Generative Design by Reinforcement Learning: Enhancing the Diversity of Topology Optimization Designs. (May 2022)

• Record Type: Journal Article
• Authors: Jang, Seowoo; Yoo, Soyoung; Kang, Namwoo
• Is Part Of: Computer aided design. Volume 146(2022)
• Publication Date: 2022-05
• Subjects: Generative design -- Topology optimization -- Deep learning -- Reinforcement learning -- Design diversity; Computer-aided design -- Periodicals; Engineering design -- Data processing -- Periodicals; Computer graphics -- Periodicals; Conception technique -- Informatique -- Périodiques; Infographie -- Périodiques; Computer graphics; Engineering design -- Data processing; Periodicals; Electronic journals; 620.00420285
• DOI: 10.1016/j.cad.2022.103225
• Languages: English
• ISSNs: 0010-4485

Abstract

Abstract: Generative design refers to computational design methods that can automatically conduct design exploration under constraints defined by designers. Among many approaches, topology optimization-based generative designs aim to explore diverse topology designs, which cannot be represented by conventional parametric design approaches. Recently, data-driven topology optimization research has started to exploit artificial intelligence, such as deep learning or machine learning, to improve the capability of design exploration. This study proposes a reinforcement learning (RL) based generative design process, with reward functions maximizing the diversity of topology designs. We formulate generative design as a sequential problem of finding optimal design parameter combinations in accordance with a given reference design. Proximal Policy Optimization is used as the learning framework, which is demonstrated in the case study of an automotive wheel design problem. To reduce the heavy computational burden of the wheel topology optimization process required by our RL formulation, we approximate the optimization process with neural networks. With efficient data preprocessing/augmentation and neural architecture, the neural networks achieve a generalized performance and symmetricity-reserving characteristics. We show that RL-based generative design produces a large number of diverse designs within a short inference time by exploiting GPU in a fully automated manner. It is different from the previous approach using CPU which takes much more processing time and involving human intervention. Highlights: This study proposes an RL-based generative design framework enhancing the diversity of topology optimized designs. Proximal Policy Optimization as the learning framework is demonstrated in the case study of an automotive wheel design problem. This study proposes a neural network to generate designs without topology optimization process, thus reducing complexity.