Data-driven online detection of tip wear in tip-based nanomachining using incremental adaptive support vector machine

Abstract

Abstract This paper presented an online statistical pattern recognition method to detect the severe tip wear in nanomachining using an incremental adaptive support vector machine (IASVM). From the time series data of the collected nanomachining force, the two feature variables (i.e., peak-to-peak force and variance of each machining cycle) were calculated to classify the state of the nanomachining process. To enable online detection, the data was collected in the form of a moving window, sliding once every 0.2\xa0s to add 20 sets of new feature data to update the data set each time. For each data window, the support vector machine (SVM) was restructured by modifying the regularization parameter and the Kernel parameter adaptively. The solution structure of the updated SVM was calculated to classify the incremental data and the disturbing data with the best accuracy. The number of tip failure points in each window was counted to determine the tip wear severity and the moment when the AFM tip needs to be replaced by monitoring the trend of tip failure points. From experimental data, it was shown that the average accuracy of IASVM s recognition of the tip wear conditions was 95% and the average calculation time was 0.166\xa0s, which makes it a promising approach for online detection of tip damage in nanomachining process.