Application of XGBoost algorithm as a predictive tool in a CNC turning process

Abstract

In this paper, an ensemble learning method, in the form of extreme gradient boosting (XGBoost) algorithm is adopted as an effective predictive tool for envisaging values of average surface roughness and material removal rate during CNC turning operation of high strength steel grade-H material. In order to develop the related models, a grid with 24600 combinations of different hyperparameters is created and tested for all the possible hyperparametric combinations of the model. The configurations having the optimal values of the considered hyperparameters and yielding the lowest training error are finally employed for predicting the response values in the CNC turning process. The performance of the developed models is finally validated with the help of five statistical error estimators, i.e. mean absolute percentage error, root mean squared percentage error, root mean squared logarithmic error, correlation coefficient and root relative squared error. Based on the favorable values of all the statistical metrics, it can be observed that XGBoost can be efficiently applied as a predictive tool with excellent accuracy in machining processes.