In order to reduce the time spent on tolerance analysis, it is necessary to correctly identify and prioritize the key characteristics of the product. For multiple-state mechanisms, a systematic procedure for doing this is lacking. We present a new complexity metric for multiple-state mechanisms based on the product behavior, describing the impact of geometrical variation. The sequence of the structural state transitions is linked to the product composition, enabling a clear prioritization of variation-critical states and interfaces. The approach is applied on an industrial case and verified based on a comparison with the company-specified priority tolerance calculations.

The use of decision-making models in the early stages of the development of complex products and technologies is a well-established practice in industry. Engineers rely on well-established statistical and mathematical models to explore the feasible design space and make early decisions on future design configurations. At the same time, researchers in both value-driven design and sustainable product development areas have stressed the need to expand the design space exploration by encompassing value and sustainability-related considerations. A portfolio of methods and tools for decision support regarding value and sustainability integration has been proposed in literature, but very few have seen an integration in engineering practices. This paper proposes an approach, developed and tested in collaboration with an aerospace subsystem manufacturer, featuring the integration of value-driven design and sustainable product development models in the established practices for design space exploration. The proposed approach uses early simulation results as input for value and sustainability models, automatically computing value and sustainability criteria as an integral part of the design space exploration. Machine learning is applied to deal with the different levels of granularity and maturity of information among early simulations, value models, and sustainability models, as well as for the creation of reliable surrogate models for multidimensional design analysis. The paper describes the logic and rationale of the proposed approach and its application to the case of a turbine rear structure for commercial aircraft engines. Finally, the paper discusses the challenges of the approach implementation and highlights relevant research directions across the value-driven design, sustainable product development, and machine learning research fields.

Product–service systems (PSSs) have drawn significant attention as a driver for business innovation and manufacturing servitization. A PSS is a system of products and services, supporting networks and infrastructure that are designed to satisfy customer needs and to generate values. In this paper, a representation framework for PSSs with eight spaces of product, customer, value, actor, service, business model, interaction context and time space has been proposed to help compare different PSSs and to support the design process. A PSS repository using the representation framework has been developed. To illustrate the representation method and its utility, 15 industry PSS cases are briefly introduced, and comparisons of these cases using the PSS representation framework and similarity assessments are explained. The utilities of the framework in designing PSSs and in analyzing and planning manufacturing servitization are discussed as well.

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 the context of cross-disciplinary and cross-company cooperation, several challenges in developing manufacturing systems are revealed through industrial use cases. To tackle these challenges, two propositions are used in parallel. First, coupling technical models representing different content areas facilitates the detection of boundary crossing consequences, either by using a posteriori or a priori connection. Second, it is necessary to enrich these coupled technical models with team and organizational models as interventions focusing on the collaboration between individuals and teams within broader organizational conditions. Accordingly, a combined interdisciplinary approach is proposed. The feasibility and benefits of the approach is proven with an industrial use case. The use case shows that inconsistencies among teams can be identified by coupling engineering models and that an integrated organizational model can release the modelling process from communication barriers.

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.

Ecodesign has gained significant traction in recent years ranging from academic research to business applications at a global scale. Initial emphasis on the environmental aspect of design has evolved to include economic and social aspects, with projects ranging from small-scale products to large-scale industrial systems. In this paper, the authors re-analyse 10 of their major ecodesign research projects of the past ten years to identify five categories of challenges and promising future directions for ecodesign research. This paper is primarily a retrospective position paper based on the authors’ experience of actual design studies, providing also a relevant literature review and summary of design practices.

The architecture of a system is decided at the initial stage of the design. However, the robustness of the system is not usually assessed in detail during the initial stages, and the exploration of alternative system architectures is limited due to the influence of previous designs and opinions. This article presents a novel network generator that enables the analysis of the robustness of alternative system architectures in the initial stages of design. The generator is proposed as a network tool for system architectures dictated by their configuration of source and sink components structured in a way to deliver a particular functionality. Its parameters allow exploration with theoretical patterns to define the main structure and hub structure, vary the number, size, and connectivity of hub components, define source and sink components and directionality at the hub level and adapt a redundancy threshold criterion. The methodology in this article assesses the system architecture patterns through robustness and modularity network based metrics and methods. Two naval distributed engineering system architectures are examined as the basis of reference for the simulated networks. The generator provides the capacity to create alternative complex system architecture options with identifiable patterns and key features, aiding in a broader explorative and analytical, in-depth, time and cost-efficient initial design process.

A multitude of design guidelines that are intended to support design engineers with knowledge and information during the embodiment design of physical products have been developed back in the 1980s and 1990s. However, since then, the setting in which products are developed and designed has changed and associated tasks and activities are carried out globally rather than locally. Yet, knowledge on the benefit of such design guidelines and their impact on the performance of multinational design engineers from different regions and with different levels of experience is still lacking. To address this, a mobile eye tracking study has been developed and carried out with 47 differently experienced practitioners from Germany, Eastern Europe and Asia. The results show differences in how design engineers from different regions with different levels of experience may benefit from design guidelines and how design guidelines may impact experts’ and novices’ performance, indicate beneficial ways of using them and point out the kind of information and the way of representation that attracts the most attention within a design guideline. The paper concludes that the improvement and development of design guidelines that are intended to support the embodiment design of physical products is needed and proposes to rethink current engineering design guidelines both content-wise and representation-wise.

