SmartFixture: Physics-guided reinforcement learning for automatic fixture layout design in manufacturing systems

Fixture layout design critically impacts the shape deformation of large-scale sheet parts and the quality of the final product in the assembly process. The existing works focus on developing Mathematical-Optimization (MO)-based methods to generate the optimal fixture layout via interacting with Finite Element Analysis (FEA)-based simulations or its surrogate models. Their limitations can be summarized as memorylessness and lack of scalability. Memorylessness indicates that the experience in designing the fixture layout for one part is usually not transferable to others. Scalability becomes an issue for MO-based methods when the design space of fixtures is large. Furthermore, the surrogate models might have limited representation capacity when modeling high-fidelity simulations. To address these limitations, we propose a learning-based framework, SmartFixture, to design the fixture layout by training a Reinforcement learning agent through direct interaction with the FEA-based simulations. The advantages of the proposed framework include: (i) it is generalizable to design fixture layouts for unseen scenarios after offline training; (ii) it is capable of finding the optimal fixture layout over a massive search space. Experiments demonstrate that the proposed framework consistently generates the best fixture layouts that receive the smallest shape deformations on the sheet parts with different initial shape variations.