Board 411: Thinking Inversely in Engineering Design: Towards an Operational Definition of Generative Design Thinking

Dr. Molly H. Goldstein is a Teaching Assistant Professor and Product Design Lab Director in Industrial and Enterprise Systems Engineering at the Grainger College at the University of Illinois. She is also courtesy faculty in Mechanical Science and Engineering, Curriculum & Instruction (College of Education) and Industrial Design (School of Fine and Applied Arts). Dr. Goldstein’s research focuses on student designers through the study of their design actions and thinking.

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Dr. Zhenghui Sha is an Assistant Professor in the Walker Department of Mechanical Engineering at the University of Texas at Austin.

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Abstract

Generative design (GD) is a computational design method in which computer algorithms generate design options for human designers while considering design goals and certain constraints, e.g., from the material, manufacturing, and cost perspectives. In a typical GD process, artificial intelligence (AI) explores all the possible permutations in the design space and tests and learns which alternatives could work according to the pre-defined objectives and constraints. Once various design options are generated, human designers enter the decision loop by choosing the solution that best suits the goal and constraints. The GD process requires inverse thinking from the objective space point of view, which differs from the traditional design (TD) process, where designers actively explore design alternatives via cognitive idea generation iteration in the design space. Therefore, GD requires the designer to use a different set of cognitive skills than TD. However, there has been little research done on an operational definition of Generative Design Thinking (GDT), e.g., specifying the cognitive processes necessary to gauge the efficacy of a designer’s thinking in the GD process.

This study aims to define GDT as a unique design thinking and methodology within engineering design. A robust definition of GDT and a description of the cognitive processes that make up GDT are important for two primary reasons. First, it is essential for design education, including curriculum development, to foster next-generation engineers who know how to tackle design challenges using generative design methodology and related computational tools. As GD is gaining an interest in the industry, the engineers involved with the GD methodology earlier are likely to succeed in tackling design problems. Second, research into the cognitive processes that make up GD can be beneficial in developing AI design agents that can augment designers in the conceptual design phase. For example, overcoming design fixation enable designers to be competent at the human-technology frontier of the future.

To achieve this goal, we review the literature on related design thinking concepts, such as engineering design thinking, engineering systems thinking, computational thinking, and parametric design thinking, to identify the similarities and differences toward reaching a comprehensive understanding of the uniqueness of GDT. To facilitate the literature review process, we developed an Evolving Design Thinking (EDT) model as a meta-representation to connect different design thinking concepts at three different levels: technology, methodology, and psychology. With the EDT model, we will examine the influence of technological development on the formation of design thinking, the cognitive competencies associated with each design thinking, and the design methodologies implementing the corresponding design thinking. The review contributes new knowledge to the existing literature by providing an in-depth understanding of GDT and a clarification of the obscure boundary between various design thinking concepts.

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Clay, J. Z., & Li, X., & Demirel, H. O., & Goldstein, M. H., & Jiang, R., & Xie, C., & Zabelina, D., & Sha, Z. (2023, June), Board 411: Thinking Inversely in Engineering Design: Towards an Operational Definition of Generative Design Thinking Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--42724

TY  - CPAPER
AB  - Generative design (GD) is a computational design method in which computer algorithms generate design options for human designers while considering design goals and certain constraints, e.g., from the material, manufacturing, and cost perspectives. In a typical GD process, artificial intelligence (AI) explores all the possible permutations in the design space and tests and learns which alternatives could work according to the pre-defined objectives and constraints. Once various design options are generated, human designers enter the decision loop by choosing the solution that best suits the goal and constraints. The GD process requires inverse thinking from the objective space point of view, which differs from the traditional design (TD) process, where designers actively explore design alternatives via cognitive idea generation iteration in the design space. Therefore, GD requires the designer to use a different set of cognitive skills than TD. However, there has been little research done on an operational definition of Generative Design Thinking (GDT), e.g., specifying the cognitive processes necessary to gauge the efficacy of a designer’s thinking in the GD process.

This study aims to define GDT as a unique design thinking and methodology within engineering design. A robust definition of GDT and a description of the cognitive processes that make up GDT are important for two primary reasons. First, it is essential for design education, including curriculum development, to foster next-generation engineers who know how to tackle design challenges using generative design methodology and related computational tools. As GD is gaining an interest in the industry, the engineers involved with the GD methodology earlier are likely to succeed in tackling design problems. Second, research into the cognitive processes that make up GD can be beneficial in developing AI design agents that can augment designers in the conceptual design phase. For example, overcoming design fixation enable designers to be competent at the human-technology frontier of the future.

To achieve this goal, we review the literature on related design thinking concepts, such as engineering design thinking, engineering systems thinking, computational thinking, and parametric design thinking, to identify the similarities and differences toward reaching a comprehensive understanding of the uniqueness of GDT. To facilitate the literature review process, we developed an Evolving Design Thinking (EDT) model as a meta-representation to connect different design thinking concepts at three different levels: technology, methodology, and psychology. With the EDT model, we will examine the influence of technological development on the formation of design thinking, the cognitive competencies associated with each design thinking, and the design methodologies implementing the corresponding design thinking. The review contributes new knowledge to the existing literature by providing an in-depth understanding of GDT and a clarification of the obscure boundary between various design thinking concepts.
AU  - John Zachary Clay
AU  - Xingang Li
AU  - H. Onan Demirel
AU  - Molly H Goldstein
AU  - Rundong Jiang
AU  - Charles Xie
AU  - Darya Zabelina
AU  - Zhenghui Sha
CY  - Baltimore , Maryland
DA  - 2023/06/25
PB  - ASEE Conferences
TI  - Board 411: Thinking Inversely in Engineering Design: Towards an Operational Definition of Generative Design Thinking
UR  - https://peer.asee.org/42724
DO  - 10.18260/1-2--42724
ER  -

Board 411: Thinking Inversely in Engineering Design: Towards an Operational Definition of Generative Design Thinking

Paper Authors

John Zachary Clay The University of Texas, Austin

Xingang Li The University of Texas, Austin

H. Onan Demirel

Molly H Goldstein University of Illinois, Urbana - Champaign orcid.org/0000-0002-2382-4745

Rundong Jiang

Charles Xie

Darya Zabelina

Zhenghui Sha The University of Texas, Austin orcid.org/0000-0003-3267-0941

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