Generative adversarial networks (GANs) have recently been proposed as a potentially disruptive approach to generative design due to their remarkable ability to generate visually appealing and realistic samples. Yet, we show that the current generator-discriminator architecture inherently limits the ability of GANs as a design concept generation (DCG) tool. Specifically, we conduct a DCG study on a large-scale dataset based on a GAN architecture to advance the understanding of the performance of these generative models in generating novel and diverse samples. Our findings, derived from a series of comprehensive and objective assessments, reveal that while the traditional GAN architecture can generate realistic samples, the generated and style-mixed samples closely resemble the training dataset, exhibiting significantly low creativity. We propose a new generic architecture for DCG with GANs (DCG-GAN) that enables GAN-based generative processes to be guided by geometric conditions and criteria such as novelty, diversity and desirability. We validate the performance of the DCG-GAN model through a rigorous quantitative assessment procedure and an extensive qualitative assessment involving 89 participants. We conclude by providing several future research directions and insights for the engineering design community to realize the untapped potential of GANs for DCG.

Generative neural networks (GNNs) have successfully used human-created designs to generate novel 3D models that combine concepts from disparate known solutions, which is an important aspect of design exploration. GNNs automatically learn a parameterization (or latent space) of a design space, as opposed to alternative methods that manually define a parameterization. However, GNNs are typically not evaluated using an explicit notion of physical performance, which is a critical capability needed for design. This work bridges this gap by proposing a method to extract a set of functional designs from the latent space of a point cloud generating GNN, without sacrificing the aforementioned aspects of a GNN that are appealing for design exploration. We introduce a sparsity preserving cost function and initialization strategy for a genetic algorithm (GA) to optimize over the latent space of a point cloud generating autoencoder GNN. We examine two test cases, an example of generating ellipsoid point clouds subject to a simple performance criterion and a more complex example of extracting 3D designs with a low coefficient of drag. Our experiments show that the modified GA results in a diverse set of functionally superior designs while maintaining similarity to human-generated designs in the training data set.

Traditional aerospace design methods offer quick and efficient ways to generate new designs, but such that often resemble previous ones. For truly innovative design, however, a different approach is needed. This paper suggests that a general conceptual design method called ‘parameter analysis’ (PA) may be used for teaching and practicing innovative aerospace design. To support this proposition, we investigate four diverse, innovative and unique case studies, all carried out by very experienced aerospace designers: the ‘dam busting’ bouncing bomb of World War II, the Gossamer Condor human-powered plane of the 1970s, the asymmetric Boomerang twin-engine plane of the 1990s and the SpaceShipOne suborbital spacecraft of the early 2000s. The paper elaborates on how the methodology of case-study research has been adapted and applied to provide the evidence supporting the research hypothesis, and presents the results of analyzing the case studies. It shows that the expert aerospace designers followed a thought process similar to PA, even if unknowingly, where the similarity was measured by counting the number of PA characteristics that could be shown to exist in the case studies. Advantages and limitations of the research methodology are also discussed.

Electric vehicles (EVs) are very quiet at low speed, which can be hazardous for pedestrians, especially visually impaired people. It is now mandatory (since mid-2019 in Europe) to add external warning sounds, but poor sound design can lead to noise pollution, and consequently annoyance. Moreover, it is possible that EVs are not sufficiently detectable in urban areas because of the masking effect from the background noise. In this paper, we propose a method for the design of warning sounds that takes into account both detectability and unpleasantness. The method implements a multiobjective interactive genetic algorithm (IGA) for the optimisation of the characteristics of synthesised sounds. An experiment is proposed to a first panel of participants in order to define a set of Pareto efficient sounds. At the individual level, sounds obtained with the IGA are compared to different sound design proposals. Results show that the quality of the sounds designed by the IGA method is comparable to those provided by a sound designer. From the sounds of the Pareto set, a design recommendation method based on the probability distributions of the sounds’ characteristics is proposed. An external validation with a second panel of participants shows that these recommended sounds constitute relevant trade-offs when compared to other design proposals.

User experience (UX) application in the practice of engineering and product design is still limited. The present paper provides insights into research on UX design and recommendations for design practitioners by pointing out common criticalities. These outcomes are achieved through a literature review on how UX relates to design. First, issues in benefitting from UX understanding in design are identified with a specific focus on theoretical contributions. Second, experimental papers investigating UX and design are analysed in relation to previously identified issues. Although issues are present to some extent in all the contributions, the empirical studies dealing with UX in design are overall valid. The results highlight UX’s support in revealing design requirements, but its capability of steering design processes is arguable, as concrete guidelines for practitioners are not well described. Based on identified issues, the authors propose a checklist to make UX studies in design more reliable and their outcomes more comparable.

