The computer aided internal optimisation (CAIO) method produces an optimised fibre layout for parts made from fibre-reinforced plastics (FRP), starting from an initial shell geometry and a given load case. Its main principle is iterative reduction of shear stresses by aligning fibre main axes with principal normal stress trajectories. Previous contributions, ranging from CAIO’s introduction over testing to extensions towards multi-layer FRP laminates, highlighted its lightweight design potential. For its application to laminate design approaches, alterations have been proposed; however, questions remain open. These questions include which convergence criteria to use, how to handle ambiguous principle normal stress trajectories, influence of using multi-layer CAIO optimisation instead of the initial single-layer CAIO and how dire consequences of slightly deviating fibre orientations from the optimised trajectories are. These challenges are discussed in depth and guidelines are given. This paper is an enhanced version of a distinguished contribution at the first symposium ‘Lightweight Design in Product Development’, Zurich (June 14–15, 2018).

In this paper, a concept for a community innovation platform is introduced focusing on human behavior of motivation and barriers in the context of product generation development. Building on the state of the art, a three year case study in cooperation with Dr. Ing. h.c. F. Porsche AG is at the core. In the first step of the study, a questionnaire and interviews are used to point out that a new attractive channel for ideas is needed. The three perspectives of users, experts and stakeholders are investigated in order to discover needs and preferences. Based on these findings, in a second step, a software prototype was designed and introduced in a pilot project with more than 200 users. Using a questionnaire and expert workshops, in the final step, this project is evaluated as appealing to the community, and the generated content is demonstrated to be valuable to specific activities of product generation development.

Perceived quality refers to customers’ cognitive and emotional responses to a particular design, often also associated with craftsmanship and customer satisfaction. Previous research defined a taxonomy of perceived quality and provided understanding about how engineering design decisions impact customer satisfaction. Furthermore, development of new products is frequently based on carrying over attributes of existing products, either from the same producer or from competitors. Previous research offered a new product development methodology combining variations of subsystems to carry over from existing products. This brief presents how these two lines of research combined to design the central console of the Porsche Panamera automobile and discusses the opportunities and challenges posed in the practical implementation of this research.

The importance of inter- and transdisciplinary research for addressing today’s complex challenges has been increasingly recognised. This requires new forms of communication and interaction between researchers from different disciplines and nonacademic stakeholders. Demonstrators constitute a crucial communication tool in technology research and development and have the potential to leverage communication between different bodies of knowledge. However, there is little knowledge on how to design demonstrators. This research aims to understand how demonstrators from the fields Internet of Things and Robotics are designed to communicate technology. The goal is to increase the efficiency and effectiveness of demonstrator practice with readily implemented design knowledge and to advance theoretical knowledge in the field of communicating artefacts. We thematically analysed 28 demonstrator design cases, which led to a typology that assists in categorising and understanding 13 key design principles. The typology is built from three perspectives: First, in terms of the overall goal communication, second, in terms of visitor engagement goals (attraction, initial engagement, deep engagement) and third, in terms of resource-related goals (low effort in development and operation). With this typology, we have taken a significant step towards understanding demonstrator design principles for effective technology communication between different stakeholders.

Coordination of distributed design work is an important activity in large-scale and complex engineered systems (LSCES) design projects. Coordination strategies have been studied formally in system design optimization and organizational science. This article reports on a study to identify what strategies are used in coordination practice. While the literature primarily offers prescriptive coordination strategies, this study focussed on the contribution of individuals’ behaviours to system-level coordination. Thus, a coordination strategy is seen as a particular set of individual actions and behaviours. We interviewed professionals with expertise in systems engineering, project management and technical leadership at two large aerospace design organizations. Through qualitative thematic analysis, we identified two strategies used to facilitate coordination. The first we call authority-based and is enabled by technical know-how and the use of organizational authority; the second we call empathetic leadership and includes interpersonal skills, leadership traits and empathy. These strategies emerged as complementary and, together, enabled individuals to coordinate complex design tasks. We found that skills identified in competency models enable these coordination strategies, which in turn support management of interdependent work in the organization. Studying the role of individuals contributes an expanded view on how coordination facilitates LSCES design practice.

