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

Abstract

Fixture layout design critically impacts the shape deformation of large-scale sheet parts and the quality of the final product in the assembly process. Existing works focus on Mathematical-Optimization (MO)-based methods to generate the optimal fixture layout via interacting with Finite Element Analysis (FEA)-based simulations or their surrogate models. Their limitations can be summarized as memorylessness and lack of scalability. Memorylessness indicates that 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 FEA-based simulations. The proposed framework is generalizable to unseen scenarios after offline training and can find optimal fixture layouts over a massive search space. Experiments demonstrate that SmartFixture consistently generates the best fixture layouts that receive the smallest shape deformations on sheet parts with different initial shape variations.

Juan Du

Assistant Professor

My research interests include knowledge-infused data science for quality improvement, industrial data analytics and machine learning, and system informatics and control for manufacturing applications.