Cutting tool wear prediction based on the multi-stage Wiener process

Cutting tools are one type of critical component of modern computer numerical control (CNC) machining systems. They wear out continuously during the machining process until they fail, and cutting tool failure can lead to the collapse of the entire system and even cause substantial losses. Therefore, it is of great importance to study the method for tool wear prediction. A new model for wear prediction of cutting tools is established based on a multi-stage Wiener process, where the degradation rates of cutting tools are considered to change in three stages based on the typical cutting tool wear curve model. Firstly, the degradation processes of cutting tools are divided into three stages. Secondly, the parameter estimation for each stage of the degradation processes of cutting tools is completed, respectively, by utilizing the EM (expectation–maximization) algorithm. Then, the wear of cutting tools is predicted, and the reliability of cutting tools is analyzed by using a numerical integration simulation method based on the Monte Carlo algorithm. Finally, the proposed model is illustrated and verified via the flank wear data of cutting tools, and the prediction accuracy is measured by mean squared error (MSE) and the coefficient of determination ( R^2 ). The prediction results show that the proposed model enables us to make more economical maintenance by delaying the tool replacement time with fewer degradation data. Compared to the traditional methods based on machine learning (ML), the proposed model can complete the wear prediction and reliability analysis more accurately.