The presented study investigates differences in engineering designers' CAD performance when modelling from two types of projections in technical drawings – isometric and orthographic. The results revealed significant differences in the percentage of correctly replicated components' size and shape, indicating better CAD outcomes when generating CAD models from the orthographic projection. In addition, a comparison of duration, as well as the number and type of sketch entities, sketch relations, and CAD features, showed that CAD modelling processes were similar in both conditions.

Managing general domain knowledge and asset-specific information in the form of digital representations is especially important and challenging when focusing on long-living and complex assets. Implicit knowledge and existing structures need to be captured and digitalised, ideally without introducing unnecessary complexity through unfamiliar wording or new structures. To achieve this, a methodical approach that utilises ontologies as well as system modelling techniques and focuses on early-stage model instantiation is presented and applied to the cabin retrofit of aircraft

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.

Designers are experiencing greater mental demands given the complexity of design tools, necessitating the study of cognitive load in design. Researchers have identified task- and designer-related factors that affect cognitive load; however, these studies primarily use self-reported measures that could be inaccurate and incomplete. Little research has tested the accuracy and completeness of self-reported measures and we aim to explore this gap. Towards this aim, we seek to answer the question: How does cognitive load vary based on the different design representations used, and do these differences depend on the measure of cognitive load? From our results, we see that the design representations vary in the range of cognitive load experienced by designers when using them. Moreover, this role of the range of cognitive load variance was observed given our use of pupil diameter. These findings call for the use of a multi-modal approach for measuring cognitive load with the combined use of subjective (e.g., self-report) and objective measures (e.g., physiological measures), as well as the use of both retrospective (e.g., self-report) and concurrent measures (e.g., physiological measures).

Design representations are important tools for designers in the design process. To help designers choose the appropriate representation, taxonomies have been proposed based on type, degree of fidelity, and when to use them. However, Design representations may also play an important role in designers' communication with users and enabling users as co-designers. Therefore, new taxonomies, focusing on design representations’ potential mediating roles in collaborative design processes with users, have been developed. The purpose of this interview study, with twelve designers within Swedish industry, was thus to investigate how designers use design representations in communication with users in the design process. The study indicates that the designers mainly interacted with users in order for them to answer specific design questions or to evaluate design solutions. If design representations’ value for facilitating communication and collaboration with users should be emphasized, we need to shift from teaching mainly taxonomies related to fidelity levels or when to use them in the design process, and rather educate future designers about design representations inherent potential to mediate and enhance the dialogue with users.

During engineering design, different representations are used to convey information about a systems' components, functionality, spatial layout, and interdependencies. These varying representations may have an impact on the interpretation of a system and consequently the decision-making process. This paper presents a research study that tries to capture these different interpretations by investigating how designers divide a system into subsystem clusters. These subsystem clusters can be considered partitions of a set-in combinatorial mathematics. Given designers' subsystem clusters for three products across three representation modalities, three different analysis methods for finding the most likely partition from observed data are presented. Analysis shows that the Variation of Information analysis method gives the most coherent and consistent results for the search of a most likely cluster. In addition, differences in clustering behaviour are observed based on representation modality. These results show that the way an engineer or designer chooses to represent a system impacts how that system is interpreted, which has implications for the decision-making process during engineering design.

Deep learning (DL) from various representations have succeeded in many fields. However, we know little about the machine learnability of distinct design representations when using DL to predict design performance. This paper proposes a graph representation for designs and compares it to the common image representation. We employ graph neural networks (GNNs) and convolutional neural networks (CNNs) respectively to learn them to predict drone performance. GCNs outperform CNNs by 2.6-8.1% in predictive validity. We argue that graph learning is a powerful and generalizable method for such tasks.

This paper explores the notion of mentored reflective practice to describe design studio pedagogy. Students learn how to design by doing design in the studio. Design critiques are a key element in design studio pedagogy as they capture a moment where students get feedback from their tutors on their designs. The research questions addressed concern the roles of each participant during design studio reviews and their interactions with design representations. The protocol analysis methodology and the Function Behavior Structure ontology are utilized to convey our case study analysis.

Coherence is important in the design of products, because it makes them easier to understand for their users. Designers can use different representation modes to express ideas about coherence. However, perception of a representation can be influenced by its mode. Therefore, designers must be aware of the influences that different representation modes can have on perception of coherence.

The aim of the present study was to investigate the effects of experienced modality on perception of conceptual coherence. In the study, participants were divided into two conditions for two representation modes: written word and physical object, both representing the same concepts. Each participant was presented with the concepts as words or objects and asked to list the properties of each concept. The results showed that between the two conditions, frequencies of responses were similar while frequencies of response contents were dissimilar and sometimes contrary. The main findings suggest that the effects of the modalities, written word and physical object, do not differ significantly for mental activity, but do differ considerably for semantic processing in ways that has implications for design.

Many tools are being developed to assist designers in retrieving analogies. One critical question these designers face is how these analogues should be represented in order to minimize design fixation and maximize idea generation. To address this question, an experiment is presented that compares various representations' influence on creativity and design fixation. This experiment presents an effective example (analogue) as computer-aided design (CAD), sketch, or photograph representations. We found that all representations induced fixation, and the degree of fixation did not vary significantly. We also found that CAD representations encourage engineering designers to identify and copy the key effective features of the example. CAD and photo representations also produced a higher quality of design concepts. Results from this experiment offer insights into how these various representations may be used in examples during idea generation; CAD representations appear to offer the greatest advantages during the idea generation process. The results from this experiment also indicate that analogical databases of effective design examples should include CAD and photolike images of the analogue rather than sketches.

Creativity of designers can be enhanced by the application of appropriate triggers. The presence of triggers helps designers to search solution spaces. The searching of a solution space increases the possibility of finding creative solutions. Both representation and content of the triggers or stimulus to which the designers are exposed are believed to play a vital role in the representation and content of the outcome of the designers during problem solving. We studied the effect of representation of triggers on ideas generated by six design engineers while trying to solve a given problem. A variety of representations (video/animation and audio, text, explanation, and others) that are potentially useful to designers for five prespecified triggers were administered to each designer, who generated ideas in response to each trigger–representation combination individually. The effect of representations of these triggers on the content and representation of the solutions generated by the design engineers was studied. The results showed significant influence of the representation of the triggers on the representations, number, and quality of the resulting ideas that were generated.