Engineering design research has focused on developing and refining methods and evaluating design education in design education, design research and design in practice. One important aspect that is not thoroughly investigated is the influence of bias on design within these spaces of design. Bias is known to impact the interpretation of information, decision-making and practices in all areas. These factors are vital in engineering design education, practice and research, emphasizing the importance of investigating bias. The first goal of this study is to highlight and synthesize existing bias research in design education, research and practice. The second goal is to identify areas where bias may be under-researched or under-reported in design. To achieve these goals, a comparative analysis is performed against a comparable field: medicine. Many parallels exist between both fields. Patient–provider and designer–end-user relationships are comparable. Medical education is comparable to design education with the cooperative, inquiry-based and integrated learning pedagogy approaches. Lastly, physicians and design engineers both solve cognitively complex systems-oriented problems. Leveraging research on bias in medicine enables us to highlight gaps in engineering design. Recommendations are made to help design researchers address these gaps.

We developed a method to generate omnidirectional depth maps from corresponding omnidirectional images of cityscapes by learning each pair of an omnidirectional and a depth map, created by computer graphics, using pix2pix. Models trained with different series of images, shot under different site and sky conditions, were applied to street view images to generate depth maps. The validity of the generated depth maps was then evaluated quantitatively and visually. In addition, we conducted experiments to evaluate Google Street View images using multiple participants. We constructed a model that predicts the preference label of these images with and without the generated depth maps using the classification method with deep convolutional neural networks for general rectangular images and omnidirectional images. The results demonstrate the extent to which the generalization performance of the cityscape preference prediction model changes depending on the type of convolutional models and the presence or absence of generated depth maps.

Recent years have seen the rise of citizens as contributors to hardware product creation. This trend has increased attention to open source hardware (OSH): a phenomenon that extends the intellectual property management and development practices in open source software (OSS) into the design of physical objects. OSH projects are different from OSS projects due to product type, and distinct from traditional closed source new product development (NPD) ones due to their openness. These differences challenge the degree of applicability of existing project success definitions in the OSH context. To investigate project success in OSH, we conducted a qualitative survey with practitioners. We report characteristics of successful OSH projects through three identified themes: (a) value creation – the big-picture impact, (b) quality of output – the quality of the hardware and accompanying documentation and (c) project process – activities that contribute to success. We contextualise by comparing OSH with selected literature on the success of OSS and NPD project management. While our study confirms a similarity between OSS and OSH in defining project success, it also highlights themes that are uniquely important to the latter. These findings are helpful for OSH development practice and could provide lessons for OSS development and closed source NPD.

A radical shift in technology is necessary to enable future air transport solutions. Sustainability targets for aeroengine manufacturing mean more than reducing CO2 and NOX. The future will open up possibilities and bring new challenges when introducing hybrid- and electrical propulsion technologies using new materials, technology solutions and new business models. This article reports on findings from a longitudinal study and many years of collaboration between researchers and industry experts, where a first-tier aeroengine manufacturer transforms their product development capabilities to enable sustainable product development. The article highlights some activities undertaken and identifies critical challenges and opportunities remaining for a manufacturer of next-generation aeroengine solutions. It is argued that the challenge for aeroengine manufacturers to develop new-generation propulsive technologies will require a systemic change in the undertaking of design and development. The opportunities of sustainable technologies are evident yet require: (1) means to tighter integrate business and technology development, (2) the ability to quantify and assess sustainability impacts of different concept solutions, and (3) means to utilise natural resources, alloys and materials for a circular and life-cycle optimised solution.

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.

Consumers’ choice of services and the product platforms that deliver them, such as apps and mobile devices, or eBooks and eReaders, are becoming inextricably interrelated. Market viability demands that product–service combinations be compatible across multiple producers and service channels, and that the producers’ profitability must include both service and product design. Some services may be delivered contractually or physically, through a wider range of products than others. Thus, optimization of producers’ contingent products, services, and channel decisions becomes a combined decision problem. This article examines three common product–service design scenarios: exclusive, non-exclusive asymmetric, and non-exclusive symmetric. An enterprise-wide decision framework has been proposed to optimize integrated services and products for each scenario. Optimization results provide guidelines for strategies that are mutually profitable for partner–competitor firms. The article examines an example of an eBook service and tablet, with market-level information from four firms (Amazon, Apple, Barnes & Noble, and Google) and conjoint-based product–service choice data to illustrate the proposed framework using a scalable sequential optimization algorithm. The results suggest that firms in market equilibrium can markedly differ in the services they seek to provide via other firms’ products and demonstrate the interrelationship among marketing, services, and product design.

