Abstract
Tool wear of machine tools not only directly affects the processing quality, but also leads to the life of processing equipment and production costs. It is of great significance to correctly identify, classify and predict the state of tool wear. In this paper, through the collection of machine tool operation data, using machine learning modeling, using the model to identify the tool wear state, and then predict and classify the tool wear state, using the classification results to determine whether the tool can continue to use. After simulation verification, the results show that the model can identify and predict the wear state more realistically, and has strong practicability.
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