Abstract
An approach for tool wear monitoring is presented, which bases on the Feature Vector Selection with Linear Regression (FVS-LR). In this approach, feature vectors are used to capture the geometrical characteristics of tool wear samples, and detection of tool wear is performed by using the model derived from the feature vectors in linear regression method. The signals of cutting force under the condition of tool non-wear and tool wear in 0.6 mm are used to testify the FVS-LR based method for monitoring of tool wear. The results indicate that tool wear can be successfully detected in this method, which is more suitable for the on-line detection in real time because of its efficient algorithm in learning stage and high computing speed in utilization stage.
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© 2005 Springer-Verlag Berlin Heidelberg
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Chen, Z., Zhang, X. (2005). Monitoring of Tool Wear Using Feature Vector Selection and Linear Regression. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_1
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DOI: https://doi.org/10.1007/11539117_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28325-6
Online ISBN: 978-3-540-31858-3
eBook Packages: Computer ScienceComputer Science (R0)