Tool wear prediction based on SVR optimized by hybrid differential evolution and grey wolf optimization algorithms

Tool wear prediction is key to ensuring product quality and machining efficiency. However, the prediction results of most models are unstable or inaccurate. To address the issues, a tool wear prediction model, based on support vector regression which was optimized by differential evolution and gray wolf optimization algorithms, was proposed in this paper. The method optimized the parameters of support vector regression model through differential evolution and grey wolf optimization algorithms to make the model more balanced in terms of its global and local search capabilities. First, the vibration and power signals were collected by sensors during the milling processes. Then, the features extraction and features selection were performed on the vibration and power signals. Next, the proposed model was developed and trained. Finally, the tool wear was predicted using the proposed model. The results showed that the proposed model had better performance than other models in terms of prediction accuracy and prediction efficiency, and it was applicable to the condition of multiple cutting parameters with generalizability, which will provide some valuable technical support for machining.