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.
Heuristic Feature Selection for Shaving Tool Wear Classification
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.
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Wang, Y., Brzezinski, A. J., Qiao, X., and Ni, J. (October 14, 2016). "Heuristic Feature Selection for Shaving Tool Wear Classification." ASME. J. Manuf. Sci. Eng. April 2017; 139(4): 041001. https://doi.org/10.1115/1.4034630
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