Machine Tool Wear State Integrated Learning Identification and Testing
In order to improve the ability of tool wear state recognition,a regression model of tool wear stage was developed.AdaBoost integrated algorithm is added to the regression model to reduce the prediction error of the regression model in the process of wear.The results show that the time required for smooth wear stage is the shortest,while the time required for sharp wear stage is the longest.During tool wear identification,the integrated learning algorithm can obtain better performance than the single algorithm.The error during wear is greatly affected by the rate of wear change at each stage.The integration method AdaBoost obtained a small MAE,only 36.3％,which can effectively promote the performance improvement of the non-integrated algorithm model and achieve the improvement effect of the integrated learning algorithm model.
NETL
NSTL
万方数据
