Machine learning models for enhanced cutting temperature prediction in hard milling process

Cutting temperature is the most crucial quality character in the machining process. By prudently controlling this factor, high precision workpiece can be produced. Determination of cutting temperature in milling operation is challenging, time consuming and expensive process. These cost and time losses can be eliminated by predicted cutting temperature with machine learning models. The present study deals with the prediction of the cutting temperature on end milling of H11 steel with coated cemented carbide tool under three cooling environments, such as dry Machining, Minimum Quantity Lubrication (MQL) and Nano Fluid Minimum Quantity Lubrication (NMQL). In this study, various machine learning models such as Regularized Linear Regression Model (RLRM), Decision Tree (DT), XGB Regression (XGBR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Gaussian Process Regression (GPR) were developed. These models use speed, feed, and lubrication conditions as input parameters. Among all the models, GPR yielded the best performance, achieving the highest evaluation metric scores of mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), determination coefficient (R2) and accuracy as of 14.04, 18.79, 14%, 0.9 and 85% respectively.