Prediction of drilling force for high volume fraction SiCp/Al composite based on neural network

Abstract High volume fraction SiCp/Al composite is an important particle reinforced composite material, which is widely used in the aerospace and automotive fields. Drilling is a common processing method of SiCp/Al composite. It is of great significance to study the prediction of drilling force to improve the drilling efficiency and optimize the drilling parameters. In this paper, a high-volume fraction SiCp/Al composite drilling experiment was designed to obtain the drilling force at different drill rotational speed and feed rate. The improved BP neural network algorithm was used to predict the drilling force. The result shows that the predicted drilling force based on the improved neural network algorithm is consistent with the experimental drilling force, and the prediction model has high prediction accuracy. This study lays a good foundation for further research on tool wear and control of drilling surface quality.