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Heuristic Feature Selection for Shaving Tool Wear Classification

[+] Author Affiliations
Yong Wang

Binghamton University, Binghamton, NY

Adam J. Brzezinski

HGST, Inc., San Jose, CA

Xianli Qiao, Jun Ni

University of Michigan – Ann Arbor, Ann Arbor, MI

Paper No. MSEC2016-8547, pp. V002T04A040; 7 pages
doi:10.1115/MSEC2016-8547
From:
  • ASME 2016 11th International Manufacturing Science and Engineering Conference
  • Volume 2: Materials; Biomanufacturing; Properties, Applications and Systems; Sustainable Manufacturing
  • Blacksburg, Virginia, USA, June 27–July 1, 2016
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-4990-3
  • Copyright © 2016 by ASME

abstract

In this paper, we develop and apply feature extraction and selection techniques to classify tool wear in the shaving process. Because shaving tool condition monitoring is not well-studied, we extract both traditional and novel features from accelerometer signals collected from the shaving machine. We then apply a heuristic feature selection technique to identify key features and classify the tool condition. Run-to-life data from a shop-floor application is used to validate the proposed technique.

Copyright © 2016 by ASME

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