The research presented in this paper explores how engineering students cognitively manage concept generation and measures the effects of additional dimensions of sustainability on design cognition. Twelve first-year and eight senior engineering students generated solutions to 10 design problems. Half of the problems included additional dimensions of sustainability. The number of unique design solutions students developed and their neurocognitive activation were measured. Without additional requirements for sustainability, first-year students generated significantly more solutions than senior engineering students. First-year students recruited higher cortical activation in the brain region generally associated with cognitive flexibility, and divergent and convergent thinking. Senior engineering students recruited higher activation in the brain region generally associated with uncertainty processing and self-reflection. When additional dimensions of sustainability were present, first-year students produced fewer solutions. Senior engineering students generated a similar number of solutions. Senior engineering students required less cortical activation to generate a similar number of solutions. The varying patterns of cortical activation and different number of solutions between first-year and senior engineering students begin to highlight cognitive differences in how students manage and retrieve information in their brain during design. Students’ ability to manage complex requirements like sustainability may improve with education.

This paper builds on the lead author’s keynote address to the Design Society’s 22nd International Conference on Engineering Design in 2019, and in doing so provides a personal perspective on the development of the field of design for sustainability. It begins by describing some of the history of the research from the late 1980s until the present day. This is followed by an analysis of the way in which design for sustainability has been reflected within the International Conference on Engineering Design over the last 30 years, highlighting the way in which the focus has shifted over this time from a focus on recycling and end of life to today where sustainability is playing a leading role in the research. The analysis compares the evolution of the subject with the wider policy and practice perspectives linked to global recognition of the need to move towards Sustainable Development. Finally, the paper reflects on the lessons to be learned from this work and their implications for design research illustrating that engineering design has an opportunity to take more leadership within design for sustainability research and use this to enable change within industry.

The engineering design process can produce stress that endures even after it has been completed. This may be particularly true for students who engage with the process as novices. However, it is not known how individual components of the design process induce stress in designers. This study explored the cognitive experience of introductory engineering design students during concept generation, concept selection and physical modelling to identify stress signatures for these three design activities. Data were collected for the design activities using pre- and post-task surveys. Each design activity produced distinct markers of cognitive experience and a unique stress signature that was stable across design activity themes. Rankings of perceived sources of stress also differed for each design activity. Students, however, did not perceive any physiological changes due to the stress of design for any of the design activities. Findings indicate that physical modelling was the most stressful for students, followed by concept generation and then concept selection. Additionally, recommendations for instructors of introductory engineering design courses were provided to help them apply the results of this study. Better understanding of the cognitive experience of students during design can support instructors as they learn to better teach design.

Hackathons are short-term events at which participants work in small groups to ideate, develop and present a solution to a problem. Despite their popularity, and significant relevance to design research, they have only recently come into research focus. This study presents a review of the existing literature on the characteristics of designing at hackathons. Hackathon participants are found to follow typical divergence–convergence patterns in their design process throughout the hackathon. Unique features include the initial effort to form teams and the significant emphasis on preparing and delivering a solution demo at the final pitch. Therefore, hackathons present themselves as a unique setting in which design is conducted and learned, and by extension, can be studied. Overall, the review provides a foundation to inform future research on design at hackathons. Methodological limitations of current studies on hackathons are discussed and the feasibility of more systematic studies of design in these types of settings is assessed. Further, we explore how the unique nature of the hackathon format and the diverse profiles of hackathon participants with regards to subject matter knowledge, design expertise and prior hackathon experience may affect design cognition and behaviour at each stage of the design process in distinctive ways.

