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

共 50 条

[41] Feature Selection for Gender Classification
Zhang, Zhihong
Hancock, Edwin R.
PATTERN RECOGNITION AND IMAGE ANALYSIS: 5TH IBERIAN CONFERENCE, IBPRIA 2011, 2011, 6669 : 76 - 83
[42] Sequential Feature Selection for Classification
Rueckstiess, Thomas
Osendorfer, Christian
van der Smagt, Patrick
AI 2011: ADVANCES IN ARTIFICIAL INTELLIGENCE, 2011, 7106 : 132 - +
[43] Feature Selection in Text Classification
Sahin, Durmus Ozkan
Ates, Nurullah
Kilic, Erdal
2016 24TH SIGNAL PROCESSING AND COMMUNICATION APPLICATION CONFERENCE (SIU), 2016, : 1777 - 1780
[44] MULTISENSOR INTEGRATION - AN AUTOMATIC FEATURE-SELECTION AND STATE IDENTIFICATION METHODOLOGY FOR TOOL WEAR ESTIMATION
GUINEA, D
RUIZ, A
BARRIOS, LJ
COMPUTERS IN INDUSTRY, 1991, 17 (2-3) : 121 - 130
[45] Robust Tool Wear Monitoring Using Systematic Feature Selection in Turning Processes With Consideration of Uncertainties
Zhang, Bin
Katinas, Christopher
Shin, Yung C.
JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME, 2018, 140 (08):
[46] Tool wear prediction based on XGBoost feature selection combined with PSO-BP network
Lin, Zhangwen
Fan, Yankun
Tan, Jinling
Li, Zhen
Yang, Peng
Wang, Hua
Duan, Weiwei
SCIENTIFIC REPORTS, 2025, 15 (01):
[47] Comparative evaluation of feature selection methods and deep learning models for precise tool wear prediction
Kumar, Anuj
Vasu, V.
MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN, 2025, 8 (01)
[48] Tool wear state recognition based on GWO-SVM with feature selection of genetic algorithm
Liao, Xiaoping
Zhou, Gang
Zhang, Zhenkun
Lu, Juan
Ma, Junyan
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2019, 104 (1-4): : 1051 - 1063
[49] Study of Tool Wear in the Process of Physical Modeling of Shaving–Rolling of Cylindrical Gears
Sidorkin, A.V.
Kovalev, Yu. V.
Artamonov, V.D.
Malikov, B.A.
Russian Engineering Research, 2024, 44 (06) : 861 - 867
[50] Tool wear state recognition based on feature selection method with whitening variational mode decomposition
Wei, Xudong
Liu, Xianli
Yue, Caixu
Wang, Lihui
Liang, Steven Y.
Qin, Yiyuan
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2022, 77

← 1 2 3 4 5 →