Abstract
Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps: first, decomposing cutting torque components from the original signals by wavelet packet decomposition (WPD); second, extracting wavelet coefficients of different wear states (i.e., slight, normal, or severe wear) with signal features adapting to Welch spectrum. Finally, monitoring and recognition of the feature vectors of cutting torque signal are performed by using the K-means cluster and radial basis function neural network (RBFNN). The experiments on different tool wears of the multivariable features reveal that the results of monitoring and recognition are significant and effective.
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Xu Yang received M. Tech. degree in mechanical engineering from Dalian Polytechnic University, Dalian, PRC in 2005. He has been a lecturer of Dalian Polytechnic University. Currently, he is a Ph.D. candidate in the Department of Mechanical System Engineering at Faculty of Engineering of Gunma University, Kiryu, Japan.
His research interests include mechanical automation, artificial intelligence, and virtual instrument.
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Yang, X. Wear state recognition of drills based on K-means cluster and radial basis function neural network. Int. J. Autom. Comput. 7, 271–276 (2010). https://doi.org/10.1007/s11633-010-0502-z
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DOI: https://doi.org/10.1007/s11633-010-0502-z