Predicting volumetric error compensation for five-axis machine tool using machine learning

This work proposes a rapid and robust machine-learning model to predict the volumetric error of a five-axis machine tool. For this purpose, several machine learning models – which are MultiOutput regression, Chained MultiOutput regression, Linear regression, SVM regression, XGB regression and ANN regression – have been selected, and their performances were compared to other models in the literature and to one another to find the best model for the problem at hand. The robustness of each model is investigated using three statistical metrics: namely Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Coefficient of Determination (R²). The results show that the models proposed in this paper are more effective than those in the literature, using the same data. Amongst the proposed models, the SVR Regression model has proven to be the best, considering all statistical metrics. Compared to the polynomial method in the literature with varying order levels, the method proposed here improves accuracy and predictive performance by 27%, with an RMSE of 0.03246 mm for the SVR model. Finally, based on the best model, an explicit equation has been deduced for practical applications. That prediction equation has been implemented in Excel and included in this paper.