Supporting designers is one of the main motivations for design research. However, there is an ongoing debate about the ability of design research to transfer its results, which are often provided in form of design methods, into practice. This article takes the position that the transfer of design methods alone is not an appropriate indicator for assessing the impact of design research by discussing alternative pathways for impacting design practice. Impact is created by different means – first of all through the students that are trained based on the research results including design methods and tools and by the systematic way of thinking they acquired that comes along with being involved with research in this area. Despite having a considerable impact on practice, this article takes the position that the transfer of methods can be improved by moving from cultivating method menageries to facilitating the evolution of method ecosystems. It explains what is understood by a method ecosystem and discusses implications for developing future design methods and for improving existing methods. This paper takes the position that efforts on improving and maturing existing design methods should be raised to satisfy the needs of designers and to truly support them.

Recommendations of sustainable design methods are usually based on theory, not empirical industry tests. Furthermore, since professionals often mix components of different design methods, recommending whole methods may not be relevant. It may be better to recommend component activities or mindsets. To provide empirical grounding for recommendations, this study performed 23 workshops on three sustainable design methods involving over 172 professionals from 27 companies, including consultancies and manufacturers in three industries (consumer electronics, furniture and clothing). The design methods tested were The Natural Step, Whole System Mapping and Biomimicry. Participants were surveyed about what components in each design method drove perceived innovation, sustainability or other value, and why. The most valued components only partially supported theoretical predictions. Thus, recommendations should be more empirically based. Results also found unique and complementary value in components of each method, which suggests recommending mixed methods for sustainable design. This may help design professionals find more value in green design practices, and thus integrate sustainability more into their practice.

During the design stage, quick and accurate predictions are required for effective design decisions. Model developers prefer simple interpretable models for high computation speed. Given that deep learning (DL) has high computational speed and accuracy, it will be beneficial if these models are explainable. Furthermore, current DL development tools simplify the model development process. The article proposes a method to make the learning of the DL model explainable to enable non–machine learning (ML) experts to infer on model generalization and reusability. The proposed method utilizes dimensionality reduction (t-Distribution Stochastic Neighbour Embedding) and mutual information (MI). Results indicate that the convolutional layers capture design-related interpretations, and the fully connected layer captures performance-related interpretations. Furthermore, the global geometric structure within a model that generalized well and poorly is similar. The key difference indicating poor generalization is smoothness in the low-dimensional embedding. MI enables quantifying the reason for good and poor generalization. Such interpretation adds more information on model behaviour to a non-ML expert.

Knowledge management (KM) is the process of creating, sharing, using and managing the knowledge and information of an organization. In this paper, we investigate KM practices and needs in Open Source Hardware (OSH) communities. The aim is to provide insights into the KM issues and challenges facing these communities. Our analysis is based on interviews carried out with the participants of 22 projects. Interview transcriptions were coded and tagged with concepts extracted from the literature. Text mining was used to uncover the main concepts embedded in the corpus. From this analysis, knowledge sharing emerged as one of the top-rated concepts. Codification and personalization also proved to be important KM approaches, both requiring support in the OSH design process. Using a dendrogram, we highlighted the benefits and challenges of codification together with some interesting concept associations. High contributor turnover, little or no standardization and weak project structuring are still key challenges for OSH communities when it comes to ensuring awareness, making decisions and sharing knowledge.

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

This paper presents results from an experiment using electroencephalography to measure neurophysiological activations of mechanical engineers and industrial designers when designing and problem-solving. In this study, we adopted and then extended the tasks described in a previous functional magnetic resonance imaging study reported in the literature. The block experiment consists of a sequence of three tasks: problem-solving, basic design and open design using a physical interface. The block is preceded by a familiarizing pre-task and then extended to a fourth open design task using free-hand sketching. This paper presents the neurophysiological results from 36 experimental sessions of mechanical engineers and industrial designers. Results indicate significant differences in activations between the problem-solving and the open design tasks. The paper focuses on the two prototypical tasks of problem-solving layout and open design sketching and presents results for both aggregate and temporal activations across participants within each domain and across domains.

Digitalization and the momentous role being assumed by data are commonly viewed as pervasive phenomena whose impact is felt in all aspects of society and the economy. Design activity is by no means immune from this trend, and the relationship between digitalization and design is decades old. However, what is the current impact of this ‘data revolution’ on design? How will the design activity change? What are the resulting research questions of interest to academics? What are the main challenges for firms and for educational institutions having to cope with this change? The paper provides a comprehensive conceptual framework, based on recent literature and anecdotal evidence from the industry. It identifies three main streams: namely the consequences on designers, the consequences on design processes and the role of methods for data analytics. In turn, these three streams lead to implications at individual, organizational and managerial level, and several questions arise worthy of defining future research agendas. Moreover, the paper introduces relational diagrams depicting the interactions between the objects and the actors involved in the design process and suggests that what is occurring is by no means a simple evolution but a paradigmatic shift in the way artefacts are designed.

