HEURISTIC FEATURE SELECTION FOR SHAVING TOOL WEAR CLASSIFICATION

被引：0

作者：

Wang, Yong ^{[1
]}

Brzezinski, Adam J. ^{[2
]}

Qiao, Xianli ^{[3
]}

Ni, Jun ^{[3
]}

机构：

[1] SUNY Binghamton, Dept Syst Sci & Ind Engn, 4400 Vestal Pkwy E, Binghamton, NY 13902 USA

[2] HGST Inc, Western Digital, 3403 Yerba Buena Rd, San Jose, CA 95135 USA

[3] Univ Michigan, Dept Mech Engn, 1255 HH Dow,2350 Hayward St, Ann Arbor, MI 48109 USA

来源：

PROCEEDINGS OF THE ASME 11TH INTERNATIONAL MANUFACTURING SCIENCE AND ENGINEERING CONFERENCE, 2016, VOL 2 | 2016年

关键词：

Shaving process; condition monitoring; feature selection; tabu search; probabilistic neural network; CUTTER;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

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.

引用

页数：7

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