Tool wear prediction using long short-term memory variants and hybrid feature selection techniques

Abstract

Tool wear prediction is a challenging aspect of the milling machine as the cutting tool is responsible for the accuracy and precision of the final machined product. The accuracy of tool wear prediction depends on how well-established data are provided to the prediction model. The appropriate and optimal features need to be selected from the features to provide appropriate data to the model. The implementation of proper feature selection methods substantially reduces the complexity of the raw data for analysis and transfers vital information to the prediction model. In this paper, the IEEE NUAA Ideahouse tool wear dataset was used for the study of tool wear prediction. Different feature selection methods, namely the Pearson correlation coefficient (PCC) and random forest (RF), along with hybrid methods such as PCC with RF and principal component analysis (PCA) with PCC, were used for the feature selection. The selected features obtained from the feature selection techniques were then applied to the different variants of the long short-term memory (LSTM) model for tool wear prediction. The best feature selection method was chosen based on the performance metrics such as the root mean square error (RMSE), R-squared (R2), and mean absolute percentage error (MAPE). The hybrid feature selection method of RF, PCA with PCC used together with the encode-decoder LSTM model was found to have the best accuracy with R2, RMSE, and MAPE values of 0.97, 0.036, and 5.79, respectively, for tool wear prediction compared to the other feature selection methods and LSTM variants.