Co-evolution accounts have generally been used to describe how problems and solutions both change during the design process. More generally, problems and solutions can be considered as analytic categories, where change is seen to occur within categories or across categories. There are more categories of interest than just problems and solutions, for example, the participants in a design process (such as members of a design team or different design teams) and categories defined by design ontologies (such as function-behaviour-structure or concept-knowledge). In this paper, we consider the co-evolution of different analytic categories (not just problems and solutions), by focussing on how changes to a category originate either from inside or outside that category. We then illustrate this approach by applying it to data from a single design session using three different systems of categorisation (problems and solutions, different designers and function, behaviour and structure). This allows us to represent the reciprocal influence of change within and between these different categories, while using a common notation and common approach to graphing quantitative data. Our approach demonstrates how research traditions that are currently distinct from each other (such as co-evolution, collaboration and function-behaviour-structure) can be connected by a single analytic approach.

Additive manufacturing (AM) has gained prominence over the last 15 years and become a viable manufacturing option. Since there is great industrial interest to implement serial production of products using AM, the education of engineers in design for additive manufacturing (DfAM) is important today. DfAM extends on design for manufacturing to provide knowledge about the new design opportunities enabled by AM. A set of design heuristics for additive manufacturing (DHAM) to assist designers with conceptual-level DfAM has previously been proposed. In this paper, these DHAM are evaluated through workshops with 3 engineering companies and 19 engineers, in which the participants re-design existing company products for AM using the DHAM as an aid, and then give feedback. The DHAM are well received by the workshop participants, and they find that the DHAM are good for teaching others about the capabilities of AM, provide a checklist of items to consider to help avoid oversights, and encourage the generation of new ideas. Criticisms include the number of examples provided and the lack of information about how to implement the ideas concretely. It is also found that the DHAM fulfil 16 of 18 criteria for early design phase DfAM methods, identified in academic literature.

The uncertainties and variation of additive manufacturing (AM) material properties and their impact on product quality trouble designers. The lack of experience in AM technologies renders the experts’ assessment of AM components and the establishment of safety margins difficult. Consequently, unexpected qualification difficulties resulting in expensive and lengthy redesign processes might arise. To reduce the risk of qualification failure, engineers might perform copious time-consuming and expensive specimen testing in early phases, or establish overconservative design margins, overriding the weight reduction benefits of AM technologies. In this article, a model-based design method is proposed for the conceptual design of AM space components with affordable test phases. The method utilizes fuzzy logics to systematically account for experts’ assessment of AM properties variation, and to provide an early estimation of a product qualification likelihood related to design parameters of interest, without the need for copious testing. The estimation of qualification likelihood can also point out which are the unique AM material uncertainties that require further specific testing, to enable the design of a product with a better performance and more affordable test phases. The method is demonstrated with the design for AM gridded of ion thrusters for satellite applications.

Sustainability considerations are traditionally difficult to trade-off with technical and business requirements in an early design phase. Hence, design teams need support to reflect early on in the process, on how sustainability may affect profitability and customer value fulfilment in the long term. The commoditisation of modelling and simulation techniques points to gamification and serious gaming as emerging approaches to raise awareness among the design team – as well as users and stakeholders – of the expected behaviour of a solution along its life cycle. The objective of this paper is to explore how serious games can be used to inform decision-makers about the value versus cost implications of being (or not being) ‘sustainability compliant’ when designing products and systems. The paper initially presents the findings from a descriptive study focused on the definition of ‘design support’ intended to raise sustainability awareness through serious gaming. It further describes the development, application and testing of one of such games for material selection in the aerospace industry.

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.

Engineering design induces mental stress for students and the sources of stress for each stage of design are unique. Therefore, strategies are needed to manage the stress of engineering design that are applicable across the design process. This study investigated the effect of a brief mindfulness-based intervention on first-year students’ cognitive stress during concept generation, concept selection and physical modelling. It was found that the mindfulness-based intervention did increase one aspect of students’ state mindfulness (though the effect was small). While prior work indicates that increased mindfulness can lower perceived stress, the increase in students’ state mindfulness during this study was not found to have an observable impact on students’ stress experience. However, students were receptive to completing a mindfulness-based activity in-class and perceived multiple benefits. Physical modelling was the most stressful of the design tasks while concept generation and concept selection produced similar levels of stress. Students used five reoccurring mechanisms for coping with the stress of design including focusing on the task, minimising the importance of their performance, breathing, taking a break and avoidance/distraction. More research should be conducted with longer duration mindfulness-based interventions to understand their potential as a stress management strategy for engineering design.