Designing complex products involves working with uncertainties as the product, the requirements and the environment in which it is used co-evolve, and designers and external stakeholders make decisions that affect the evolving design. Rather than being held back by uncertainty, designers work, cooperate and communicate with each other notwithstanding these uncertainties by making assumptions to carry out their own tasks. To explain this, the paper proposes an adaptation of Kendall Walton’s make-believe theory to conceptualise designing as playing games of make-believe by inferring what is required and imagining what is possible given the current set of assumptions and decisions, while knowing these are subject to change. What one is allowed and encouraged to imagine, conclude or propose is governed by socially agreed rules and constraints. The paper uses jet engine component design as an example to illustrate how different design teams make assumptions at the beginning of design activities and negotiate what can and cannot be done with the design. This often involves iteration – repeating activities under revised sets of assumptions. As assumptions are collectively revised, they become part of a new game of make-believe in the sense that there is social agreement that the decisions constitute part of the constraints that govern what can legitimately be inferred about the design or added to it.

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.

The use of metal Additive Manufacturing (AM) has increased in recent years with potential benefits for novel design solutions and efficient manufacturing. In order to utilise these potentials, engineers need to address uncertainties related to product design and the AM process. This paper presents a design process utilising product-specific AM Design Artefacts (AMDAs) to assess uncertainties identified during design. The process emphasises the importance of concurrently developing the product and AM knowledge. Based on a research collaboration with industry, three case studies describe the use of this process in the development of products for AM. In total, six different types of AMDAs show how AM-related uncertainties are resolved to provide confidence in design solutions and manufacturability. The contributions of this paper are: (i) a design process where AMDAs are used as support in evolving and defining an AM design specification, (ii) an example of how Design for AM (DfAM) is practiced in industry and of typical AM uncertainties that are encountered and addressed, and (iii) an example of how collaborative research can facilitate new knowledge for both industry and academia. The practical implication is a DfAM process for engineers to use and adapt according to existing AM knowledge.

This paper presents how numerical optimization methods, like topology optimization, and new design possibilities through additive manufacturing (AM) can be used for structural improvements of the load introduction points in sandwich structures. A new design approach is presented, which allows a direct load-path optimized integration of the load introduction point into the sandwich core. The corresponding methodical procedure is shown and the application is demonstrated exemplarily for a sandwich structure with a honeycomb core. The advantages for design science are that the new design possibilities of AM can be considered and used when designing the load introduction points. Thus, the additional reinforcements of the sandwich structures to absorb locally introduced forces in the lightweight structure can be minimized. This enables a meaningful technical comparison and it can be decided in the future whether such a design can be used for sandwich structures under economic aspects. In addition, the influence of the initial and boundary conditions on the design is presented and discussed in this paper. The challenges of optimizing multiple load introduction points simultaneously as well as the special aspects to be considered when transferring the design approach to larger sandwich structures are also highlighted.

The wearable product market is growing rapidly and is full of products with similar functions and features. Engaging users at an emotional level may be the key to differentiating a product and encouraging long-term use. While researchers have proposed various design approaches to realize design qualities for wearable devices, emotional needs are often extracted by analysis-heavy methods and disconnected in the design process. To bridge this gap, we developed a new approach that uses a two-axis interactive collage tool for users to compare and evaluate wearable products with targeted emotion-related descriptive words. This approach enabled designers to explore how users perceive products and identify types of emotions that were associated with users’ preferences for and perception of the product’s form and visible characteristics. The example study demonstrated this approach by exploring the relationships between product characteristics and design goals, such as user comfort, user delight, and perceived product usefulness. The results showed that products that resemble clothing were perceived as more delightful and comfortable. The approach can be further used to explore other design concepts or goals.

Computational modeling of the human sequential design process and successful prediction of future design decisions are fundamental to design knowledge extraction, transfer, and the development of artificial design agents. However, it is often difficult to obtain designer-related attributes (static data) in design practices, and the research based on combining static and dynamic data (design action sequences) in engineering design is still underexplored. This paper presents an approach that combines both static and dynamic data for human design decision prediction using two different methods. The first method directly combines the sequential design actions with static data in a recurrent neural network (RNN) model, while the second method integrates a feed-forward neural network that handles static data separately, yet in parallel with RNN. This study contributes to the field from three aspects: (a) we developed a method of utilizing designers’ cluster information as a surrogate static feature to combine with a design action sequence in order to tackle the challenge of obtaining designer-related attributes; (b) we devised a method that integrates the function–behavior–structure design process model with the one-hot vectorization in RNN to transform design action data to design process stages where the insights into design thinking can be drawn; (c) to the best of our knowledge, it is the first time that two methods of combining static and dynamic data in RNN are compared, which provides new knowledge about the utility of different combination methods in studying sequential design decisions. The approach is demonstrated in two case studies on solar energy system design. The results indicate that with appropriate kernel models, the RNN with both static and dynamic data outperforms traditional models that only rely on design action sequences, thereby better supporting design research where static features, such as human characteristics, often play an important role.

