Prediction of Process Parameters for Ultra-Precision Optical Processing Based on Dual-Stacked LSTM

Abstract

High-precision and large-aperture optical components have important applications in modern optics and optoelectronics. However, the traditional continuous polishing technology of optical components relies heavily on the processing experience of the processing personnel. The surface shape of the pitch lap is judged by frequent offline measurement of the surface shape of the processing workpiece, and then the processing personnel judges how to adjust the next process parameters through their own experience, which leads to uncertainty of processing time and low processing efficiency. In this paper, based on the historical processing data, including the surface parameters of workpieces and process parameters before and after each processing, a machine learning-based prediction method of process parameters is proposed. At first, taking the surface shape of the pitch lap as the hidden parameter of the model, a time-series mathematical model of the forward and reverse processing processes is constructed. Theoretical and experimental results show that the prediction method in this paper can effectively reduce the processing time and improve the stability of the workpiece quality.