AdaBoost 算法使能的刀具磨损状态 分阶段评估集成方法

A regression method of full-stage wear of cutting tool based on the AdaBoost (Adaptive Boosting) integrated algorithm is proposed. Firstly, using the acquired machining process signals and tool wear values, a fitting curve of cutting tool wear is established to achieve an accurate division in the initial wear stage, smooth wear stage, and sharp wear stage. Secondly, the three-stage data samples with the corresponding wear values of cutting tool by extracting the data feature from the machining process signals are obtained. The regression models for the three stages are established with the support vector machine. Thirdly, the AdaBoost algorithm is used to determine the weights of the three regression models in the three stages, and a regression model is established of full-stage wear regression. Finally, the effectiveness of the present model and method is verified with the wear data of a cutting tool collected in the milling cutter.