Prediction Model of Material Removal for Polishing Single Crystal Silicon by Cluster Magnetorheological Finishing with Dynamic Magnetic Fields Based on BP Neural Network

Magnetorheological Finishing (MRF) is a new optical surface processing method, which has the advantages that good polishing effect, no subsurface damage, and suitable for complex surface processing. However, the interaction mechanism between the MRF pad and the workpiece is very complicated, so that the existing MRF material removal theoretical model is not accurate enough to establish the relationship between polishing parameters and material removal. In order to improve the processing efficiency and explore the material removal mechanism, a cluster magnetorheological finishing (CMRF) with dynamic magnetic fields method was proposed. Studying CMRF with dynamic magnetic fields material removal model is helpful to explain the removal mechanism more deeply, and improve the processing efficiency. In this study, the CMRF method was used to conduct a multi-factor orthogonal test on 2-inch single crystal silicon wafers. Based on the empirical Preston equation, the relationship between the machining gap and the polishing pressure was explained. Orthogonal experiments were done for a series of speeds, and obtaining the order of the influence of various factors on the average surface roughness Ra of the workpiece was: workpiece rotation speed > polishing disk speed > magnetic poles rotation speed > oscillating speed; the material removal rate (MRR) was: polishing disk speed > workpiece rotation speed > magnetic poles rotation speed > oscillating speed. Then combining with the orthogonal experimental data, and taking the surface roughness Ra and MRR as evaluation criteria, using Adam (Adaptive momentum) optimization algorithm to build a prediction model of Ra and MRR for polishing single crystal silicon by CMRF with dynamic magnetic fields based on BP neural network. For the prediction result, Ra of the maximum error was 7.05%, the minimum was 0.31%; MRR of the maximum error was 10.22%, the minimum was 1.32%. Therefore, the feasibility of this model for predicting the results of CMRF was verified, and it laid a good foundation for the development of CMRF technology and its industrial application.