Despite its importance, the understanding of the behavioural mechanisms underlying rebound effects triggered by sustainable design is still limited. Through a systematic literature review, this study analyses and discusses 18 behavioural mechanisms. The key gaps of behavioural research on rebound effects are (1) limited in-depth analysis of different mechanisms (2); lack of clearly defined concepts; and (3) neglect of various research topics. To bring the behavioural understanding of rebound effects and sustainable design to a higher level, four key steps for future research are suggested.

The design of systems today often involves computer simulation to assess performance and design margins. Understanding how variability erases design margin is important to assure adequacy of margins, especially in optimization efforts. In this paper, we develop a toolchain using open source code libraries in Python, and encapsulate it in Jupyter notebooks, to provide an open source, interactive uncertainty quantification and sensitivity analysis toolchain. This works generally with simulation tools, where a reference folder is created containing a script that reads an input file of parameter values and runs the simulation. With that easily created, the toolchain executes the necessary uncertainty quantification steps with replicates of that reference folder. This approach fits within a broader workflow outlined that defines the variation modes to study, maps to simulation inputs, and screens the variables for sensitivity before conducting an uncertainty quantification. An example is shown in the simulation analysis of a Stirling engine.

This paper proposes an approach for capital goods manufacturers to design Product-Service System (PSS) pilot projects by selecting appropriate products and customers. The authors conducted a single-case empirical study as a part of an ongoing action research project to maximise the learnings from a pilot project while minimising expenditure in extensive trial and error PSS solution testing. Two sets of criteria were elicited for product and customer selection, respectively, followed by a description of the ideal product and customer attributes for each of the three most common PSS archetypes.

Digital technology is dramatically changing our life, industry, and society. Recent evolution of digital technology is expected to significantly impact service work, such as healthcare and social welfare. Meanwhile, an insufficient understanding of service work and workers' needs leads to the failure of technology development and integration. To make digital technology better fit to values and work practices of service workers, various studies have been conducted, especially in the field of participatory design, interaction design, and service design. However, the contribution of engineering design to this topic is still limited. This paper proposes an engineering design approach to design digital technology for better integration in service work. This study combines a modelling method to represent specifications of digital technology and the participatory approach in alignment with the changing service work and workers' values. A case study of the mobile application system for elderly-care professionals is introduced to exemplify the effectiveness of the proposed method. This study intends to foster innovation of service work through digitalization.

The representation of the product use context is a well established design practice in Engineering Design. Recently, design theory is studying the product interaction involving several cognitive aspects such as the possible conditions in which a wrong interaction occurs. The aim of this paper is to find a quantitative evidence of the causes of these misuses. In particular, this study focuses on the detection of bad design and biases.

In this paper, we propose a method that helps to the automatic detection of bad design and biases from patents. The method is based on an approach that defines syntactic rules to detect sentences containing these artifacts. These rules are defined based on an exploratory analysis of the explicit mention of “bad design” and “bias” and then, tested with multiple experiments on a sample of patents. The results give a first quantitative evidence of the presence of bad design and biases in patents and consequently of their importance in the design theory. In particular, it is provided a fine grain analysis of the linguistic structure of sentences containing these artifacts helping designers in detecting automatically them from patents.

The main design activity in engineering practice is to adapt existing designs or to create variants of existing products to new demands, which require a robust model against both parametric and topological changes. To design such a kind of model becomes a big challenge, especially in the development of structural components due to the number of load application points, variable load cases and restrictions from manufacturing technologies. Thus, the generative parametric design approach is applied to generating high dynamic product models, allowing them to be adjusted for changes feasibly.

Advanced Driver Assistance Systems (ADAS) require a high level of interaction between the driver and the system, depending on driving context at a particular moment. Context-aware ADAS evaluation based on vehicle data is the most prominent way to assess the complexity of ADAS interactions. In this study, we conducted interviews with the ADAS development team at Volvo Cars to understand the role of vehicle data in the ADAS development and evaluation. The interviews’ analysis reveals strategies for improvement of current practices for vehicle data-driven ADAS evaluation.

