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1. Investigating Information: A Qualitative Analysis of Expert Designers’ Information Representation and Structuring Behaviors

   https://doi.org/10.1115/1.4046647  

   Damen, Nicole B. ; Toh, Christine ( August 2021 , Journal of Mechanical Design)

   null (Ed.)

   Abstract Information organization and utilization are integral to the design and development of creative ideas. However, navigating this often complex information space can be challenging, even for experienced designers. Therefore, a deep analysis of how expert software designers utilize and organize information is needed to provide qualitative insights into their information organization strategies. To address this, four professionals in the software design and development field were recruited for individual 3-hour design sessions. They were asked to generate ideas for a design challenge (reducing distraction-based pedestrian accidents) using information sheets specifically developed to contain different types of information, as identified by prior work. Results reveal individual differences in information approach and categorization, although these were motivated by similar underlying patterns of evaluating the relevance of the information for its ability to inform the project constraints, resources, or (user) requirements. Designer experience and use of design processes and knowledge transfer tools enhanced their ability to turn information into insights. 

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2. Investigating Information: A Qualitative Analysis of Expert Designers’ Information Representation and Structuring Behaviors

   https://doi.org/10.1115/DETC2019-98362  

   Damen, Nicole B. ; Toh, Christine A. ( August 2019 , International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   Information organization and utilization are integral to the design and development of creative ideas. However, navigating this often complex information space can be challenging, even for experienced designers. Therefore, deep analysis of how expert designers utilize and organize information is needed to provide qualitative insights into their information organization strategies. To address this, four professionals in the software design and development field were recruited for individual 3-hour design sessions. They were asked to generate ideas for a design challenge (reducing distraction-based pedestrian accidents) using information sheets specifically developed to contain different types of information, as identified by prior work. Results reveal individual differences in information approach and categorization, although these were motivated by similar underlying patterns of evaluating the relevance of information for its ability to inform the project constraints, resources or (user) requirements. Designer experience and use of design processes and knowledge transfer tools enhanced their ability to turn information into insights.

    

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3. Favoring Complexity: A Mixed Methods Exploration of Factors That Influence Concept Selection When Designing for Additive Manufacturing

   https://doi.org/10.1115/1.4050303  

   Prabhu, Rohan ; Leguarda, Rainmar L. ; Miller, Scarlett R. ; Simpson, Timothy W. ; Meisel, Nicholas A. ( October 2021 , Journal of Mechanical Design)

   null (Ed.)

   Abstract The capabilities of additive manufacturing (AM) open up designers’ solution space and enable them to build designs previously impossible through traditional manufacturing (TM). To leverage this design freedom, designers must emphasize opportunistic design for AM (DfAM), i.e., design techniques that leverage AM capabilities. Additionally, designers must also emphasize restrictive DfAM, i.e., design considerations that account for AM limitations, to ensure that their designs can be successfully built. Therefore, designers must adopt a “dual” design mindset—emphasizing both, opportunistic and restrictive DfAM—when designing for AM. However, to leverage AM capabilities, designers must not only generate creative ideas for AM but also select these creative ideas during the concept selection stage. Design educators must specifically emphasize selecting creative ideas in DfAM, as ideas perceived as infeasible through the traditional design for manufacturing lens may now be feasible with AM. This emphasis could prevent creative but feasible ideas from being discarded due to their perceived infeasibility. While several studies have discussed the role of DfAM in encouraging creative idea generation, there is a need to investigate concept selection in DfAM. In this paper, we investigated the effects of four variations in DfAM education: (1) restrictive, (2) opportunistic, (3) restrictive followed by opportunistic (R-O), and (4) opportunistic followed by restrictive (O-R), on students’ concept selection process. We compared the creativity of the concepts generated by students to the creativity of the concepts they selected. The creativity of designs was measured on four dimensions: (1) uniqueness, (2) usefulness, (3) technical goodness, and (4) overall creativity. We also performed qualitative analyses to gain insight into the rationale provided by students when making their design decisions. From the results, we see that only teams from the restrictive and dual O-R groups selected ideas of higher uniqueness and overall creativity. In contrast, teams from the dual R-O DfAM group selected ideas of lower uniqueness compared with the mean uniqueness of ideas generated. Finally, we see that students trained in opportunistic DfAM emphasized minimizing build material the most, whereas those trained only in restrictive DfAM emphasized minimizing build time. These results highlight the need for DfAM education to encourage AM designers to not just generate creative ideas but also have the courage to select them for the next stage of design. 