In the design of long fibre reinforced thermoplastic (LFT) structures, there is a direct dependency on the manufacturing. Therefore, it is indispensable to integrate the manufacturing influences into the design process. This not only offers new opportunities for material- and load-adapted designs, but also reduces cost-intensive modifications in later stages. The goal of this contribution is to make the complexity manageable by presenting a method which couples LFT manufacturing and structural simulations in an automated optimization loop. Herein, the influence of linear-elastic, local anisotropic material properties as well as residual stresses resulting from the compression molding of LFT on the stiffness-optimized design of beaded plates is investigated. Based on the simulation studies in this contribution, it can be summarized that the resulting bead height and flank angle, considering anisotropies and residual stresses, are smaller compared to isotropic modelling. As a conclusion, the strength constraint limits the maximum bead height and the flank angle needs to be additionally chosen as a consequence of the local fibre orientations and residual stresses resulting from manufacturing. Optimized bead cross sections are only valid for a specific system under investigation, as they depend on the defined boundary conditions (load case, initial charge geometry and position, fibre orientations, etc.).

Facilitation style appears to be an important determinant of design team effectiveness. The neutrality of the group facilitator may be a key factor; however, the characteristics and impact of neutrality are relatively understudied. In a designed classroom setting, we examine the impact of two different approaches to group facilitation: (i) facilitator’s neutrality expressed as low equidistance and high impartiality and (ii) facilitator’s neutrality expressed as high equidistance and low impartiality, on team trust, trust to the facilitator and team potency. To do this, we conducted a repeated-measures experiment with a student sample. Our results indicate that facilitators expressing neutrality through low equidistance and high impartiality had a greater positive impact on team trust. The two approaches did not differ on team potency and facilitator trust. These results contribute to developing theories of design facilitation and team effectiveness by suggesting how facilitation may shape team trust and potency in group design. Based on our findings, we point to the need for future work to further examine the impact of facilitator’s process awareness and neutrality, and show how facilitation methods may benefit teams during creative design teamwork.

Prototyping constitutes a major theme of design education and an integral part of engineering design academic courses. Physical prototypes and the model building process, in particular, have been proved to boost students’ creativity and resourcefulness and assist in the better evaluation of concepts. However, students’ usage of prototypes has still not been explored in depth with the aim of being transformed into educational guidelines. This paper presents an investigation of students’ reasoning behind prototyping activities based on the concept of Purposeful Prototyping, developed in the authors’ previous work. This is performed by identifying instances of prototype use in students’ design projects and by discovering which types of prototyping purposes they apply and to what extent, as well as by studying the relationships between purposes, early design stages, academic performance and project planning. The analysis of the results shows that prototyping can support students’ learning objectives by acting as a project scheduling tool and highlights the contribution of early-stage prototyping in academic performance. It is also confirmed that students’ limited prototyping scope prevents them from gaining prototyping’s maximum benefits and that they require strategic guidelines tailored to their needs. A new, improved list of prototyping purposes is proposed based on the study’s results.

This paper proposes a relationship between design thinking and computational thinking. It describes design thinking and computational thinking as two prominent ways of understanding how people address design problems. It suggests that, currently, each of design thinking and computational thinking is defined and theorized in isolation from the other. A two-dimensional ontological space of the ways that people think in addressing problems is proposed, based on the orientation of the thinker towards problem and solution generality/specificity. Placement of design thinking and computational thinking within this space and discussion of their relationship leads to the suggestion of a dual process model for addressing design problems. It suggests that, in this model, design thinking and computational thinking are processes that are ontological mirror images of each other, and are the two processes by which thinkers address problems. Thinkers can move fluently between the two. The paper makes a contribution towards the theoretical foundations of design thinking and proposes questions about how design thinking and computational thinking might be both investigated and taught as constituent parts of a dual process.

A network modelling approach to educational mapping leads to a scalable computational model that supports adaptive learning, intelligent tutors, intelligent teaching assistants, and data-driven continuous improvement. Current educational mapping processes are generally applied at a level of resolution that is too coarse to support adaptive learning and learning analytics systems at scale. This paper proposes a network modelling approach to structure extremely fine-grained statements of learning ability called Micro-outcomes, and a method to design sensors for inferring a learner’s knowledge state. These sensors take the form of high-resolution assessments and trackers that collect digital analytics. The sensors are linked to Micro-outcomes as part of the network model, enabling inference and pathway analysis. One example demonstrates the modelling approach applied to two community college subjects in College Algebra and Introductory Accounting. Application examples showcase how this modelling approach provides the design foundation for an intelligent tutoring system and intelligent teaching assistant system deployed at Arapahoe Community College and Quinsigamond Community College. A second example demonstrates the modelling approach deployed in an undergraduate aerospace engineering subject at the Massachusetts Institute of Technology to support course planning and teaching improvement.

