关键词: 纳米加工, 尖端磨损在线识别, 横向力特征, 增量自适应支持向量机, 统计模式损伤检测

Abstract: To improve the accuracy and generalization ability of Atomic Force Microscope (AFM) tip wear online monitoring in nano-machining,a detection method based on an Incremental Adaptive Support Vector Machine (IASVM) was proposed.The peak-to-peak value and variance of the lateral force were calculated as feature variables,and the data set was updated via a moving window.Based on the current Support Vector Machine (SVM) structure,the regularization parameter C and kernel parameter σ were adaptively modified through maintaining the optimization conditions required by Kuhn-Tucher theorem to obtain the updated SVM solution,then the incremental data and the original data disturbed by it were performed classification,the trend of the tip failure number reflected the degree of tip wear.Experiments showed that the algorithm could meet the requirements of online detection in recognition accuracy and time.Compared with the directional acyclic graph support vector classifier,IASVM had stronger robustness and higher generalization ability.

Key words: nano machining, online recognition of tip wear, lateral force characteristics, incremental adaptive support vector machine, statistical pattern damage detection