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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U. S. C. T. Institute, 1989, Metal Cutting Tool Handbook, 7th ed., Industrial Press, New York.
Klocke, F. , and Schroder, T. , “ Gear Shaving: Simulation and Technological Studies,” ASME Paper No. DETC2003/PTG-48033.
Hung, C. , Liu, J. , Chang, S. , and Lin, H. , 2007, “ Simulation of Gear Shaving With Considerations of Cutter Assembly Errors and Machine Setting Parameters,” Int. J. Adv. Manuf. Technol., 35(3–4), pp. 400–407. [CrossRef]
Lv, M. , and Yang, X. , 2002, “ Design and Manufacture of a Shaving Cutter With Unequal Depth Gashes,” J. Mater. Process. Technol., 129(1–3), pp. 193–195. [CrossRef]
Brzezinski, A. J. , Wang, Y. , Choi, D. K. , Qiao, X. , and Ni, J. , 2008, “ Feature-Based Tool Condition Monitoring in a Gear Shaving Application,” ASME Paper No. MSEC_ICMP2008-72297.
Elbestawi, M. A. , Papazafifiou, T. A. , and Du, R. X. , 1991, “ In-Process Monitoring of Tool Wear in Milling Using Cutting Force Signature,” Int. J. Mach. Tools Manuf., 31(1), pp. 55–73. [CrossRef]
Jardine, A. K. S. , Lin, D. , and Banjevic, D. , 2006, “ A Review on Machinery Diagnostics and Prognostics Implementing Condition-Based Maintenance,” Mech. Syst. Signal Process., 20(7), pp. 1483–1510. [CrossRef]
Wang, L. , and Gao, R. X. , 2006, Condition Monitoring and Control for Intelligent Manufacturing, Springer, London.
Zakrajsek, J. J. , and Lewicki, D. G. , 1998, “ Detecting Gear Tooth Fatigue Cracks in Advance of Complete Fracture,” Tribo Test, 4(4), pp. 407–422.
Yu, D. , Yang, Y. , and Cheng, J. , 2007, “ Application of Time-Frequency Entropy Method Based on Hilbert-Huang Transform to Gear Fault Diagnosis,” Measurement, 40(9–10), pp. 823–830. [CrossRef]
Wang, Y. , Li, L. , Ni, J. , and Huang, S. , 2009, “ Feature Selection Using Tabu Search With Long-Term Memories and Probabilistic Neural Networks,” Pattern Recognit. Lett., 30(7), pp. 661–670. [CrossRef]
Pudil, P. , Novovicova, J. , and Kittler, J. , 1994, “ Floating Search Methods in Feature Selection,” Pattern Recognit. Lett., 15(11), pp. 1119–1125. [CrossRef]
Siedlecki, W. , and Sklansky, J. , 1989, “ A Note on Genetic Algorithms for Large-Scale Feature Selection,” Pattern Recognit. Lett., 10(11), pp. 335–347. [CrossRef]
Rafiee, J. , Arvani, F. , Harifi, A. , and Sadeghi, M. H. , 2007, “ Intelligent Condition Monitoring of a Gearbox Using Artificial Neural Network,” Mech. Syst. Signal Process., 21(4), pp. 1746–1754. [CrossRef]
Du, R. X. , Elbestawi, M. A. , and Li, S. , 1992, “ Tool Condition Monitoring in Turning Using Fuzzy Set Theory,” Int. J. Mach. Tools Manuf., 32(6), pp. 781–796. [CrossRef]
Guyon, I. , and Elisseeff, A. , 2003, “ An Introduction to Variable and Feature Selection,” J. Mach. Learn. Res., 3(7–8), pp. 1157–1182.
Bell, A. , and Sejnowski, T. , 1997, “ The ‘Independent Components' of Natural Scenes are Edge Filters,” Vision Res., 37(23), pp. 3327–3338. [CrossRef] [PubMed]
Specht, D. F. , 1990, “ Probabilistic Neural Networks,” Neural Networks, 3(1), pp. 109–118. [CrossRef]


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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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