Detection for Cutting Tool Wear Based on Convolution Neural Networks

In modern manufacturing systems and industries, an increasing number of researches which are absorbed in recognition of abnormal process, apply data-driven model to analyze the manufacturing process. However, considering the sequence data is unable to be imported in the regression model and classification model, researchers need to investment most of time in feature extraction methods to recognize the abnormal process. In the last few years, with the development of neural network algorithm deep learning methods, which redefine the processing way from raw data, the neural network has been used to address raw sensory data. In this study, which is based on the multi-source perception signal of machining process, the CNNs algorithm's convolutional layer extracting information features, the BN normalizing retention feature information and the pooling layers reducing the feature matrix contribute to the establishment of a state prediction model which could predict and identify the degree of wear of the tool and the abnormal state of the machining process. After that, a tool wear test is introduced to comparing results of the traditional signal processing method with CNN algorithm and the feasibility of the CNN algorithm is demonstrated.