  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.
