0
Research Papers

Heuristic Feature Selection for Shaving Tool Wear Classification

[+] Author and Article Information
Yong Wang

Department of Systems Science and
Industrial Engineering,
Binghamton University,
4400 Vestal Pkwy E,
Binghamton, NY 13902
e-mail: yongwang@binghamton.edu

Adam J. Brzezinski

HGST, Inc.,
Western Digital,
3403 Yerba Buena Road,
San Jose, CA 95135
e-mail: adam.brzezinski@hgst.com

Xianli Qiao

Department of Mechanical Engineering,
University of Michigan—Ann Arbor,
1255 H. H. Dow, 2350 Hayward Street,
Ann Arbor, MI 48109
e-mail: qiao@umich.edu

Jun Ni

Department of Mechanical Engineering,
University of Michigan–Ann Arbor,
1255 H. H. Dow, 2350 Hayward Street,
Ann Arbor, MI 48109
e-mail: junni@umich.edu

1Corresponding author.

Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received August 17, 2016; final manuscript received August 23, 2016; published online October 14, 2016. Editor: Y. Lawrence Yao.

J. Manuf. Sci. Eng 139(4), 041001 (Oct 14, 2016) (6 pages) Paper No: MANU-16-1444; doi: 10.1115/1.4034630 History: Received August 17, 2016; Revised August 23, 2016

In this paper, we develop and apply feature extraction and selection techniques to classify tool wear in the gear 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.

FIGURES IN THIS ARTICLE
<>
Copyright © 2017 by ASME
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Fig. 1

Shaving cutting tool with cutting edges enhanced in inset

Grahic Jump Location
Fig. 2

Shaving tool with broken teeth

Grahic Jump Location
Fig. 3

Accelerometer locations on the machine

Grahic Jump Location
Fig. 4

Sample accelerometer data from shaving one workpiece

Grahic Jump Location
Fig. 5

FFT of data from new tool (top) and broken tool (bottom)

Grahic Jump Location
Fig. 6

Structure of a probabilistic neural network (PNN)

Grahic Jump Location
Fig. 7

Division of the CF/Y/1 data into two classes—unbroken (1) and broken (2)

Grahic Jump Location
Fig. 8

Division of the CF/Y/1 data into four classes—new, worn, broken, and severely broken

Grahic Jump Location
Fig. 9

Time history of the two features most sensitive to tool condition

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In