Prediction of machine tool wear using the weighted k-nearest neighbor method and Neural network model with acceleration response

In the manufacturing industry, with the decrease in the working population, there is a demand for intelligent manufacturing processes (smart factory). When focusing on cutting, predicting and detecting tool wear during machining is an important technical issue for autonomy. However, the tool wear of cutting tools varies from machining to machining even under the same machining conditions, with the same type of tool, and with the same material and shape of workpiece. Therefore, even if a representative wear prediction model is constructed to predict tool wear, the prediction accuracy is low and not practical. In this research, we will verify whether the amount of tool wear can be predicted by a neural network, which is a type of machine learning, using the vibration data obtained during cutting and the past. The accuracy of the proposed method is also verified by comparing the wear volume prediction by one representative neural network with the wear volume prediction by the combination of the proposed multiple neural network.