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.

Interdisciplinary programmes have become common in universities and research groups’ curricula. This study conducted a network analysis on a Korean university’s undergraduate curriculum and used several visualisation tools to assess keywords across courses and departments, revealing epistemological distances between the courses/departments and their concepts of study. This data-driven methodology defined the characteristics of close or neighbouring departments, making it possible to implement narrow interdisciplinarity through common subjects within the courses. Interestingly, a further projected network could determine the implicit relations between departments that are not considered close, which would make it possible to implement a wide interdisciplinary curriculum. The data-driven network analysis conducted in this study contributes to searching for new programmes for specific levels of interdisciplinarity on an empirical basis.

Robust designs protect system utility in the presence of uncertainty in technical and operational outcomes. Systems-of-systems, which lack centralized managerial control, are vulnerable to strategic uncertainty from coordination failures between partially or completely independent system actors. This work assesses the suitability of a game-theoretic equilibrium selection criterion to measure system robustness to strategic uncertainty and investigates the effect of strategically robust designs on collaborative behavior. The work models interactions between agents in a thematic representation of a mobile computing technology transition using an evolutionary game theory framework. Strategic robustness and collaborative solutions are assessed over a range of conditions by varying agent payoffs. Models are constructed on small world, preferential attachment and random graph topologies and executed in batch simulations. Results demonstrate that systems designed to reduce the impacts of coordination failure stemming from strategic uncertainty also increase the stability of the collaborative strategy by increasing the probability of collaboration by partners; a form of robustness by environment shaping that has not been previously investigated in design literature. The work also demonstrates that strategy selection follows the risk dominance equilibrium selection criterion and that changes in robustness to coordination failure can be measured with this criterion.

Mass Personalisation (MP) is becoming more significant to answer diversifying customer needs, as a result of the advancements in digital manufacturing. In contrary to the modular design in mass customisation, Design for MP (DfMP) proposes more profound changes in product and active user involvement in the design process, while maintaining mass efficiency. Traditional product development methodologies fall short in guiding MP, as it has the distinct differences with product variability and the customer involvement with specific needs. In this study, a dedicated design methodology for MP is presented, focussing on these key dimensions. The proposed methodology guides the designer through the development process of a user modifiable design and demonstrates how to facilitate the user involvement in reaching a personalised design. It proposes a flexible and adaptable seed design architecture, and an interactive customer co-creation process. The development of a seed design, construction of its design space, and management of the solution space with a design solution algorithm are elaborated. The application of the methodology was illustrated on the personalisation of knitted footwear, and 3D printed saxophone mouthpiece. The examples show the potential of the methodology to deal with coupled MP cases. A systematic approach to DfMP will allow expanding MP to more products, and acts as a foundation for the customer co-creation oriented design in the context of this emerging paradigm.

Interdisciplinary engineering of cyber physical production systems (CPPS) are often subject to delay, cost overrun and quality problems or may even fail due to the lack of efficient information exchange between multiple interdisciplinary teams working in complex networks within and across companies. We propose a direct integration of multiteam and organisational aspects into the graphical notation of the systems engineering workflow. BPMN++, with eight new notational elements and two subdiagrams, enables the modelling of the required cooperation aspects. BPMN++ provides an improved overview, uniform notation, more compact presentation and easier modifiability from an engineering point of view. We also included a first set of empirical studies and historical qualitative and quantitative data in addition to subjective expert-based ratings to increase validity. The use case introduced to explain the procedure and the notation is derived from surveys in plant manufacturing focussing on the start-up phase and decision support at site. This, in particular, is one of the most complex and critical phases with potentially high economic impact. For evaluation purposes, we compare two alternative solutions for a short-term management decision in the start-up phase of CPPS using the BPMN++ approach.

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.

Although robustness is an important consideration to guarantee the performance of designs under deviation, systems are often engineered by evaluating their performance exclusively at nominal conditions. Robustness is sometimes evaluated a posteriori through a sensitivity analysis, which does not guarantee optimality in terms of robustness. This article introduces an automated design framework based on multiobjective optimisation to evaluate robustness as an additional competing objective. Robustness is computed as a sampled hypervolume of imposed geometrical and operational deviations from the nominal point. In order to address the high number of additional evaluations needed to compute robustness, artificial neutral networks are used to generate fast and accurate surrogates of high-fidelity models. The identification of their hyperparameters is formulated as an optimisation problem. In the frame of a case study, the developed methodology was applied to the design of a small-scale turbocompressor. Robustness was included as an objective to be maximised alongside nominal efficiency and mass-flow range between surge and choke. An experimentally validated 1D radial turbocompressor meanline model was used to generate the training data. The optimisation results suggest a clear competition between efficiency, range and robustness, while the use of neural networks led to a speed-up by four orders of magnitude compared to the 1D code.

