Search

Design thinking is essential to the success of a design process as it helps achieve the design goal by guiding design decision-making. Therefore, fundamentally understanding design thinking is vital for improving design methods, tools and theories. However, interpreting design thinking is challenging because it is a cognitive process that is hidden and intangible. In this paper, we represent design thinking as an intermediate layer between human designers’ thought processes and their design behaviors. To do so, this paper first identifies five design behaviors based on the current design theories. These behaviors include design action preference, one-step sequential behavior, contextual behavior, long-term sequential behavior, and reflective thinking behavior. Next, we develop computational methods to characterize each of the design behaviors. Particularly, we use design action distribution, first-order Markov chain, Doc2Vec, bi-directional LSTM autoencoder, and time gap distribution to characterize the five design behaviors. The characterization of the design behaviors through embedding techniques is essentially a latent representation of the design thinking, and we refer to it as design embeddings. After obtaining the embedding, an X-mean clustering algorithm is adopted to each of the embeddings to cluster designers. The approach is applied to data collected from a high school solar system design challenge. The clustering results show that designers follow several design patterns according to the corresponding behavior, which corroborates the effectiveness of using design embedding for design behavior clustering. The extraction of design embedding based on the proposed approach can be useful in other design research, such as inferring design decisions, predicting design performance, and identifying design actions identification.

During a design process, designers iteratively go back and forth between different design stages to explore the design space and search for the best design solution that satisfies all design constraints. For complex design problems, human has shown surprising capability in effectively reducing the dimensionality of design space and quickly converging it to a reasonable range for algorithms to step in and continue the search process. Therefore, modeling how human designers make decisions in such a sequential design process can help discover beneficial design patterns, strategies, and heuristics, which are important to the development of new algorithms embedded with human intelligence to augment computational design. In this paper, we develop a deep learning based approach to model and predict designers’ sequential decisions in a system design context. The core of this approach is an integration of the function-behavior-structure model for design process characterization and the long short term memory unit model for deep leaning. This approach is demonstrated in a solar energy system design case study, and its prediction accuracy is evaluated benchmarked on several commonly used models for sequential design decisions, such as Markov Chain model, Hidden Markov Chain model, and random sequence generation model. The results indicate that the proposed approach outperforms the other traditional models. This implies that during a system design task, designers are very likely to reply on both short-term and long-term memory of past design decisions in guiding their decision making in future design process. Our approach is general to be applied in many other design contexts as long as the sequential design action data is available.

Design is essentially a decision-making process, and systems design decisions are sequentially made. In-depth understanding on human sequential decision-making patterns in design helps discover useful design heuristics to improve existing algorithms of computational design. In this paper, we develop a framework for clustering designers with similar sequential design patterns. We adopt the Function-Behavior-Structure based design process model to characterize designers’ action sequence logged by computer-aided design (CAD) software as a sequence of design process stages. Such a sequence reflects designers’ thinking and sequential decision making during the design process. Then, the Markov chain is used to quantify the transitions between design stages from which various clustering methods can be applied. Three different clustering methods are tested, including the K-means clustering, the hierarchical clustering and the network-based clustering. A verification approach based on variation of information is developed to evaluate the effectiveness of each method and to identify the clusters of designers who show strong behavioral similarities. The framework is applied in a solar energy systems design problem — energy-plus home design. The case study shows that the proposed framework can successfully cluster designers and identify their sequential decision-making similarities and dissimilarities. Our framework can support the studies on the correlation between potential factors (e.g., designers’ demographics) and certain design behavioral patterns, as well as the correlation between behavioral patterns and design quality to identify beneficial design heuristics.