Material removal rate optimization with bayesian optimized differential evolution based on deep learning in robotic polishing

Abstract

Large aperture aspheric optical surfaces (LAAOS) have been applied in many industries, but their high requirements for precision and efficiency make manufacturing more challenging. Robotic polishing is a representative computer-controlled optical surfacing technique to manufacture LAAOS with low-cost and high-efficiency. However, how to achieve the highest material removal rate (MRR) involves many process parameters. It is difficult to determine the optimal parameter settings since the complex relationships among them. In this paper, a novel Bayesian optimized differential evolution based on deep learning method is proposed to optimize the MRR, in which the designed deep neural network is responsible for MRR modeling and Bayesian optimized differential evolution is used for MRR optimization. Bayesian optimization is used to find the best hyperparameter of differential evolution method so as to improve optimization performance. To evaluate the proposed method, a series of robotic polishing experiments are conducted to build the MRR model. The optimization performance comparison experiments show the superiority of our proposed method, which increases MRR by an average of 0.16.