Empathic design highlights the relevance of understanding users and their circumstances in order to obtain good design outcomes. However, theory-based quantitative methods, which can be used to test user understanding, are hard to find in the design science literature. Here, we introduce a validated method used in social psychological research – the empathic accuracy method – into design to explore how well two designers perform in a design task and whether the designers’ empathic accuracy performance and the physiological synchrony between the two designers and a group of users can predict the designers’ success in two design tasks. The designers could correctly identify approximately 50% of the users’ reported mental content. We did not find a significant correlation between the designers’ empathic accuracy and their (1) performance in design tasks and (2) physiological synchrony with users. Nevertheless, the empathic accuracy method is promising in its attempts to quantify the effect of empathy in design.

In many cases, the use of damping technologies is the only option to reduce undesired vibrations. Despite various damping techniques available on the market, the design of a precise damping behaviour still needs a lot of experimental testing and engineering experience. This is also the case for particle damping. However, for lightweight structures, technologies such as particle damping provide an opportunity to improve the structural dynamic behaviour without a large mass gain. With respect to this conflict, a hybrid numerical and experimental design approach is presented based on frequency based substructuring (FBS). With this technique, the use of experimental data for design optimization is possible and detailed modelling of the nonlinear particle damping system can be avoided. Moreover, based on the FBS, an approach to optimize damping and weight is proposed. All results are compared to experiments, and a subsequent discussion shows that the predictions for particle damping with FBS are accurate for defined operating points from which realistic designs can be derived. Generally, it is shown that methodical design approaches may strongly improve not only product development processes but also structural mechanical design.

Set-based design (SBD), sometimes referred to as set-based concurrent engineering (SBCE), has emerged as an important component of lean product development (LPD) with all researchers describing it as a core enabler of LPD. Research has explored the principles underlying LPD and SBCE, but methodologies for the practical implementation need to be better understood. A review of SBD is performed in this article in order to discover and analyse the key aspects to consider when developing a model and methodology to transition to SBCE. The publications are classified according to a new framework, which allows us to map the topology of the relevant SBD literature from two perspectives: the research paradigms and the coverage of the generic creative design process (Formulation–Synthesis–Analysis–Evaluation–Documentation–Reformulation). It is found that SBD has a relatively low theoretical development, but there is a steady increase in the diversity of contributions. The literature abounds with methods, guidelines and tools to implement SBCE, but they rarely rely on a model that is in the continuum of a design process model, product model or knowledge-based model with the aim of federating the three Ps (People–Product–Process) towards SBCE and LPD in traditional industrial contexts.

This paper presents a framework for studying design thinking. Three paradigmatic approaches are described to measure design cognitive processes: design cognition, design physiology and design neurocognition. Specific tools and methods serve each paradigmatic approach. Design cognition is explored through protocol analysis, black-box experiments, surveys and interviews. Design physiology is measured with eye tracking, electrodermal activity, heart rate and emotion tracking. Design neurocognition is measured using electroencephalography, functional near infrared spectroscopy and functional magnetic resonance imaging. Illustrative examples are presented to describe the types of results each method provides about the characteristics of design thinking, such as design patterns, design reasoning, design creativity, design collaboration, the co-evolution of the problem solution space, or design analysis and evaluation. The triangulation of results from the three paradigmatic approaches to studying design thinking provides a synergistic foundation for the understanding of design cognitive processes. Results from such studies generate a source of feedback to designers, design educators and researchers in design science. New models, new tools and new research questions emerge from the integrated approach proposed and lay down future challenges in studying design thinking.

‘Design cognition’ refers to the mental processes and representations involved in designing, and has been a significant area of interest since the emergence of design research in the 1960s. The field now faces significant challenges moving into the future, with major change required to overcome stagnation in research topics and methodologies. Tackling these challenges requires us to understand the past and present of design cognition research, and open fresh discussions on its future. This thematic collection aims to address this need by taking stock of current approaches, exploring emerging topics and methodologies, and identifying future directions for enquiry. In this editorial, we examine key issues regarding both what we investigate and how we conduct this research. We present a vision formed from a structured literature review, the work of authors in the collection, and the views of a broad cross-section of the design cognition community. This vision is formalized in a roadmap from the present to the near and far future, highlighting key topics and research questions for the field. Ultimately, ecological measurement, new applications of artificial intelligence, and a move towards theory construction and research maturation constitute key long term challenges for the design cognition community.