Creativity is required in engineering design. It is required in the aspects of problem-solving - conceptualizing a new solution to a problem, and problem-exploring - conceptualizing a new problem. Studies show that, in both aspects, creativity is a difficult task in practice. The aim of this study is to support the engineering design community by easing the difficulty in the problem-exploring practice. To achieve this, a computational problem-exploring (CPE) model is developed to mimic how design engineers identify a valid design problem. Consequently, a CPE tool - Pro-Explora V1 is developed based on the CPE model. The CPE model consists of a synergy of emergent computational technologies including data retrieval and machine learning. A Markovian model is employed in the CPE model to enable a data-driven random process for exploring design problems. In pilot test, Pro-Explora V1 generated some engineering design-related problems which are meaningful, unique, and could not be distinguished from naturally generated ones. It provides support to design engineers in problem-exploring at the early stage in engineering design. This study contributes to the global effort towards data-driven processes in the fourth industrial revolution.

Design methods are claimed to support designers but, although they are largely taught in academia, their industrial uptake is still lacking. Many reasons have been identified about this flaw and some potential suggestions have been proposed and discussed in literature to overcome the problem. However, a further evidence is that although many students learn such methods from years, they partially or totally abandon the learned methods in their professional careers. This could partially explain the gap between academic and industrial diffusion of design methods. Literature provides suggestions for improving the learning experience of students but different didactical contexts may need more tailored solutions. The work shown in this paper exploits the problem solving potentialities of the TRIZ toolset to provide hints for improving a course focused on teaching a systematic conceptual design method. A set of suggestions has been obtained together some guidelines for applying the considered TRIZ tools to other didactical contexts.

Additive manufacturing has the potential to decrease the climate impact of aviation by providing more light-weight designs. Sustainability is however required to be assessed from a systemic view, including all lifecycle phases, and from a social, ecologic, and economic dimension. This is however challenging in early phase design, where also a large design space need to be explored. A case study is carried out with an aerospace company where two candidate engineering design tools are combined to address this. The integration of these two engineering tools are applied on a Turbine Rear Structure, and shows promising results in enabling a systemic view of sustainability to be integrated and assessed in early phase design space explorations of additive manufactured components. It is recommended that the integration between the two tools is further established and validated.

This article presents a design of an experiment for investigating the effect of changing the geometry of Paralympic alpine sit-ski poles/outriggers in the LW 10-12 class. An experiment design for mapping an individual athlete’s performance parameters has been developed, with a resolution for finding the optimal outrigger geometry. By prototyping an adjustable experiment setup with implemented sensor systems, the performance increase can be analysed and implemented in new equipment. Results show that changing double poling geometry provides a substantial performance increase, regarding time and propulsive force.

The contradictions of TRIZ are now widespread and recognized as an effective inventive design tool. They make it possible to find solution concepts to problems that cannot be solved by optimization approaches. However, many contradictions could be formulated and it could be difficult to choose the priority one. The authors propose here two methods to formulate the contradictions and identify the priority contradiction: an experimental approach on the one hand, and a multiphysics approach on the other hand. This analysis, illustrated through an example of 3D printing of parts, shows that these two approaches are similar in terms of result, and indeed make it possible to formulate contradictions taking into account all the complexity of a system.

As design and design thinking become increasingly important competencies for a modern workforce, the burden of assessing these fuzzy skills creates a scalability bottleneck. Toward addressing this need, this paper presents an exploratory study into a scalable computational approach for design thinking assessment. In this study, student responses to a variety of contextualized design questions – gathered both before and after participation in a design thinking training course – are analyzed. Specifically, a variety of text features are engineered, tested, and interpreted within a design thinking framework in order to identify specific markers of design thinking skill acquisition. Key findings of this work include identification of text features that may enable scalable measurement of (1) user-centric language and (2) design thinking concept acquisition. These results contribute toward the creation of computational tools to ease the burden of providing feedback about design thinking skills to a wide audience.

When designing high performance sports equipment for Paralympic athletes, there are many unknowns for the design engineer to consider. The design challenge is an optimisation task per individual athlete. However, modelling this optimisation is difficult due to the many variables. This article presents the design of an experiment for identifying and evaluating various seating positions in Paralympic rowing by using a rowing ergometer with a modified seat. Results indicate that changing seating position has a substantial impact on per-athlete rowing performance.

