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

Mahdi S Alajmi,Abdullah M Almeshal

doi:10.3390/ma13214952

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.

Highlights

Production companies are attempting to boost product quality as well as to reduce operating costs
The results show that the predicted tool wear obtained by XGBoost-Spiral Dynamics Optimisation Algorithm (SDA) closely matches the actual values of the measured tool wear compared to the support vector machines (SVM) and MLP methods, which visually confirmed how well the XGBoost-SDA fitted the validation dataset
In this research we have presented a novel hybrid XGBoost-SDA prediction model for the flank wear of a drilling process

Summary

Introduction

Production companies are attempting to boost product quality as well as to reduce operating costs. Online real-time control and monitoring of drilling processes was proposed as an effective method to minimize manufacturing costs [1]. One of the common problems across processes such as drilling, milling, and turning is the tool wear [3,4]. The worn tools lower the production quality and result in drilling holes and may lead to damage to both the workpiece and the machine. It may result in increasing the cutting force that results in raising the temperature and accelerate the tool wear [5,6]

Methods

Results

Conclusion