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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.

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References

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Figures

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Fig. 1

Shaving cutting tool with cutting edges enhanced in inset

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Fig. 2

Shaving tool with broken teeth

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Fig. 3

Accelerometer locations on the machine

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Fig. 4

Sample accelerometer data from shaving one workpiece

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Fig. 5

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

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Fig. 6

Structure of a probabilistic neural network (PNN)

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Fig. 7

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

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Fig. 8

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

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Fig. 9

Time history of the two features most sensitive to tool condition

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