Additive manufacturing offers a high degree of design freedom. When Design for Additive Manufacturing is conducted properly, lightweight potential can be exploited. This contribution introduces a novel design approach for the widespread fused layer modelling (FLM) technology when using orthotropic Fibre Reinforced Polymer filament. Its objective is to obtain stiff and strong load-path optimized FLM structures in a structured and algorithmic way. The approach therefore encompasses (1) build orientation optimization to consider weaker bonding between layers than intralayer; (2) topology optimization with orthotropic material properties to obtain favourable overall geometry and inner structure; (3) direct build path generation from optimized material orientation and alternatives to the direct generation and (4) simulation. The approach is demonstrated using a lift arm under multiple load cases and further demonstrator parts to show its general applicability. Lightweight potential of individual optimization steps and the influence of modifications contrasting general non-FLM-specific optimization are studied and discussed.

This paper draws on perspectives from co-design as an integrative and collaborative design activity and co-simulation as a supporting information system to advance engineering design methods for problems of societal significance. Design and implementation of the Sustainable Infrastructure Planning Game provides a prototypical co-design artifact that leverages the High Level Architecture co-simulation standard. Three role players create a strategic infrastructure plan for agricultural, water and energy sectors to meet sustainability objectives for a growing and urbaninzing population in a fictional desert nation. An observational study conducts 15 co-design sessions to understand underlying dynamics between actors and how co-simulation capabilities influence design outcomes. Results characterize the dependencies and conflicts between player roles based on technical exchange of resource flows, identifying tension between agriculture and water roles based on water demands for irrigation. Analysis shows a correlation between data exchange, facilitated by synchronous co-simulation, and highly ranked achievement of joint sustainability outcomes. Conclusions reflect on the opportunities and challenges presented by co-simulation in co-design settings to address engineering systems problems.

Although TRIZ is widely acknowledged as a powerful aid to improve efficacy and efficiency of the creative design process, practitioners diffusedly experience difficulties in the selection of the most suitable tool. Such an issue represents a severe limitation in consideration of the large number of tools TRIZ offers. Here, Inventive Principles (IPs) are acknowledged as the most popular TRIZ technique, and their conjointly use with the Contradiction Matrix makes the selection of the appropriate IP a sufficiently supported task. However, the reliability of the Contradiction Matrix is often questioned and an agreement on a solid and reliable procedure for the selection of IPs is far from being reached. In such a context, the paper investigates the recurrence of IPs to solve contradictions, with reference to a classification framework that takes into consideration the nature of the problem to be solved and the technical-scientific domain it belongs to. The outcomes of the analysis reveal that leveraged IPs are considerably related with the technical-scientific domain and the nature of the problem to be solved. The found relationships are worth delving into and translating into selection guidelines.

The importance of intangible code modularity in open-source software, as well as of tangible product modularity in proprietary hardware, is widely acknowledged. Nevertheless, modularity in open-source hardware (OSH) remains under-researched. This article first describes qualitatively different types of modularity based on two OSH case studies and then quantifies each type of modularity, following a unified network-based approach. The results are discussed and compared within each case to test the ‘mirroring hypothesis’, and between cases to evaluate the impact of physical against intangible modularity types. The ultimate goal is to prompt a discussion into a wide but under-explored subset in OSH.

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.

