Accurate estimation of tool wear levels during milling, drilling and turning operations by designing novel hyperparameter tuned models based on LightGBM and stacking

Abstract

The study is focused on executing machining operations by using 18 tools of milling, drilling, and turning thus 6 tools were used for each machining process. The VB of tool was measured for categorizing the tools ranging from level-1 to level-5 based on the severity of tool wear. The first model was designed based on LightGBM whereas the second model was developed by designing six algorithms i.e. LR, RF, CART, NB, SVM, and KNN. All algorithms were combined to develop an ensemble stacking model. Manual hyperparameter tuning was done for the LightGBM model whereas automatic hyperparameter tuning was adopted for the Stacking model by using GridSearchCV. The force signals’ features extraction was done by SSA whereas dimensionality reduction was accomplished by PCA. One-hot encoding technique has transformed target variables into binary form. The application of techniques of dropout and early stopping to both models has overcome overfitting.