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Recurrence Quantification Analysis of System Signals for Detecting Tool Wear in a Lathe

[+] Author Affiliations
Rajesh Vazhayil Govindan

Government Model Engineering College, Cochin, India

Narayanan Namboothiri Velimamkovil Narayanan

Cochin University of Science and Technology, Cochin, India

Paper No. MSEC2015-9214, pp. V002T04A005; 8 pages
doi:10.1115/MSEC2015-9214
From:
  • ASME 2015 International Manufacturing Science and Engineering Conference
  • Volume 2: Materials; Biomanufacturing; Properties, Applications and Systems; Sustainable Manufacturing
  • Charlotte, North Carolina, USA, June 8–12, 2015
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-5683-3
  • Copyright © 2015 by ASME

abstract

The work investigates applicability of recurrence quantification analysis (RQA) in metal cutting with an objective to detect tool wear. The effectiveness of applying a system input signal; the drive motor current, in relation to a system output signal; the tool vibration, for the analysis is also explored. The work establishes conclusively that three of the RQA variables, percent determinism, percent recurrence and entropy are sensitive to tool wear.

Copyright © 2015 by ASME

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