基于时间序列数据和支持向量机的纳米加工AFM刀尖损伤监测

Nano-machining AFM Tip Wear Monitoring Based on Time Series Data and Support Vector Machine

研究了基于支持向量机(SVM)的时间序列数据分析和模式识别,以监测基于AFM尖端的纳米加工过程在加工性能和尖端磨损方面的状态变化。具有瞬态、非线性和非静止特性的时间序列数据(即来自过程的加工力)由数据采集系统收集。提取3种状态检测特征,包括最大侧向加工力、侧向加工力值峰间距以及侧向加工力的方差,以对纳米加工过程的状态进行分类。构造具有(高斯)径向基核函数(RBF内核)的定向非循环图支持向量机(DAGSVM)以识别尖端状态。使用多元SVM分类机,将加工过程和刀尖磨损分为初始磨损、过渡区域磨损以及尖端失效(破裂/磨损严重的加工/不加工)3个区域。实验数据表明,二元和三元分类中SVM的准确率均超过94.73%。

Time-series data analysis and pattern recognition using support vector machine (SVM) are studied to monitor the state changes of the AFM tip-based nanomachining process with respect to the machining performance and tip wear.Time series data (i.e.machining force from the process),which has transient,nonlinear,and non-stationary characteristics,is collected by a data acquisition system.Three status detection features including the maximum force,peak to peak force value,and the variance of the collected lateral machining force,are extracted to classify the state of the nanomachining process.Directed acyclic graph support vector machines with a (Gaussian) radial basis kernel function is constructed to identify the tip wear status.Using this multi-class SVM,the machining process and the tip wear can be classified into three regions,which are effective machining with sharp tip,transition region,and bad/no machining with severe tip wear.The experimental data shows that the accuracy of the SVM is over 94.73% in both binary and ternary classifications.