Abstract
In this paper, a tool wear assessment method based on wavelet packet energy spectrum and energy value of the characteristic spectrum band is introduced. The experiment to different wear condition of the turning tool were completed. The typical time and frequency feature of acoustic emission signals was collected. By wavelet packet analysis, the energy spectrum coefficient of wavelet packet were extracted, which can be used to describe the energy distribution in different frequency band. And then the characteristic spectrum band which is sensitive to the tool wearing can be found, and the relationship between the energy value of the characteristic spectrum band and the degree of tool wear is established. The result shows that distribution of the energy spectrum coefficient of wavelet packet changed significantly after the tool worn, and the energy value of the characteristic spectrum band increased with the tool wear. Therefore, the characteristic index can accurately describe the extent of tool wear.
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References
Gao, H.: The investigation of intelligent tool wear monitoring techniques for metal cutting process. Southwest Jiaotong University, Chengdu (2005) (in Chinese)
Wang, J., Yu, J., Huang, W.: Application of Wavelet Package Analysis and Support Vector Machine to Fault Diagnosis of Cutting Tool. Journal of Vibration, Measurement & Diagnosis 28, 273–276 (2008) (in Chinese)
Yu, F.: The AE monitoring research of tool cutter breakage and abrasion. Machine Development, 72–75 (2005)
Wang, J., