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초록
Recent advances in deep learning enable machines to learn existing designs by themselves and to create new designs. Generative adversarial networks (GANs) are widely used to generate new images and data by unsupervised learning. Certain limitations exist in applying GANs directly to product designs. It requires a large amount of data, produces uneven output quality, and does not guarantee engineering performance. To solve these problems, this paper proposes a design automation process by combining GANs and topology optimization. The suggested process has been applied to the wheel design of automobiles and has shown that an aesthetically superior and technically meaningful design can be automatically generated without human interventions. Copyright © 2018 ASME.
키워드
- 제목
- Design automation by integrating generative adversarial networks and topology optimization
- 저자
- Oh, Sangeun; Jung, Yongsu; Lee, Ikjin; Kang, Namwoo
- 발행일
- 2018-08
- 유형
- Conference Paper
- 저널명
- Proceedings of the ASME Design Engineering Technical Conference
- 권
- 2A-2018