to expand the problem finding phase for enhancing students’ creativity and divergent thinking abilities. In this study, we have conducted a literature review on needfinding through observation and how the early stage of design thinking process is related to understanding users deeply. By presenting students’ field research projects, we highlighted the process of identifying unexpected latent needs to gain qualitative data for future business implications.

Considering transformation processes within organizations, reflection is an enabler for learning and adaption of engineering processes, methods, and tools. Moreover, reflection as a core element of agile engineering approaches. However, a sound understanding of reflection behaviors of engineers or engineering teams is lacking. In this paper we proposed a structured reflection procedure including different dimensions to reflect on. To analyse reflection behavior of engineers and engineering teams we propose a coding scheme comprising of nine behaviors. The coding scheme allows to evaluate the reflection behavior in real time and give feedbacks to improve the quality of reflection. The proposed coding scheme is initially tested within a workshop with industry partners.

Prior work has demonstrated that gender identity affects team psychological safety, which is critical to the development of a shared understanding of the task. Further, we know that a shared understanding can increase team cohesion and team performance. Little work has investigated how gender differences affect communicative acts within the context of design, and more specifically how gender differences may affect the development of a shared understanding of the design concept between designers. As a first step towards filling this gap, the current work presents findings from a controlled study conducted at The Pennsylvania State University with 22 design dyads (44 designers). The findings from this study indicate that gender identity within design dyads does not affect participants’ shared understanding of a design concept.

Product development is time-consuming and cost-intensive. Part of the costs can be attributed to physical tests. Therefore, new methods are being researched to save resources. One upcoming area that becomes important for the industry is Virtual Reality (VR) technology.

In the state of research studies have already compared VR with methods such as CAD visualizations. However, there is a need for research regarding the comparison to physical models.

Therefore, a comparative study between a physical system and a VR system is evaluated in terms of functional analysis. The study task was to analyze the mechanism of a lawn sprinkler. For evaluation, a function explanation in a final interview was used.

Although more different representations were possible with VR, there was no general improvement. This could be because movements were more difficult to visualize and recognize. The VR application is very suitable if you mainly have to look at systems that are difficult to view in reality. For example, some physical systems may be challenging to see in operation or may not allow a physical cut, so VR can be a solution. The advantages of physical systems can be in using other impressions, such as a feeling of certain forces.

In global competition, companies are continuously searching for a competitive advantage. A growing number of companies have identified the design of their products as a major success factor. The promises for companies associated with outstanding design are extremely attractive, ranging from better customer experience to higher sales growth. But even though the advantages of design seem to be compelling, companies are still having difficulties in leveraging the benefits of design.

In this paper, we want to identify the underlying challenges that explain this gap. We are especially interested in the role and challenges of design in interdisciplinary product development. Therefore, we perform a literature review that clarifies terminology, analyses research approaches and identifies challenges. We determine seven key challenges for design in interdisciplinary product development. Furthermore, we observe a limited scope of research approaches in the reviewed literature. Based on these findings, we propose three further research directions to strengthen the role of design in interdisciplinary product development in the future.

Human-artificial intelligent (AI) - assisted teaming is becoming a strategy for coalescing the complementary strengths of humans and computers to solve difficult tasks. Yet, there is still much to learn regarding how the integration of humans with AI agents into a team affects human behavior. Accordingly, this work begins to inform this research gap by focusing specifically on how the communication structure and interaction changes within AI-assisted human teams. The underlying discourse data for this work originates from a prior research study in which teams solve an interdisciplinary drone design and path-planning problem. Several metrics are employed in this work to study team discourse, including count, diversity, content richness, and semantic coherence. Results show significant differences in communication behavior in AI-assisted teams including more diversity and frequency in communication, more exchange of information regarding principal design parameters and problem-solving strategies, and more cohesion. Overall, this work takes meaningful steps towards understanding the effects of AI agents on human behavior in teams, critical for fully building effective human-AI hybrid teams in the future.

In this paper, we present results from an experiment using EEG to measure brain activity and explore EEG frequency power associated with gender differences of professional industrial designers while performing two prototypical stages of constrained and open design tasks, problem-solving and design sketching. Results indicate no main effect of gender. However, among other main effects, a consistent main effect of hemisphere for the six frequency bands under analysis was found. In the problem-solving stage, male designers show higher alpha and beta bands in channels of the prefrontal cortices and female designers in the right occipitotemporal cortex and secondary visual cortices. In the design sketching stage, male designers show higher alpha and beta bands in the right prefrontal cortex, and female designers in the right temporal cortex and left prefrontal cortex, where higher theta is also found. Prioritising different cognitive functions seem to play a role in each gender's approach to constrained and open design tasks. Results can be useful to design professionals, students and design educators, and for the development of methodological approaches in design research and education.

The extent to which designers can understand users often determines the quality of design outcomes. A deep understanding of users allows the designers to focus on the right problem and make optimal design decisions, which encouraged designers to empathize with users. However, the current imagination-based empathizing strategy appears to be too susceptible to their previous experience and knowledge, which has been questioned concerning effectiveness and accuracy. On the other hand, Virtual Reality (VR) technology provides an opportunity for designers to gain experience-driven empathy by immersing them in a virtual environment that mimics the users' surroundings as if they are seeing the world from users' eyes. While abundant studies covered empathy VR and empathy for design, limited attention has been paid to the chance of bringing VR, empathy, and design research together. Addressing this gap, this study explored literature across domains, identified major concerns about this approach, synthesized the evidence, and discussed the feasibility and validity of the VR-based empathic design research approach.