Prediction of cutting forces and surface roughness in hard turning process of 42CrMo4 steel using machine learning

Abstract

The purpose of this paper is to develop machine learning (ML) models for prediction of surface roughness and cutting forces of 42CrMo4 steel in hard turning process. A full factorial experimental design with four input parameters: cutting speed, depth of cut, feed and insert radius was used to develop ML models for predicting the performance of turning process. The backward linear regression, random forest (RF) and XGBoost were used. Also, for the linear regression model and for the best RF and XGBoost model five-fold cross validation was done to confirm that the models provide reliable generalization estimates rather than performance dependent on a single data split. The XGBoost model demonstrates the most compact clustering of residuals with fewer large errors, indicating better overall stability and predictive consistency compared to the linear regression and RF models. The application of different ML methods with monitoring of standardized residuals on unseen data confirms the reliability of the developed models in real application conditions. This study provides a structured and comparative modeling framework across multiple output variables, where backward linear regression, RF and XGBoost models were developed. Several architectural and hyperparameter variations of the RF and XGBoost models were evaluated to ensure optimal configuration for each output. Also, variable influence was examined through permutation feature importance for ensemble models and statistical significance testing for linear regression, enabling interpretation and discussion of the influence of input variables on selected outputs.