The diversity of design research studies and their associated methods and reporting style make it difficult for the design research community of practice to leverage its work into further advancing the field. We illustrate how a structured multilevel analysis of diverse studies creates a canonical model that allows for the transfer of insight between studies, enhances their comprehension, and supports improved study designs. The benefits of such an approach will increase if different stakeholders adopt such structured approaches to enrich the design research community of practice.

This paper develops theoretical foundations for extending Gauss–Hermite quadrature to robust design with computer experiments. When the proposed method is applied with m noise variables, the method requires 4m + 1 function evaluations. For situations in which the polynomial response is separable, this paper proves that the method gives exact transmitted variance if the response is a fourth-order separable polynomial response. It is also proven that the relative error mean and variance of the method decrease with the dimensionality m if the response is separable. To further assess the proposed method, a probability model based on the effect hierarchy principle is used to generate sets of polynomial response functions. For typical populations of problems, it is shown that the proposed method has less than 5% error in 90% of cases. Simulations of five engineering systems were developed and, given parametric alternatives within each case study, a total of 12 case studies were conducted. A comparison is made between the cumulative density function for the hierarchical probability models and a corresponding distribution function for case studies. The data from the case-based evaluations are generally consistent with the results from the model-based evaluation.

The design of a connected engineered system requires numerous design decisions that influence one another. In a connected system that comprises numerous interacting decisions involving concurrency and hierarchy, accounting for interactions while also managing uncertainties, it is imperative to make robust decisions. In this article, we present a method for robust design using coupled decisions to identify design decisions that are relatively insensitive to uncertainties. To account for the influence among decisions, design decisions are modelled as coupled decisions. They are defined using three criteria: the types of decisions, the strength of interactions and the decision levels. In order to make robust decisions, robust design methods are classified based on sources of uncertainty, namely, Type I (noise factors), Type II (design variables) and Type III (function relationship between design variables and responses). The design of a one-stage reduction gearbox is used as a demonstration example. To illustrate the proposed method for robust design using coupled decisions, we present the simultaneous selection of gear material and gearbox geometry in a coupled decision environment while managing the uncertainties involved in designing gearboxes.

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.

‘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.

Robust design methods have expanded from experimental techniques to include sampling methods, sensitivity analysis and probabilistic optimisation. Such methods typically require many evaluations. We study design and noise variable cross-term second derivatives of a response to quickly identify design variables that reduce response variability. We first compute the response uncertainty and variance decomposition to determine contributing noise variables of an initial design. Then we compute the Hessian second-derivative matrix cross-terms between the variance-contributing noise variables and proposed design change variables. Design variable with large Hessian terms are those that can reduce response variability. We relate the Hessian coefficients to reduction in Sobol indices and response variance change. Next, the first derivative Jacobian terms indicate which design variable can shift the mean to maintain a desired nominal target value. Thereby, design changes can be proposed to reduce variability while maintaining a targeted nominal value. This workflow finds changes that improve robustness with a minimal four runs per design change. We also explore further computation reductions achieved through compounding variables. An example is shown on a Stirling engine where the top four variance-contributing tolerances and design changes identified through 16 Hessian terms generated a design with 20% less variance.

Over the last decades, finite element analysis (FEA) has become a standard tool in industrial product development, allowing for virtual analysis of designs, quick turnaround times and prompt implementation of results. Although academic research also provides numerous approaches for evaluating a product’s robustness towards geometrical, material and load variations based on FEA analyses, this, however, stands in striking contrast to the limited use of these FEA-based variation simulations in industry. In order to bridge the existing gap between academic research and industrial application, this paper explores the barriers that limit the adoption of FEA-based variation simulation. The investigation is based on interviews with five lead engineers, followed by a case study that details the underlying technical challenges and allows for some initial suggestions for future solutions. The case study involves a sterile canister with seven geometrical variables. The design objective is to ensure sufficient sealing within the range of expected probabilistic variation. The combined study details the identified main barriers for a wider application, that is, the lack of robust CAD, practical guidelines to select an efficient design of experiments for design purposes, and the complexity of the automated processes. From a technical perspective, the case study results in estimations for main and interaction effects, an accurate metamodel and Monte Carlo simulations of 100,000 samples providing the design engineer with more detailed and actionable insights on the performance of the product compared with the traditional nominal or best/worst case simulations.