Reinforcement learning method for machining deformation control based on meta-invariant feature space

Precise control of machining deformation is crucial for improving the manufacturing quality of structural aerospace components. In the machining process, different batches of blanks have different residual stress distributions, which pose a significant challenge to machining deformation control. In this study, a reinforcement learning method for machining deformation control based on a meta-invariant feature space was developed. The proposed method uses a reinforcement-learning model to dynamically control the machining process by monitoring the deformation force. Moreover, combined with a meta-invariant feature space, the proposed method learns the internal relationship of the deformation control approaches under different stress distributions to achieve the machining deformation control of different batches of blanks. Finally, the experimental results show that the proposed method achieves better deformation control than the two existing benchmarking methods.