机床刀具磨损状态集成学习识别及测试

Machine Tool Wear State Integrated Learning Identification and Testing

为了提高刀具磨损状态识别能力,开发出了刀具磨损阶段回归模型方法。把AdaBoost集成算法加入回归模型,降低磨损过程中回归模型预测误差。研究结果表明:平稳磨损阶段所需时间最短,最长为急剧磨损阶段。进行刀具磨损识别期间,集成学习算法可以获得比单独算法更优性能。磨损期间误差受到各阶段磨损变化率的较大影响。采用集成方法AdaBoost得到了较小MAE,只有36.3%,可以有效促进非集成算法模型的性能提升,实现集成学习算法模型的改善效果。

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.