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Estimation of On-line Tool Wear in Turning Processes Using Recurrence Quantification Analysis (RQA)

Published

Author(s)

Sagar Kamarthi, Yung-Tsun Lee, Srinivasan Radhakrishnan

Abstract

This paper exploits the underlying dynamics of a turning process captured in force measurements for online flank wear estimation. We transform the sensor signals into feature vectors using recurrence quantification analysis and then estimate flank wear using a gradient boosted regression model. The data is collected by conducting two sets of turning experiments. The first set of data, which has 168 records, is used for training the machine learning model. The second set of data, which has 95 records, is used for testing the performance of the flank wear estimation method. The results indicate that the proposed method gives accurate flank wear estimates. The root mean square of the flank wear estimation for the test data is 9.87 x 10^(-5) mm.
Proceedings Title
2017 IEEE Big Data
Conference Dates
December 11-14, 2017
Conference Location
Boston, MA, US

Keywords

Tool wear, flank wear, turning process, recurrence quantification analysis, gradient boosting

Citation

Kamarthi, S. , Lee, Y. and Radhakrishnan, S. (2017), Estimation of On-line Tool Wear in Turning Processes Using Recurrence Quantification Analysis (RQA), 2017 IEEE Big Data, Boston, MA, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=924678 (Accessed April 18, 2024)
Created December 10, 2017, Updated October 12, 2021