This paper critically reviews literature about design framing to clarify an understanding of what is meant by the construct of a design frame. It describes the origins of the term design frame and characterises three distinct definitions that can be found within the literature. It reviews empirical studies of design framing to highlight definitional confusion between studies. It discusses the significance of Dorst’s propositional model of design frames and juxtaposes design frames with other related constructs. It clarifies ways that the resolution of nomenclature for describing design framing might lead to a more coherent body of empirical research into this topic. It suggests that there is value in developing a better cognitive model of design framing and outlines potential steps towards such a model.

Automakers are interested in creating optimal car shapes that can visually convey environmental friendliness and safety to customers. This research examined the influence of vehicle form on perceptions based on two subjective inference measures: safety and perceived environmental friendliness (PEF). A within-subjects study was conducted in 2009 (Study 1) to study how people would evaluate 20 different vehicle silhouettes created by designers in industry. Participants were asked to evaluate forms on several scales, including PEF, safety, inspired by nature, familiarity, and overall preference. The same study was repeated in 2016 (Study 2). The results from the first study showed an inverse relationship between PEF and perceptions of safety. That is, vehicles that appeared to be safe were perceived to be less environmentally friendly, and vice versa. Participants in the second study showed a similar trend, but not as strongly as the 2009 participants. Several shape variables were identified to be correlated with participants’ PEF and safety ratings. The changes in the trend of participants’ evaluations over seven years were also discussed. These results can provide designers with insights into how to create car shapes with balanced PEF and safety in the early design stage.

Seasonal effects can significantly impact the robustness of socio-technical systems (STS) to demand fluctuations. There is an increasing need to develop novel design approaches that can support capacity planning decisions for enhancing the robustness of STS against seasonal effects. This paper proposes a new network motif-based approach to supporting capacity planning in STS for an improved seasonal robustness. Network motifs are underlying nonrandom subgraphs within a complex network. In this approach, we introduce three motif-based metrics for system performance evaluation and capacity planning decision-making. The first one is the imbalance score of a motif (e.g., a local service network), the second one is the measurement of a motif’s seasonal robustness, and the third one is a capacity planning decision criterion. Based on these three metrics, we validate that the sensitivity of STS performance against seasonal effects is highly correlated with the imbalanced capacity between service nodes in an STS. Correspondingly, we formulate a design optimisation problem to improve the robustness of STS by rebalancing the resources at critical service nodes. To demonstrate the utility of the approach, a case study on Divvy bike-sharing system in Chicago is conducted. With a focus on the size-3 motifs (a subgraph consisting three docked stations), we find that there is a significant correlation between the difference of the number of docks among the stations in a motif and the return/rental performance of such a motif against seasonal changes. Guided by this finding, our design approach can successfully balance out the number of docks between those stations that have caused the most severe seasonal perturbations. The results also imply that the network motifs can be an effective local structural representation in support of STS robust design. Our approach can be generally applied in other STS where the system performances are significantly impacted by seasonal changes, for example, supply chain networks, transportation systems and power grids.

The 4th industrial revolution (IR) requires new products and business models adjusted to the rapidly changing market conditions, which calls for evolutions in design engineering. In this context, smart design engineering is here defined as a methodology used during the analysis and design of a product or a system, which can be composed by hardware, software and/or services, and that explicitly considers exploiting the technologies and opportunities from the 4th IR. This work contributes to both design theory and practice: (i) it deepens the understanding about the necessary smart design engineering features and shows how these features impact the product design and development process’s (PDDP) key dimensions (procedures, people and tools); (ii) it maps the features’ impact on PDDP’s phases, thus providing practical direction for process improvement. To accomplish these results, a survey was performed on the initial features presented in the literature, which helped to identify new features and to confirm their relation to the 4th IR and their impact on the PDDP. These features were then analyzed against the 4th IR environment perspectives and core technologies, which led to understanding how they can be used to improve the PDDP.

A critical task in product design is mapping information from the consumer space to the design space. This process is largely dependent on the designer to identify and relate psychological and consumer level factors to engineered product attributes. In this way, current methodologies lack provision to test a designer’s cognitive reasoning and may introduce bias through the mapping process. Prior work on Cyber-Empathic Design (CED) supports this mapping by relating user–product interaction data from embedded sensors to psychological constructs. To understand consumer perceptions, a network of psychological constructs is developed using Structural Equation Modeling for parameter estimation and hypothesis testing, making the framework falsifiable in nature. The focus of this technical brief is toward automating CED through unsupervised deep learning to extract features from raw data. Additionally, Partial Least Square Structural Equation Modeling is used with extracted sensor features as inputs. To demonstrate the effectiveness of the approach a case study involving sensor-integrated shoes compares three models – a survey-only model (no sensor data), the existing CED approach with manually extracted sensor features, and the proposed deep learning based CED approach. The deep learning based approach results in improved model fit.