The problem at hand is that vast amount of data on industrial changes is captured and stored; yet the present challenge is to systematically retrieve and use them in a purposeful way. This paper presents an industrial case study where complex product development processes are modeled using the design structure matrix (DSM) to analyze engineering change requests sequences. Engineering change requests are documents used to initiate a change process to enhance a product. Due to the amount of changes made in different projects, engineers want to be able to analyze these change processes to identify patterns and propose the best practices. The previous work has not specifically explored modeling engineering change requests in a DSM to holistically analyze sequences. This case study analyzes engineering change request sequences from four recent industrial product development projects and compares patterns among them. In the end, this research can help to identify and guide process improvement work within projects.

Along with the ever-increasing customer demands, early consideration of variation in terms of robust design is important to avoid expensive iterations in the product development. However, existing methods are detached from the development process and can therefore only be applied at a late stage or only with comprehensive expert knowledge. Especially in the concept stage, where the geometry of a product is not yet defined and the optimisation potential is high, effective solution proposals for systematic consideration of variation are lacking. Therefore, this paper describes a new integrated approach facilitating robust and tolerance design in the concept stage. The novelty of the approach using ontologies and graph-based visualisation is the close linkage of product development and tolerance knowledge, which allows automation and helps to avoid time- and cost-intensive iteration loops. As a result, a robust and tolerance-compliant concept design, an initial qualitative tolerance specification and instructions for the further tolerancing process are already available at the end of the concept stage. The applicability and the benefits of the approach are illustrated by representative case studies and a user study allowing a critical comparison between the conventional, mostly subjective procedure and the presented approach.

Much of the literature on biologically inspired design makes two, often unstated and largely unexamined, assumptions: (i) The process of biologically inspired design is independent of the biological domain, and (ii) the design process leads to multifunctional designs. In this paper, we perform a meta-analysis of 74 case studies of biologically inspired design in the Design Study Library. We begin by noting that biologically inspired design has two core processes: problem-driven design and solution-based design. We find that the first assumption about the domain independence of these design processes is questionable. Our analysis indicates that the problem-driven process of biologically inspired design is more prevalent in some domains, whereas the solution-based design process is more common in other domains. Our analysis also indicates that the solution-based process leads to multifunctional designs more often than the problem-driven process. These findings may have useful implications not only for building information-processing theories of biologically inspired design, but also for developing pedagogical techniques for teaching about the paradigm and computational tools for supporting its practice.

Design structure matrices (DSMs) are widely known for their ability to support engineers in the management of dependencies across product and organisational architectures. Recent work in the field has exploited product lifecycle management systems to generate DSMs via the co-occurrence of edits to engineering files. These are referred to as dynamic DSMs and results have demonstrated both the efficacy and accuracy of dynamic DSMs in representing engineering work and emergent product architectures. The wide-ranging applicability of the theoretical model and associated analytical process to generate dynamic DSMs enables investigations into the evolving structures within digital engineering work. This paper uses this new capability and presents the results of the world’s first comparison of dynamic DSMs from a set of near-identical systems design projects. Through comparison of the dynamic DSMs’ end-of-project state, change propagation characteristics and evolutionary behaviour, 10 emergent structures are elicited. These emergent structures are considered in the context of team performance and design intent in order to explain and code the identified structures. The significance of these structures for the management of future systems design projects in terms of productivity and efficacy is also described.

Although nanoparticles have been shown to have clear technological advantages, their use in some consumer products remains controversial, particularly where these products come in direct contact with our bodies. There has been much discussion about using metal oxide nanoparticles in sunscreens, and numerous technology assessments aimed at predicting the type, size and concentration of nanoparticles and surface treatments that will be best for consumers. Yet, the optimal configuration is ultimately the one that people actually want and are willing to pay for, but until now consumer preferences have not been included in model predictions. We describe and discuss a proof of concept study in which we design and implement a hypothetical sunscreen product configurator to predict how people tradeoff sun protection factor (SPF), product transparency and potential toxicity from reactive oxygen species (ROS) in configuring their most preferred sunscreen. We also show that preferred nanoparticle sizes and concentrations vary across demographic groups. Our results suggest that while consumers choose to reduce or eliminate potential toxicity when possible, they do not automatically sacrifice high SPF and product transparency to avoid the possibility of toxicity from ROS. We discuss some advantages of using product configurators to study potential product designs and suggest some future research possibilities.

