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Monitoring tip-based nanomachining process by time series analysis using support vector machine
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DOI：10.1016/j.jmapro.2019.01.011.png)
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In this paper, time-series data analysis and pattern recognition using a multi-class support vector machine (SVM) were 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, was 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, were extracted to classify the state of the nanomachining process. Directed Acyclic Graph Support Vector Machines (DAGSVM) with a Gaussian Radial Basis Kernel Function (RBF Kernel) was constructed to identify the different process states. Using this multi-class SVM, the machining process and the tip wear can be classified into three regions, which are effective machining with a sharp tip, transition region and bad/no machining with severe tip wear. The experimental data showed that the accuracy of the SVM was over 94.73% in both binary and ternary classifications, which confirmed that the SVM-based pattern recognition technology via time series data could successfully monitor the tip wear and process performance for tip-based nanomachining process.
Keyword:
AFM tip-based nanomachining
Process monitoring
Tip wear detection
Time series data
Support vector machine
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Evaluation of intermittent contact mode AFM probes by HREM and using atomically sharp CeO2 ridges as tip characterizer.png)
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