Human-Centered Generative Design Framework: An Early Design Framework to Support Concept Creation and Evaluation

Abstract

Generative design uses artificial intelligence-driven algorithms to create and optimize concept variants that meet or exceed performance requirements beyond what is currently possible using the traditional design process. However, current generative design tools lack the integration of human factors, which diminishes the efforts to understand and inject a broad set of human capabilities, limitations, and potential emotional responses for future human-centered product and service innovation. This paper demonstrates collaborative research in formulating a human-centered generative design framework that injects human factors early in the design for quick-and-dirty concept creation and evaluation. Three case studies overviewing our ongoing multidisciplinary research efforts in synthesizing human and mechanical attributes are presented. The results show that the framework has the potential to enhance human factors representation within generative design workflow. Strategies from a computational design perspective, such as data-driven generative design, digital human modeling, and mixed-reality validation, are discussed as alternative approaches that could be implemented to augment designers.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 In this paper, authentic shapes are referred to as products that are reasonable and realistic to consumers unless otherwise specified.

2 Obtained from https://wiki.freecadweb.org/Main_Page

Additional information

Funding

The work presented in this manuscript is based upon work supported by the National Science Foundation (NSF) Grant No. 2207408. Any opinions, findings, conclusions, or recommendations expressed in this paper are those of the authors and do not reflect the views of the funding bodies.

Notes on contributors

H. Onan Demirel

H. Onan Demirel is an Assistant Professor of Mechanical Engineering at Oregon State University. His research interests lie at the intersection of human factors engineering, engineering design, and systems engineering. His work focuses on developing human-centered design methodologies. He received his Ph.D., M.S., and B.S. degrees from Purdue University.

Molly H. Goldstein

Molly H. Goldstein is a Teaching Assistant Professor in Industrial and Enterprise Systems Engineering at the University of Illinois. Her research focuses on student designer trade-off decisions. She earned her B.S. and M.S. from the University of Illinois and a Ph.D. in Engineering Education at Purdue University in 2018.

Xingang Li

Xingang Li is a Ph.D. student in the Walker Department of Mechanical Engineering at the University of Texas at Austin. His research interests include generative design, deep learning for conceptual engineering, and human-AI collaboration. Before joining the Ph.D. program, he worked in the auto industry at OTICS Corporation in Japan.

Zhenghui Sha

Zhenghui Sha is an Assistant Professor in the Walker Department of Mechanical Engineering at the University of Texas at Austin. His research focuses on system science and design science, as well as the intersection between these two areas. He received a Ph.D. from Purdue University in Mechanical Engineering.