Open Innovation (OI) supports companies in systematically collaborating with external partners, offering various advantages. However, companies still face several challenges when applying OI, e.g., identifying relevant OI partners, collaboration methods, and project risks. Often, insufficient planning is the reason for subsequent deficits in OI projects. The analysis of relevant context factors (‘situation’) is important, which affect and constrain OI. To date, a general approach for analyzing (open) innovation situations or guidelines for developing one do not exist. Usually researchers develop their own situation analysis, including extensive literature reviews and experiencing similar challenges. This publication sets the basis for successfully planning OI projects. It focuses on developing an analysis approach for OI situations and supports other researchers in developing their own analysis approaches. The resultant objectives of the publication are to: (1) provide a list of potential situation analysis criteria; (2) provide a guideline for developing a situation analysis; (3) provide initial indications of relevant OI-specific situation criteria. The criteria were derived from the literature and qualitatively evaluated by three industry partners to assess their usability. Although this work is exploratory, and the results are not automatically generalizable, it is an important contribution for ensuring the success of OI, and for analyzing enablers and barriers to knowledge transfer from academia to industry.

3D printing technologies, such as material extrusion (MEX), hold the potential to revolutionise manufacturing by providing individuals without traditional manufacturing capabilities with powerful and affordable resources. However, widespread adoption is impeded by the lack of user-friendly design tools due to the necessity of domain-specific expertise in computer-aided design (CAD) software and the overwhelming level of design freedom afforded by the MEX process. To overcome these barriers and facilitate the democratisation of design (DoD), this article introduces an innovative, generative-based design (GBD) methodology aimed at enabling non-technical users to create functional components independently. The novelty of this methodology lies in its capacity to simplify complex design tasks, making them more accessible to non-designers. The proposed methodology was tested in the design of a load-bearing part, yielding a functional component within two design iterations. A comparative analysis with the conventional CAD-based process revealed that the GBD methodology enables the DoD, reflected in a 68% reduction in design activities and a decrease in design difficulty of 62% in requisite know-how and a 55% in understanding. Through the creation and implementation of this methodology, the article demonstrates a pioneering integration of state-of-the-art techniques of generative design with design repositories enabling effective co-design with non-designers.

Customer preference modelling has been widely used to aid engineering design decisions on the selection and configuration of design attributes. Recently, network analysis approaches, such as the exponential random graph model (ERGM), have been increasingly used in this field. While the ERGM-based approach has the new capability of modelling the effects of interactions and interdependencies (e.g., social relationships among customers) on customers’ decisions via network structures (e.g., using triangles to model peer influence), existing research can only model customers’ consideration decisions, and it cannot predict individual customer’s choices, as what the traditional utility-based discrete choice models (DCMs) do. However, the ability to make choice predictions is essential to predicting market demand, which forms the basis of decision-based design (DBD). This paper fills this gap by developing a novel ERGM-based approach for choice prediction. This is the first time that a network-based model can explicitly compute the probability of an alternative being chosen from a choice set. Using a large-scale customer-revealed choice database, this research studies the customer preferences estimated from the ERGM-based choice models with and without network structures and evaluates their predictive performance of market demand, benchmarking the multinomial logit (MNL) model, a traditional DCM. The results show that the proposed ERGM-based choice modelling achieves higher accuracy in predicting both individual choice behaviours and market share ranking than the MNL model, which is mathematically equivalent to ERGM when no network structures are included. The insights obtained from this study further extend the DBD framework by allowing explicit modelling of interactions among entities (i.e., customers and products) using network representations.

Mindfulness-based interventions (MBIs) for engineering design are promising for their stress management, cognition and well-being benefits. Prior work concluded that engineering design is stressful and that each engineering design stage has unique stressors. This non-randomized study investigated the effect of an MBI on students’ cognitive stress and final design creativity during a multistage, hands-on design assessment. Data were collected using surveys, project deliverables and follow-up interviews. While no significant increase was found in students’ measured state mindfulness due to the intervention, students in the MBI condition were more likely to perceive the intervention positively compared to students in the control condition (alternative use tasks). Students in both conditions were found to have similar levels of state stress, which indicates that the MBI had no observable effect on students’ measured stress during design. Although students in the MBI condition were found to produce higher-quality final designs, there were no differences in design creativity or novelty. When data were clustered to identify types of student experiences, state mindfulness was found to meaningfully contribute, but state stress was not. Future research should continue to investigate MBIs in engineering design as a potential approach to improve design education and outcomes.