Real-time monitoring of depth of cut in the multi-axis milling process with ball-end cutter

Abstract

Machining accuracy is one of the most critical issues in multi-axis CNC milling. However, various factors have an adverse effect on the production of precise surfaces such as imprecision of tool-setting, force-induced deformation, and machining errors of the previous cut. Monitoring the depth of cut (DOC) in the milling process has become increasingly important. The paper develops a deep neural network for real-time monitoring of the DOC based on the measured cutting force signal. The network is a variant of ResNet and soft-thresholding is embedded for denoising. Raw force signals and other parameters can be handled by the network without preprocessing. The network is trained with multiple milling tests of various DOC and cutter axis inclination angles, and a comparison is made with classic deep residual networks. The results show that the proposed network DRSN64 performs better under high noise levels with high computational efficiency. An average absolute error of 0.03 mm is reached in the real-time tracking test with varying cutter axis inclination angle and DOC.