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4. Iterative transformations for deeper exploration during concept generation

   https://doi.org/10.1007/s10798-023-09813-1  

   Clancy, Shannon M. ; Murphy, Laura R. ; Daly, Shanna R. ; Seifert, Colleen M. ( March 2023 , International Journal of Technology and Design Education)

   Engineering designers often generate multiple concepts to increase novelty and diversity among early solution candidates. Many past studies have focused on creating new concepts “from scratch;” however, designers at every level become fixated on their initial designs and struggle to generate different ideas. In line with prior work on design transformations, we propose a concept generation process ofiterative transformationto create new ideas by intentionally introducing major changes in form, nature, or function to an existing concept. A study of this concept generation process recruited beginning engineering students likely to benefit from an alternative to blank slate generation. Working alone in a single test session, students generated an initial concept for a presented design problem. Then, they were instructed to generate another concept by transforming their initial design into a new concept and repeated this process to create three more concepts. In a second design round, students were asked to consider 7 Design Heuristics strategies to prompt possible transformations for their concepts. Beginning again with their initial concept, each student generated another set of four transformed concepts using iterative transformation. The analysis considered 60 initial concepts and 476 transformed concepts with and without the use of Design Heuristics. We createdDesign Transformation Diagramsto observe links (sequential, non-sequential, or both) between transformed concepts within each set of four concepts and between the two sets. Three patterns across the diagrams were identified: Fully Sequential, Sequential with Deviation, and Divergent. When aided by Design Heuristics, transformations included more non-sequential links, suggesting synthesis, refinement, and extension of other prior concepts, and resulting in more varied and distinct transformations. This iterative transformation process may support more diversity in concepts generated through a deeper exploration of related concepts without requiring an escape from the influence of existing concepts. Concept generation strategies like Design Heuristics may support engineering students as they learn to expand their early exploration of design concepts.

    

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5. Predicting human design decisions with deep recurrent neural network combining static and dynamic data

   https://doi.org/10.1017/dsj.2020.12  

   Rahman, Molla Hafizur ; Yuan, Shuhan ; Xie, Charles ; Sha, Zhenghui ( January 2020 , Design Science)

   Computational modeling of the human sequential design process and successful prediction of future design decisions are fundamental to design knowledge extraction, transfer, and the development of artificial design agents. However, it is often difficult to obtain designer-related attributes (static data) in design practices, and the research based on combining static and dynamic data (design action sequences) in engineering design is still underexplored. This paper presents an approach that combines both static and dynamic data for human design decision prediction using two different methods. The first method directly combines the sequential design actions with static data in a recurrent neural network (RNN) model, while the second method integrates a feed-forward neural network that handles static data separately, yet in parallel with RNN. This study contributes to the field from three aspects: (a) we developed a method of utilizing designers’ cluster information as a surrogate static feature to combine with a design action sequence in order to tackle the challenge of obtaining designer-related attributes; (b) we devised a method that integrates the function–behavior–structure design process model with the one-hot vectorization in RNN to transform design action data to design process stages where the insights into design thinking can be drawn; (c) to the best of our knowledge, it is the first time that two methods of combining static and dynamic data in RNN are compared, which provides new knowledge about the utility of different combination methods in studying sequential design decisions. The approach is demonstrated in two case studies on solar energy system design. The results indicate that with appropriate kernel models, the RNN with both static and dynamic data outperforms traditional models that only rely on design action sequences, thereby better supporting design research where static features, such as human characteristics, often play an important role. 

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