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

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.

Applying Bayesian Optimization for Machine Learning Models in Predicting the Surface Roughness in Single-Point Diamond Turning Polycarbonate

“…XGB is the most popular ML algorithm, developed in 2015. Regardless of the data type, it is well known to provide better solutions than other ML algorithms, because of its rapidity, efficiency, and scalability [52,53]. It has been the focus of research in various fields [54][55][56].…”

“…It has been the focus of research in various fields [54][55][56]. In particular, in mechanical machining [52,57,58], XGB is a good choice to predict tool wear and surface roughness. XGB is used for supervising learning problems, where we use the training data (with multiple features) x i to predict a target variable y i .…”

“…Before we learn about trees specifically, let us first review the basic elements in supervised learning. Below are the analytical formulas for the regression math on which XGB is based [52,59].…”

“…Let D � (x i , y i ) be a dataset composed of n samples and m features |D| � n, x i ∈ R m , y i ∈ R. A number of kadditive functions f k (x) are used in tree ensemble models to evaluate the function ∅(x i ). is function can be expressed as [52]…”

“…where k represents the number of trees, x i is the i th iteration of the training process, and f k denotes the decision rules of the tree and weight of leaf score. e regression space F can be expressed as follows [52]:…”