Predicting the Tool Wear of a Drilling Process Using Novel Machine Learning XGBoost-SDA

Mahdi S Alajmi; Abdullah M Almeshal

doi:10.3390/ma13214952

Predicting the Tool Wear of a Drilling Process Using Novel Machine Learning XGBoost-SDA

Materials (Basel). 2020 Nov 4;13(21):4952. doi: 10.3390/ma13214952.

Authors

Mahdi S Alajmi¹, Abdullah M Almeshal²

Affiliations

¹ Department of Manufacturing Engineering Technology, College of Technological Studies, Public Authority for Applied Education and Training, Safat 13092, Kuwait.
² Department of Electronic Engineering Technology, College of Technological Studies, Public Authority for Applied Education and Training, Safat 13092, Kuwait.

Abstract

Tool wear negatively impacts the quality of workpieces produced by the drilling process. Accurate prediction of tool wear enables the operator to maintain the machine at the required level of performance. This research presents a novel hybrid machine learning approach for predicting the tool wear in a drilling process. The proposed approach is based on optimizing the extreme gradient boosting algorithm's hyperparameters by a spiral dynamic optimization algorithm (XGBoost-SDA). Simulations were carried out on copper and cast-iron datasets with a high degree of accuracy. Further comparative analyses were performed with support vector machines (SVM) and multilayer perceptron artificial neural networks (MLP-ANN), where XGBoost-SDA showed superior performance with regard to the method. Simulations revealed that XGBoost-SDA results in the accurate prediction of flank wear in the drilling process with mean absolute error (MAE) = 4.67%, MAE = 5.32%, and coefficient of determination R² = 0.9973 for the copper workpiece. Similarly, for the cast iron workpiece, XGBoost-SDA resulted in surface roughness predictions with MAE = 5.25%, root mean square error (RMSE) = 6.49%, and R² = 0.975, which closely agree with the measured values. Performance comparisons between SVM, MLP-ANN, and XGBoost-SDA show that XGBoost-SDA is an effective method that can ensure high predictive accuracy about flank wear values in a drilling process.

Keywords: SDA; XGBoost; artificial neural networks; drilling process; flank wear prediction; machine learning; machining parameters; optimization; support vector machines.