3D printing technologies, such as material extrusion (MEX), hold the potential to revolutionise manufacturing by providing individuals without traditional manufacturing capabilities with powerful and affordable resources. However, widespread adoption is impeded by the lack of user-friendly design tools due to the necessity of domain-specific expertise in computer-aided design (CAD) software and the overwhelming level of design freedom afforded by the MEX process. To overcome these barriers and facilitate the democratisation of design (DoD), this article introduces an innovative, generative-based design (GBD) methodology aimed at enabling non-technical users to create functional components independently. The novelty of this methodology lies in its capacity to simplify complex design tasks, making them more accessible to non-designers. The proposed methodology was tested in the design of a load-bearing part, yielding a functional component within two design iterations. A comparative analysis with the conventional CAD-based process revealed that the GBD methodology enables the DoD, reflected in a 68% reduction in design activities and a decrease in design difficulty of 62% in requisite know-how and a 55% in understanding. Through the creation and implementation of this methodology, the article demonstrates a pioneering integration of state-of-the-art techniques of generative design with design repositories enabling effective co-design with non-designers.

A radical shift in technology is necessary to enable future air transport solutions. Sustainability targets for aeroengine manufacturing mean more than reducing CO2 and NOX. The future will open up possibilities and bring new challenges when introducing hybrid- and electrical propulsion technologies using new materials, technology solutions and new business models. This article reports on findings from a longitudinal study and many years of collaboration between researchers and industry experts, where a first-tier aeroengine manufacturer transforms their product development capabilities to enable sustainable product development. The article highlights some activities undertaken and identifies critical challenges and opportunities remaining for a manufacturer of next-generation aeroengine solutions. It is argued that the challenge for aeroengine manufacturers to develop new-generation propulsive technologies will require a systemic change in the undertaking of design and development. The opportunities of sustainable technologies are evident yet require: (1) means to tighter integrate business and technology development, (2) the ability to quantify and assess sustainability impacts of different concept solutions, and (3) means to utilise natural resources, alloys and materials for a circular and life-cycle optimised solution.

In order to humanize forecasting, communities have been proposed to activate and enlarge a collective ability of foresight. To better understand how communities relate to collective foresight abilities, this article untangles its critical modes, roles and social media involved. Based on a fine-grained analysis of 10 community practices, we uncovered the abilities of capturing, conceiving and designing foresights enacted in the distinct modes of creative, user and strategic communities. Discoveries included the novel abilities of conceiving foresights, a new groundbreaker role for strategic designers and specific activities of social media listening with regard to future interests. Grounded on the prime findings, we propose a framework with propositions that shape further theory development on community abilities of designing foresights. Further research directions are outlined.

Engineers must engage project stakeholders effectively if stakeholder needs are to be met, and prototypes are key tools for communicating design form and function. Quality stakeholder engagement in the front end of design processes, in particular, is critical in the success or failure of design projects. As remote stakeholder engagement has become increasingly common as industry trends toward distributed design, there is a need to develop the theory and practices behind effective remote design processes, which have not yet been as well-studied as in-person design. This study explored the prototyping strategies for remote stakeholder engagement during front-end design used by 10 engineering practitioners and 10 senior engineering students through semi-structured interviews. Prototyping strategies were found to overlap with many of the strategies described by prior literature that are not specific to remote engagement modes, though several of these strategies were adapted to the remote context, and three emergent strategies for prototyping in remote engagements were identified. Designers’ perceptions of remote versus in-person prototyping strategies for stakeholder engagement in front-end design, including perceived advantages and limitations, were also explored, and recommendations for educators to better prepare engineering students for hybrid and remote work are provided.

Over the past years, computational methods based on deep learning—that is, machine learning with multilayered neural networks—have become state-of-the-art in main research areas in computer-aided architectural design (CAAD). To understand current trends of CAAD with deep learning, to situate them in a broader historical context, and to identify future research challenges, this article presents a systematic review of publications that apply neural networks to CAAD problems. Research papers employing neural networks were collected, in particular, from CumInCad a major open-access repository of the CAAD community and categorized into different types of research problems. Upon analyzing the distribution of the papers in these categories, namely, the composition of research subjects, data types, and neural network models, this article suggests and discusses several historical and technical trends. Moreover, it identifies that the publications analyzed typically provide limited access to important research components used as part of their deep learning methods. The article points out the importance of sharing training experiments and data, of describing the dataset, dataset parameters, dataset samples, model, learning parameters, and learning results to support reproducibility. It proposes a guideline that aims at increasing the quality and availability of CAAD research with machine learning.

