Machine Learning Prediction of Turning Precision Using Optimized XGBoost Model

Abstract

The present study proposes a machine learning approach for optimizing turning parameters in such a way as to maximize the turning precision. The Taguchi method is first employed to optimize the turning parameters, and the experimental results are then used to train three machine learning models to predict the turning precision for any given values of the input parameters. The model which shows the best prediction performance (XGBoost) is further improved through the use of a synthetic minority over-sampling technique for regression with Gaussian noise (SMOGN) and four different optimization algorithms, including center particle swarm optimization (CPSO). Finally, the performances of the various models are evaluated and compared using the leave-one-out cross-validation technique. The experimental results show that the XGBoost model, combined with SMOGN and CPSO, provides the best performance, and is a useful tool for predicting the machining error of turning. The method can also reduce the cost of obtaining the optimized turning parameters corresponding with the predicted machining error.