The impact of engineered products is a topic of concern in society. Product impact may fall under the categories of economic, environmental or social impact, with the last category defined as the effect of a product on the day-to-day life of people. Design teams lack sufficient tools to estimate the social impact of products, and the combined impacts of economic, environmental and social impacts for the products they are designing. This paper aims to provide a framework for the estimation of product impact during product design. To estimate product impact, models of both the product and society are required. This framework integrates models of the product, scenario, society and impact into an agent-based model to estimate product impact. Although this paper demonstrates the framework using only social impact, the framework can also be applied to economic or environmental impacts individually or all three concurrently. Agent-based modelling has been used previously for product adoption models, but it has not been extended to estimate product impact. Having tools for impact estimation allows for optimising the product design parameters to increase the potential positive impact and reduce potential negative impact.

Within the last few years, a need for renewed product personalisation has been observed in some markets such as consumer electronics, fashion to meet the exact demands of the customers. Product customisation emphasises the fulfilment of explicit requirements of a defined market segment, but product individualisation targets at satisfying the particular needs of a customer. Mass Individualisation (MI) is a new product design approach comprising of an open hardware platform and multiple modules to be integrated with the platform. It gives freedom to end-users to integrate different modules into the platform as per their choice. Technological and strategic integration of all actors involved in the design process is the primary focus of this research. This paper identifies key areas which need to be focussed on to realise this approach and convert it into an industrial practice by an explorative study of existing product design and customisation approaches. An industrial survey was conducted, and results for the industrial implication and insights on this approach are presented. The findings show that the end product from product design for MI will be more creative and innovative by the networking of all actors, and offers more individualised and technologically advanced products.

Over the past decade, the field of design for sustainable behaviour (DfSB) has gained a growing amount of research interest. However, as the field evolves, new challenges also arise. A suitable unit of analysis is needed to contextualize users’ behaviour issues in a broader socio-cultural and long-term perspective. This paper explores the use of activity theory (AT) as a potential lens for guiding empirical analysis and design exploration in DfSB. By employing a meta-synthesis approach, we systematically search and synthesize existing studies that adopted AT in design for sustainability. Key findings show that AT’s principles and theoretical implications are especially useful for helping design researchers frame and address DfSB challenges. We argue that by taking activity as the unit of analysis, the AT lens can enable researchers to incorporate users’ dynamic, multi-level and complex activity systems into DfSB considerations.

Sensing machine elements offer the potential to upgrade conventional machine elements by extending their primary function to be able to measure a quantity of interest at its point of origin in a technical system, the so-called in situ measurement. To ensure the functionality of these next generation machine elements, special attention must be paid to uncertainty in terms of modelling to be able to correctly evaluate the provided signal and obtain reliable information. Consequently, this contribution describes an approach to classify uncertainty in sensing technology, especially in SMEs, based on the amount of available information, which can be used as a point of departure to reduce the impact of occurring uncertainty to improve the robustness of the obtained signal. Starting from the understanding of uncertainty and the corresponding classification scheme as well as its linkage to robust design from the Collaborative Research Centre 805, a quantitative model is presented to determine the impact of uncertainty on a measuring signal. The applicability of the proposed approach is demonstrated using the example of a sensing timing belt by taking into account the uncertainty from the SME itself and also from the surrounding technical system.

Having upended the traditional software development, which historically was centred exclusively on proprietary, copyright-protected code, open-source has now entered the physical artefact world. In doing so, it has started to change not only how physical products are designed and developed, but also the commercialisation process. In recent years, authors have witnessed entrepreneurs intentionally choosing not to patent their product design and technologies but instead licencing the designs and technologies under open-source licences. The entrepreneurs share their product designs online with their community – people who congregated due to the shared interests in products’ technology or project’s social objectives. Founding a startup firm without excluding others from using their own invention is not a common practice. Therefore, there is reason to ask if this choice a strategic decision or irrational action due to short-sightedness or extreme altruism? Conducting interviews with 65 founders, we grounded a framework explaining that the driver of going open is a result of both intrinsic and extrinsic factors. In addition, we observed the change of identities over time among the entrepreneurs. We hope to use this paper as a pilot study of this emerging socio-technological phenomenon, which is understudied relative to the proprietary product commercialisation process.

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.