Prototyping is a knowledge generation activity facilitating improved understanding of problem and solution spaces. This knowledge can be generated across a range of dimensions, termed knowledge dimensions (KDs), via a range of methods and media, each with their own inherent properties. This article investigates and characterises the relationships between prototypes and knowledge generated from prototyping activities during the design process, by establishing how different methods and media contribute across KDs. In so doing, it provides insights into prototyping activity, as well as affording a means by which prototyping knowledge generation may be studied in detail. The investigation considers sets of prototypes from eight parallel 16-week design projects, with subsequent investigation of the knowledge contributions that each prototype provides and at what stage of the design process. Results showed statistical significance supporting three inferences: i) teams undertaking the same design brief create similar knowledge profiles; ii) prototyping fidelity impacts KD contribution and iii) KDs align with the different phases of the project. This article demonstrates a means to describe and potentially prescribe knowledge generation activities through prototyping. Correspondingly, the article contends that consideration of KDs offers potential to improve aspects of the design process through better prototyping method selection and sequencing.

Data-driven generative design (DDGD) methods utilize deep neural networks to create novel designs based on existing data. The structure-aware DDGD method can handle complex geometries and automate the assembly of separate components into systems, showing promise in facilitating creative designs. However, determining the appropriate vectorized design representation (VDR) to evaluate 3D shapes generated from the structure-aware DDGD model remains largely unexplored. To that end, we conducted a comparative analysis of surrogate models’ performance in predicting the engineering performance of 3D shapes using VDRs from two sources: the trained latent space of structure-aware DDGD models encoding structural and geometric information and an embedding method encoding only geometric information. We conducted two case studies: one involving 3D car models focusing on drag coefficients and the other involving 3D aircraft models considering both drag and lift coefficients. Our results demonstrate that using latent vectors as VDRs can significantly deteriorate surrogate models’ predictions. Moreover, increasing the dimensionality of the VDRs in the embedding method may not necessarily improve the prediction, especially when the VDRs contain more information irrelevant to the engineering performance. Therefore, when selecting VDRs for surrogate modeling, the latent vectors obtained from training structure-aware DDGD models must be used with caution, although they are more accessible once training is complete. The underlying physics associated with the engineering performance should be paid attention. This paper provides empirical evidence for the effectiveness of different types of VDRs of structure-aware DDGD for surrogate modeling, thus facilitating the construction of better surrogate models for AI-generated designs.

Virtual reality (VR) sketching has many advantages for product design and tends to be more and more used among designers and non-designers (end-users). Nevertheless, few studies have focused on the skills needed to use VR sketching for non-designers especially VR novices in VR software. This study focuses on identifying the cognitive impact of VR sketching compared to traditional sketching on VR expert and VR novice in an experimental setting. Thirty-one participants composed of VR experts (N = 15) and VR novices (N = 16) completed a mental rotation test and then performed one traditional paper and pencil sketching task and two VR sketching tasks. We also measured the participants’ movements when using the VR sketching. Results show that VR experts perform better than VR novices in VR sketching because training is an essential element for the quality of traditional and VR sketching. Nevertheless, VR novices with previous training in traditional drawing and/or high mental rotation skills will be able to produce good-quality sketches. In addition, the results show that users moving more in the immersive environment performed better quality sketches if the drawing requires more complex shapes. Our results suggest that VR sketching can be complex to use for a part of the population that may be end-users, especially for those with little experience in traditional and VR sketching and with poor visuospatial abilities. We, therefore, advise to check the non-designers’ prior skills, otherwise, it will be necessary to train these users in VR sketching.

Developing effective design solutions requires successful idea generation, development and selection. Studies have demonstrated that engineering students face challenges in these idea phases and may struggle to implement recommended practices, hindering the potential for an innovative outcome. The first part of the study investigated student practices in idea generation, development and selection through think-aloud experimental sessions and post-session interviews. Data analysis from mechanical engineering students’ sessions, with think-aloud and interview data, revealed that students focused on existing ideas, assumed requirements that constrained their divergence, limited their development of ideas and did not engage much in idea selection. Then, in the second phase of the study, we implemented a learning intervention that leveraged research-based education practices to examine student adoption of recommended practices. After engaging with the learning blocks, students generated unconventional ideas, abstained from requirement assumptions early in ideation, generated a larger quantity of ideas, developed ideas intentionally and used more rigorous idea selection methods. These outcomes demonstrated that a relatively short and targeted intervention can support students in leveraging recommended approaches to idea generation, development and selection.