Feedback loops are a key characteristic of engineering design processes that increase complexity, time to market, and costs. However, some feedback loops, due to design iteration, have a positive impact on design outcomes (i.e., the quality of the final design), so are worth the time and costs incurred. Other loops, resulting from rework, also have a positive impact on the final design but their impact on current projects, in terms of their urgency and so interruption, is high. Thus, overall, and drawing on socio-technical systems literature, some feedback loops are virtuous circles with a positive impact whereas others are vicious circles with a negative impact. In this paper, we report early work exploring these interplays between rework and design iteration through the development of process simulation models.

Fast fashion, with its focus on cheap clothing and everchanging trends, has an enormous negative environmental and social impact. Despite rising consumer awareness and slow fashion as an emerging trend, many consumers often still rely on fast fashion. This research aims to create insights into why customers keep returning to fast fashion and what is holding them back from buying sustainable fashion. This is done through a quantitative survey (n=521) and qualitative semi-structured interviews (n=9). Based on the findings, opportunities are defined for designers on how, where, and when to intervene, and on which target group to focus. The main obstacles that counter slow fashion are (i) a lack of understandable and clear information, (ii) the price or available budget, and (iii) the importance of social influence. Strategic design interventions should focus on educating eco-conscious and social-conscious customers based on their personal definition of sustainable fashion. By providing in-store information during time of purchase, designers can create new shopping experiences, educate customers using a positive note and build customer relations with the retailer. Further research should extend these insights with retailers' perspectives.

In current era of Anthropocene, human activities and their consequences on Earth ecosystems are under the lens of scientific research. Researchers in every field of study are trying to find alternatives to promote sustainable development. So it is for design, where researchers exploiting the problem-framing and problem-solving nature of the design discipline itself, trying to find new methodologies and tools to enhance sustainable development.

One of the most important tasks in the design for a sustainable production is focused on material selection. In this paper, authors will present a reflection upon material selection methods and an overview of existing material repositories. Traditionally, material selection is usually attributed to designers or technical professionals but nowadays information concurring in materials selection became a complex task to manage. Therefore, authors propose a case study concerning information management upon materials and their selection presented in a new possible approach: material selection as a collaborative task between several departments of an industrial company, to promote an aware information management activity upon materials.

Automating modelling activities in computer-aided design (CAD) systems is no exception within design automation, one of the current research endeavours aiming to use and transform design-related data in design decision-making processes and the generation and evaluation facilitation of new design solutions. The paper explores the differences between CAD models based on their feature-based CAD modelling sequences that lead to the final models' design. The dataset collected and structured for the study contains more than 1400 CAD models clustered on two levels by using an unsupervised K-means clustering algorithm. The algorithm is performed on the number (total and unique) and the first-order Markov model transition matrices of the CAD modelling operations and their sequential order, respectively. Therefore, three and ten groups (clusters) of CAD models are obtained regarding the level of clustering. The results show that most of the obtained groups are specified by the dominant transition between particular modelling operations. In addition, the study also provides insight into the potential of using feature-based CAD modelling operations' sequences as a first step toward automating the user interaction with the CAD system.

Corporate climate action is critical in supporting the transition towards a low-carbon economy. This paper explores what design practices have been adopted by manufacturing companies that have committed to science-based emission reduction targets and are making progress towards those targets. We contribute to the existing literature by providing empirical evidence of circular economy (CE) actions related to product design supporting firms in reaching their emission reduction targets, contributing to the calls for more research showing evidence between CE adoption and emissions reduction.

Circular ecosystem is a growing research field that is gaining attention due to representing a more robust alignment structure than a single firm. However, prior research lacks empirical evidence on how circular ecosystems are structured and how orchestrators coordinate a set of actors towards a coherent circular value proposition. By studying nine organizations related to the carton packaging recycling ecosystem, we reveal the complexity of recovering and co-creating value in a systemic network with actors competing and collaborating simultaneously. Based on that, we propose a framework for orchestrating circular ecosystems. Our results indicate that orchestrators should integrate strategic actors, invest in infrastructure, and innovate in product design. We also discuss ecosystem resilience during and after the covid 19 pandemic, showing how the orchestrator was fundamental to the sustainability of the ecosystem. Overall, this paper contributes to increasing the understanding of inter-organizational relationships towards the circularity of resources.