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.

Urban co-creation is an approach to urban design that actively involves stakeholders and end-users in the design process. As designers increasingly use digital tools to manage design information, stakeholders and residents may find it difficult to participate, resulting in a lack of engagement. The emergence of metaverse technologies offers a crucial opportunity to employ user-friendly and collaborative tools, enabling more effective participation. In the study presented in this article, a custom-designed digital game with virtual reality environment was used to facilitate a series of co-creation workshops. The study focused on changes in participants’ experience by comparing baseline and endline survey results against the design outputs. It employed a holistic framework considering four dimensions: game design, participatory experience, learning outcomes and co-creation results. The findings indicate that the digitally gamified approach helped enhance participation and knowledge sharing, and even though game design ratings varied, the use of video games motivated engagement, particularly in an intergenerational context. The co-creation workshop design documented in this article offers new methods to enhance community engagement in urban design. Especially during digital transformation, it opens renewed discussions on balancing traditional output-driven approaches with more participant-centric methods and design objectives.

Importance-performance analysis (IPA) is widely used for needs analysis, product positioning, and strategic planning in product design. Previous research on IPA often employs single-source data such as customer surveys or online reviews with unavoidable subjective bias. In contrast, product maintenance records provide objective information on product quality and failure patterns, which can be cross-validated with customers’ personal experiences from surveys or online reviews. In this paper, we propose an integrated framework for conducting IPA from online reviews and product maintenance records jointly. An attribute-keyword dictionary is first established using keyword extraction and clustering methods. Then, semantic groups, including product attributes and associated descriptions, are extracted using dependency parsing analysis. The sentiment scores of identified product attributes are determined by a voting mechanism using two pre-trained sentiment analysis models. The importance of product attributes in IPA is estimated from the impact of sentiments of each product attribute on product ratings with the extreme gradient boosting (XGBoost) model, while the performance is estimated from the sentiment scores of online reviews or the quality statistics from product maintenance records. In addition, we propose two methods to validate the IPA results, in which the IPA results are compared with the actual product improvements on the market or compared with the analysis of customer reviews from different time periods, respectively. The validated IPA results from online reviews and maintenance records are then integrated to obtain a more comprehensive understanding of customer needs. A case study of passenger vehicles is used to demonstrate the framework. The proposed framework enables automatic data processing and can support companies in making efficient design decisions with more comprehensive perspectives from multisource data.

There is a well-developed view of artifacts according to which their nature depends on the intentions of their authors or creators. However, in the modern world of artifact design and creation, typically not one but many agents are involved in the process of making an artifact. In this paper, I show how the intentional view can be maintained even for ‘collective’ artifacts having multiple authors. My approach is to combine some basic concepts that have been proposed in the study of collective intentionality with a suitable model of artifact creation that takes account of the multiple agents and processes that arise in design, engineering and manufacturing a new or existing product. In this way, we can explain how an artifactual kind can be understood via a form of collective intentionality. For the design sciences, notions such as we-intentionality and group agency can help to model different types of cooperation and, in particular, to reconcile individualism with strong forms of collectivity at a group level.

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.

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.

Improving designers’ ability to identify manufacturing constraints during design can help reduce the time and cost involved in the development of new products. Different design for additive manufacturing (DfAM) tools exist, but the design outcomes produced using such tools are often evaluated without comparison to existing tools. This study addresses the research gap by directly comparing design performance using two design support tools: a worksheet listing DfAM principles and a manufacturability analysis software tool that analyzes compliance with the same principles. In a randomized-controlled study, 49 nonexpert designers completed a design task to improve the manufacturability of a 3D-printed part using either the software tool or the worksheet tool. In this study, design outcome data (creativity and manufacturability) and design process data (task load and time taken) were measured. We identified statistically significant differences in the number of manufacturability violations in the software and worksheet groups and the creativity of the designs with novel build orientations. Results demonstrated limitations associated with lists of principles and highlighted the potential of software in promoting creativity by encouraging the exploration of alternative build orientations. This study provides support for using software to help designers, particularly nonexpert designers who rely on trial and error during design, evaluate the manufacturability of their designs more effectively, thereby promoting concurrent engineering design practices.