We developed a method to generate omnidirectional depth maps from corresponding omnidirectional images of cityscapes by learning each pair of an omnidirectional and a depth map, created by computer graphics, using pix2pix. Models trained with different series of images, shot under different site and sky conditions, were applied to street view images to generate depth maps. The validity of the generated depth maps was then evaluated quantitatively and visually. In addition, we conducted experiments to evaluate Google Street View images using multiple participants. We constructed a model that predicts the preference label of these images with and without the generated depth maps using the classification method with deep convolutional neural networks for general rectangular images and omnidirectional images. The results demonstrate the extent to which the generalization performance of the cityscape preference prediction model changes depending on the type of convolutional models and the presence or absence of generated depth maps.

This paper proposes a task-related electroencephalogram research framework (tEEG framework) to guide scholars’ research on EEG-based cognitive and affective studies in the context of design. The proposed tEEG framework aims to investigate design activities with loosely controlled experiments and decompose a complex design process into multiple primitive cognitive activities, corresponding to which different research hypotheses on basic design activities can be effectively formulated and tested. Thereafter, existing EEG techniques and methods can be applied to analyse EEG signals related to design. Three application examples are presented at the end of this paper to demonstrate how the proposed framework can be applied to analyse design activities. The tEEG framework is presented to guide EEG-based cognitive and affective studies in the context of design. Existing methods and models are summarized, for the effective application of the tEEG framework, from the current literature spread in a wide spectrum of resources and fields.

As products are being developed over time and across organisations, the risk for unintended accumulation and mis-conception of margins allocated may occur. Accumulation of margins can result in over design, but also add risk due to under allocation. This paper describes the different terminology used in one organisation and shows the different roles margins play across the design process and in particular the how margins are a critical but often overlooked aspect of product platform design. The research was conducted in close collaboration with a truck manufacturer between 2013 and 2018. The objective was to gain understanding of the current use of margins, and associated concepts evolve along the product life cycle, across organisation and product platform representations. It was found that margins already play an important role throughout the entire design process; however, it is not recognised as a unified concept which is clearly communicated and tracked throughout the design process. Rather different stakeholders have different notions of margins and do not disclose the rationale behind adding margins or the amount that they have added. Margins also enabled designers to avoid design changes as existing components and systems can accommodate new requirements and thereby saving significant design time.

This paper presents the results of studying the brain activations of 30 engineering students when using three different design concept generation techniques: brainstorming, morphological analysis, and TRIZ. Changes in students’ brain activation in the prefrontal cortex were measured using functional near-infrared spectroscopy. The results are based on the area under the curve analysis of oxygenated hemodynamic response as well as an assessment of functional connectivity using Pearson’s correlation to compare students’ cognitive brain activations using these three different ideation techniques. The results indicate that brainstorming and morphological analysis demand more cognitive activation across the prefrontal cortex (PFC) compared to TRIZ. The highest cognitive activation when brainstorming and using morphological analysis is in the right dorsolateral PFC (DLPFC) and ventrolateral PFC. These regions are associated with divergent thinking and ill-defined problem-solving. TRIZ produces more cognitive activation in the left DLPFC. This region is associated with convergent thinking and making judgments. Morphological analysis and TRIZ also enable greater coordination (i.e., synchronized activation) between brain regions. These findings offer new evidence that structured techniques like TRIZ reduce cognitive activation, change patterns of activation and increase coordination between regions in the brain.

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

Generating designs via machine learning has been an on-going challenge in computer-aided design. Recently, deep learning methods have been applied to randomly generate images in fashion, furniture and product design. However, such deep generative methods usually require a large number of training images and human aspects are not taken into account in the design process. In this work, we seek a way to involve human cognitive factors through brain activity indicated by electroencephalographic measurements (EEG) in the generative process. We propose a neuroscience-inspired design with a machine learning method where EEG is used to capture preferred design features. Such signals are used as a condition in generative adversarial networks (GAN). First, we employ a recurrent neural network Long Short-Term Memory as an encoder to extract EEG features from raw EEG signals; this data are recorded from subjects viewing several categories of images from ImageNet. Second, we train a GAN model conditioned on the encoded EEG features to generate design images. Third, we use the model to generate design images from a subject’s EEG measured brain activity. To verify our proposed generative design method, we present a case study, in which the subjects imagine the products they prefer, and the corresponding EEG signals are recorded and reconstructed by our model for evaluation. The results indicate that a generated product image with preference EEG signals gains more preference than those generated without EEG signals. Overall, we propose a neuroscience-inspired artificial intelligence design method for generating a design taking into account human preference. The method could help improve communication between designers and clients where clients might not be able to express design requests clearly.

This work presents a methodology for the generation of novel 3D objects resembling wireframes of building types. These result from the reconstruction of interpolated locations within the learnt distribution of variational autoencoders (VAEs), a deep generative machine learning model based on neural networks. The data set used features a scheme for geometry representation based on a ‘connectivity map’ that is especially suited to express the wireframe objects that compose it. Additionally, the input samples are generated through ‘parametric augmentation’, a strategy proposed in this study that creates coherent variations among data by enabling a set of parameters to alter representative features on a given building type. In the experiments that are described in this paper, more than 150 k input samples belonging to two building types have been processed during the training of a VAE model. The main contribution of this paper has been to explore parametric augmentation for the generation of large data sets of 3D geometries, showcasing its problems and limitations in the context of neural networks and VAEs. Results show that the generation of interpolated hybrid geometries is a challenging task. Despite the difficulty of the endeavour, promising advances are presented.