Intelligent recognition of milling cutter wear state with cutting parameter independence based on deep learning of spindle current clutter signal

In the process of milling, tool wear directly affects the quality and accuracy of workpieces. Online recognition of milling cutter wear state has been and remains a growing interest in intelligent manufacturing to increase the machining efficiency and control the unqualified rate of workpieces. The effective value of spindle current can effectively characterize the wear state of milling cutter, but it will change along with machining process parameters, which are not suitable for the wear state recognition of milling cutter (WSRMC) under complex working conditions. We present LeNet-WSRMC network, a novel approach to recognize wear state of milling cutter based on the clutter signal of spindle current. The cutting vibration and tool wear are the main reasons for exciting the dynamic cutting force and the clutter signal of spindle current. In order to fully describe the generation mechanism of the clutter signal, we divide the wear state of milling cutter into four categories (i.e., normal wear, severe wear, abnormal vibration caused by tool wear, and abnormal vibration caused by improper selection of cutting parameter when the tool is sharp). LeNet-WSRMC network uses the deep convolutional neural network (DCNN) model to extract features from the spindle current clutter signal (SCCS) as the wear state of milling cutter classification index. A series of experiments with different cutting parameters and conditions are implemented to validate the effectiveness and generalization of our proposed methodology. The experimental results show that this method can realize the online accurate recognition of the wear state of milling cutter under the condition of complex working condition. This study lays a foundation for the prediction of the remaining life of the milling cutter under complex working conditions and the reasonable formulation of the replacement rules of the milling cutter.