Engineered products have economic, environmental, and social impacts, which comprise the major dimensions of sustainability. This paper seeks to explore interactions between design parameters when social impacts are incorporated into the concept development phase of the systems design process. Social impact evaluation is increasing in importance similar to what has happened in recent years with environmental impact consideration in the design of engineered products. Concurrently, research into new airship design has increased. Airships have yet to be reintroduced at a large scale or for a range of applications in society. Although airships have the potential for positive environmental and economic impacts, the social impacts are still rarely considered. This paper presents a case study of the hypothetical introduction of airships in the Amazon region of Brazil to help local farmers transport their produce to market. It explores the design space in terms of both engineering parameters and social impacts using a discrete-event simulation to model the system. The social impacts are found to be dependent not only on the social factors and airship design parameters but also on the farmer-airship system, suggesting that socio-technical systems design will benefit from integrated social impact metric analysis.

Topology optimization has been identified as a powerful tool to improve aircraft structures for many years. Yet, innovative layouts have not been successfully implemented in commercial aircraft for several reasons. One reason identified by our research group is the lack of design constraints during topology optimization, such as buckling stability, which yields complex solutions that are not easily manufacturable. Second, the complexity of the resulting layouts makes integration with other systems highly challenging. With respect to these challenges, we propose a new heuristic layout optimization process: complexity-driven layout exploration for aircraft structures (CD-LEAS). The new process addresses the challenges of complexity and nonlinear constraints, such as buckling, in aircraft structure layout optimization. The novelty of CD-LEAS comes from the integration of a relative complexity metric as a driver to navigate the design space efficiently. Two case studies of commonly used stiffened panels are carried out to showcase the performance of the process. The results show that using complexity to navigate an explicit design space allows our process to quickly output a family of simple, light, stiff and buckling-resistant layouts.

This paper presents a sub-study on participatory design in visual management (VM), bracketed from a larger case study. In this case, VM refers to the recurrent meetings when managers and co-workers use dashboards (VM boards) to continuously organize work activities and performances and contribute to the ongoing development and improvements within the organization. The study focuses on when the managers and co-workers are participating in the process of designing and visualizing work-related information regarding workload for future VM boards. This paper emphasizes collaborative workshops and the creation of moodboards as participatory methods, and the application of theories of metaphorical thinking and conceptual and visual metaphors. The findings show that participants perceive the visual output they create metaphorically, in this case, the moodboards. Such visual outputs represent conceptual and visual metaphors that evoke the participants’ sharing of core concepts and an establishment of stories related to the information to be designed. In turn, the metaphors and the storytelling stress desires, visions, objectives, and themes, besides workplace atmospheres, norms, and values governing the workplace. This understanding translates to shared work experiences where conceptual and visual thinking impact how work teams develop work-related information on VM boards together.

Product prototypes and particularly those that are 3D printed will have mass properties that are significantly different from the product they represent. This affects both functional performance and stakeholder perception of the prototype. Within this work, computational emulation of mass properties for a primitive object (a cube) is considered, developing a baseline numerical method and parameter set with the aim of demonstrating the means of improving feel in 3D printed prototypes. The method is then applied and tuned for three case study products – a games controller, a hand drill and a laser pointer – demonstrating that product mass properties could be numerically emulated to within ~1% of the target values. This was achieved using typical material extrusion technology with no physical or process modification. It was observed that emulation accuracy is dependent on the relative offset of the centre of mass from the geometric centre. A sensitivity analysis is further undertaken to demonstrate that product-specific parameters can be beneficial. With tuning of these values, and with some neglect of practical limitations, emulation accuracy as high as ~99.8% can be achieved. This was shown to be a reduction in error of up to 99.6% relative to a conventional fabrication.

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.

Historically, squeak and rattle (S&R) sounds have been among the top quality problems and a major contributor to the warranty costs in passenger cars. Geometric variation is among the main causes of S&R. Though, geometric variation analysis and robust design techniques have been passively involved in the open-loop design activities in the predesign-freeze phases of car development. Despite the successful application of topometry optimisation to enhance attributes such as weight, durability, noise and vibration and crashworthiness in passenger cars, the implementation of closed-loop structural optimisation in the robust design context to reduce the risk for S&R has been limited. In this respect, the main obstacles have been the demanding computational resources and the absence of quantified S&R risk evaluation methods. In this work, a topometry optimisation approach is proposed to involve the geometric variation analysis in an attribute balancing problem together with the dynamic response of the system. The proposed method was used to identify the potential areas of a door component that needed structural reinforcement. The main objective was to enhance the design robustness to minimise the risk for S&R by improving the system response to static geometrical uncertainties and dynamic excitation.