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.

Several concepts and types of procedures for assessing novelty and related concepts exist in the literature. Among them, the two approaches originally proposed by Shah and colleagues are often considered by scholars. These metrics rely on well-defined novelty types and a specific concept of novelty; however, more than 20 years after the first publication, it is still not clear whether and to what extent these metrics are actually used, why they are used and how. Through a comprehensive review of the papers citing the main work of Shah, Vargas-Hernandez & Smith (2003a, 2003b) (the main study where the metrics are comprehensively described and applied), the present work aims to bridge this gap. The results highlight that only a few of the citing papers actually use the assessment approach proposed by Shah et al. and that a nonnegligible number uses a modified or adapted version of the original metrics. Furthermore, several criticalities in the application of the metrics have been uncovered, which are expected to provide relevant information for scholars involved in reliable and repeatable novelty assessments.

Idea selection is crucial in design as it impacts the outcome of a project. A collaborative design activity could be considered as a social process where the interactions and individual states (such as the importance in the team and self-efficacy level) could affect decision-making. It is often seen in design teams that some individuals, referred to as ‘influencers’ in the article have more capacity to influence than others, hence they govern the team process for better or worse. Due to the limited work done in the past to study the effect of these influencers on design outcomes, the work aims at increasing the understanding by presenting some insights from its agent-based simulation. The simulation results show how different influencer team compositions affect design outcomes in terms of quality and exploration of the solutions. The idea selection starts with the agents who are ready with their solution in their ‘mind’. The work presented in this article describes a framework for simulating decision-making during idea selection by considering the influencer and majority effect. The empirical study presented in the article verifies the model logic, that is, the presence of influencer and the majority during idea selection and supports the assumption that individuals’ agreement on solutions proposed by other team members depends on the degree of influence and past agreement. The results of the simulation show that teams with well-defined influencers produced solutions with higher variety and had more uniform contributions from team members, but also produced solutions of lower quality.

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.

This article proposes the solver-aware system architecting framework for leveraging the combined strengths of experts, crowds and specialists to design innovative complex systems. Although system architecting theory has extensively explored the relationship between alternative architecture forms and performance under operational uncertainty, limited attention has been paid to differences due to who generates the solutions. The recent rise in alternative solving methods, from gig workers to crowdsourcing to novel contracting structures emphasises the need for deeper consideration of the link between architecting and solver-capability in the context of complex system innovation. We investigate these interactions through an abstract problem-solving simulation, representing alternative decompositions and solver archetypes of varying expertise, engaged through contractual structures that match their solving type. We find that the preferred architecture changes depending on which combinations of solvers are assigned. In addition, the best hybrid decomposition-solver combinations simultaneously improve performance and cost, while reducing expert reliance. To operationalise this new solver-aware framework, we induce two heuristics for decomposition-assignment pairs and demonstrate the scale of their value in the simulation. We also apply these two heuristics to reason about an example of a robotic manipulator design problem to demonstrate their relevance in realistic complex system settings.

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.

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.

‘Openness’ is one of the key concepts brought forward by postindustrial narratives questioning the modern repartition of roles between industries and customers. In these narratives, citizen participation in design and intellectual property management based on open source principles are the promise of more sustainable production models. In this context, openness in product design and development has been the object of growing interest and experimentation from academia, businesses and grassroots communities. As a result, numerous concepts emerged that attempt to grasp the essence of this phenomenon, unfortunately leading to overlapping, conflicting or speculative depictions. In this article, we share the understanding we gained throughout 6 years of research on Open Design and Open Source Hardware and attempt to make the difference between myths and facts. We depict an enthusiastic but realistic picture of Open Design and Open Source Hardware practices as we could observe them and deliver a structured framework to situate concepts and their differences. From this, we share seven observations leading to seven corresponding research questions and establish a research agenda to stimulate further investigations into this socially relevant and potentially ground-breaking phenomenon.

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.