An in-process tool wear assessment using Bayesian optimized machine learning algorithm

Cutting tool wear monitoring (TWM) plays a significant role because it guarantees the machined surface integrity. Therefore, the present article proposed a TWM system using Bayesian optimized-support vector regression (BO-SVR) analysis. This objective was realized by acquiring machined surface texture video during machining from an in-situ CMOS camera, and subsequently, analyzing it by feature extraction, selection, predictive model training, model hyperparameters optimization, and model testing and validation. To develop an in-process TWM system, machined surface video is acquired during the machining process, and analyzed using Gabor wavelet (GW) and grey level co-occurrence matrix (GLCM) to extract the information related to roughness, feed marks, and waviness of texture. The significant features are selected using the fisher discriminant ratio (FDR) analysis. The in-process TWM system is trained using the FDR selected features and the predictive model hyperparameters such as C, gamma, epsilon, and kernel type are optimized using the Bayesian optimization algorithm, and their optimized results are 99.51, 0.55, 0.01186, and RBF. An optimized hyperparameters are used to establish an accurate and reliable in-process TWM system. The prediction model accuracy is compared with experimentally measured tool wear, the proposed BO-SVR model can predict tool wear with an RMSE of 0.026494.