Archimedes, the founder of statics and hydrostatics, in his mathematics and physics studies, created methods related to his inventions of new machines, for example, the method of mechanical theorems based on his lever invention. He also used the principles of decomposition and replication underlying his heat ray invention, and these two principles permeate his work. Analysis of Archimedes’ work shows how he was perhaps the first to use methodically a strategy for solving diverse complex problems. In this article, we use the term Archimedes Code to encompass the way Archimedes approached problems including those two principles. Archimedes was perhaps the first design theorist and the first to think systematically about how to address design challenges. Furthermore, his work demonstrates the fundamental role of engineering practice in advancing science. The new insights regarding the Archimedes Code and its value in design practice may inspire both design researchers and practitioners.

The evolutionary development of advanced systems (AS) leads to a necessary rethinking of how they can be supported methodically and in terms of processes in product development. Advanced systems engineering (ASE) offers a novel and holistically adaptive approach to facing such challenges in a structured way. However, many of the ASE use cases relate to the development of systems as products, product networks or individual projects. The additional consideration of entire modular product families within AS offers a further decisive advantage for companies, organisations and the people in ASE. By considering modular product families along the entire life cycle in a product family engineering (PFE), the approaches of ASE can bring their impact and potential to additional system levels occurring when considering product families. The systems, which become complex through variety and collaboration, are broken down into their system elements in a structured way and prepared for a common interdisciplinary understanding, as conveyed by ASE. In this paper, the PFE is presented in excerpts using examples of various aspects and points in time of the product’s life as a complementary approach for ASE.

Design education prepares novice designers to solve complex and challenging problems requiring diverse skill sets and an interdisciplinary approach. Hackathons, for example, offer a hands-on, collaborative learning approach in a limited time frame to gain practical experience and develop problem-solving skills quickly. They enable collaboration, prototyping and testing among interdisciplinary teams. Typically, hackathons strongly focus on the solution, assuming that this will support learning. However, building the best product and achieving a strong learning effect may not be related. This paper presents the results of an empirical study that examines the relationship between product quality, learning effect and effort spent in an academic 2-week hackathon. Thirty teams identified user problems in this course and developed hardware and mechatronic products. This study collected the following data: (1) effort spent during the hackathon through task tracking, (2) learning effect through self-assessment by the participants and (3) product quality after the hackathon by an external jury. The study found that the team effort spent has a statistically significant but moderate correlation with product quality. The correlation between product quality and learning effect is statistically insignificant, suggesting that for this setting, there is no relevant association.

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.

Developing an artificial design agent that mimics human design behaviors through the integration of heuristics is pivotal for various purposes, including advancing design automation, fostering human-AI collaboration, and enhancing design education. However, this endeavor necessitates abundant behavioral data from human designers, posing a challenge due to data scarcity for many design problems. One potential solution lies in transferring learned design knowledge from one problem domain to another. This article aims to gather empirical evidence and computationally evaluate the transferability of design knowledge represented at a high level of abstraction across different design problems. Initially, a design agent grounded in reinforcement learning (RL) is developed to emulate human design behaviors. A data-driven reward mechanism, informed by the Markov chain model, is introduced to reinforce prominent sequential design patterns. Subsequently, the design agent transfers the acquired knowledge from a source task to a target task using a problem-agnostic high-level representation. Through a case study involving two solar system designs, one dataset trains the design agent to mimic human behaviors, while another evaluates the transferability of these learned behaviors to a distinct problem. Results demonstrate that the RL-based agent outperforms a baseline model utilizing the first-order Markov chain model in both the source task without knowledge transfer and the target task with knowledge transfer. However, the model’s performance is comparatively lower in predicting the decisions of low-performing designers, suggesting caution in its application, as it may yield unsatisfactory results when mimicking such behaviors.

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.

Innovation, typically spurred by reusing, recombining and synthesizing existing concepts, is expected to result in an exponential growth of the concept space over time. However, our statistical analysis of TechNet, which is a comprehensive technology semantic network encompassing over 4 million concepts derived from patent texts, reveals a linear rather than exponential expansion of the overall technological concept space. Moreover, there is a notable decline in the originality of newly created concepts. These trends can be attributed to the constraints of human cognitive abilities to innovate beyond an ever-growing space of prior art, among other factors. Integrating creative artificial intelligence into the innovation process holds the potential to overcome these limitations and alter the observed trends in the future.