Reinforcement learning-based topology optimization for generative designed lightweight structures.

This study presents an AI-driven generative design framework for creating lightweight, manufacturable mechanical structures. It integrates topology optimization with deep reinforcement learning, specifically the Proximal Policy Optimization (PPO) algorithm, to learn optimal material layouts within a defined design space. The model adheres to strict engineering constraints, including Von Mises stress (≤ 300 MPa) and displacement (≤ 0.