This article explores the ability of ChatGPT to function as a virtual colleague in helping to design materials for higher education design students. Using a self-study methodology, two university educators attempted to collaborate with ChatGPT to create course materials targeted at higher education design students, before reflecting on its strengths and weaknesses during the process. Contextualising ChatGPT as the latest acute example of digital disruptors that design practices and processes have faced, the authors evaluated its current and potential threats and opportunities for the creation of design-focused learning content. The authors found that ChatGPT was a competent partner with regard to saving time, structuring textual content and documentation, and as a brainstorming tool. However, ChatGPT’s weaknesses included content generation that was often generic, usually requiring much human prompting, cajoling, and manual editing to produce desirable outcomes. Overall, ChatGPT was found to excel at its stated functionality as a language model, with some potentially useful functionality for the creation of higher education design course materials and outlines, as well as limitations. The reflections discussed can be used to inform design educators who may want to work with ChatGPT when designing course materials. However, acknowledging limitations and potential ethical challenges, the authors’ caution that educators may have to evaluate for themselves whether ChatGPT’s potential advantages outweigh its disadvantages.

Caused by the progressing digitalization in mechanical engineering, a significant demand for information about characteristic process and state variables of technical systems arises. However, since it is oftentimes neither obvious what nor how to measure, the integration of measuring functions, in particular in terms of a retrofit, represents a current challenge in mechanical engineering. In order to overcome this challenge, an approach for the systematic identification of potential measurands is provided in this contribution. For this purpose, the approach of physical effect catalogs is taken up and used for the systematic identification of potential measurands, starting from a physical variable to be determined. Existing catalog systems have two major limitations with respect to the intended identification of cause–effect relationships: They assume an effect to be realized and a consideration of design parameters of a technical system is not intended. These limitations are overcome by linking the fundamental idea of existing catalog systems with the basics of multipole-based modeling. In this way, a multipole-based effect catalog system is developed. It creates the foundation to systematically include the changes and transformations of a process or state variable to be determined into the identification of potential measurands.

Design representations play a crucial role in facilitating communication between individuals in design. Sketches and physical prototypes are frequently used to communicate design concepts in early-stage design. However, we lack an understanding of the communicative benefits each representation provides and how these benefits relate to the effort and resources required to create each representation. A mixed-methods study was conducted with 44 participants to identify whether sketches and physical prototypes led to different levels of cognitive load perceived by a communicator and listener and the characteristics that shape their cognitive load during communication. Results showed that listeners perceived higher levels of mental and physical demands when understanding ideas as low-fidelity physical prototypes, as compared to sketches. No significant differences were found in the cognitive load levels of communicators between the two conditions. Qualitative analyses of post-task semi-structured interviews identified five themes relating to verbal explanations and visual representations that shape designers’ cognitive load when understanding and communicating ideas through design representations. Results indicate that designers should be aware of the specific objectives they seek to accomplish when selecting the design representation used to communicate. This work contributes to the knowledge base needed for designers to use design representations more effectively as tools for communication.

Product graphics interchange formats (GIFs) employ this format to show the features of the product and make up for the lack of physical experience online. These GIFs have been widely applied in domains such as e-shopping and social media, aiming to interest and impress viewers. Contrary to this wide application, most designers in this domain lack expertise and produce GIFs of varied quality. Moreover, the knowledge of techniques to enhance viewers’ engagement with product GIFs is also lacking. To bridge the gap, we conducted a series of studies. First, we collected and summarized seven design factors referring to existing literature and semi-structured interviews. Then, the impacts of these design factors were revealed through an online study with 106 product GIFs among 307 participants. The results showed that visual-related factors such as color contrast and moving intensity mainly impact viewers’ interest, while content-related factors such as scenario and style matching impact viewers’ impressions. The simplicity of GIFs also impressed viewers with a quick viewing mode. Finally, we conducted a workshop and verified that these results support large-scale production of product GIFs. Our studies might support the codesign methods of product GIFs and enhance their quality in design practice.

This study aimed to evaluate the effectiveness of using a social science mixed-methods approach to stimulate creativity and improve the attainment of creative outcomes in teaching design. In a focused study framed through a design collaborative experiment within a classroom context, sketches produced by a sample of 351 students were analyzed and the impact of stimuli was categorized by visual, physical, quantitative and contextual information on creative processes and outcomes in product design. Sixteen combinations of these stimuli were integrated as parameters of design briefs (DBs) given to the participating students. This research was augmented with a survey to understand participants’ perceptions and reactions and was rated by expert judges. The results demonstrate that certain combinations of quantitative and qualitative stimuli have a positive impact on creative processes and outcomes. These findings will inform new techniques for engaging and inspiring students in design studies.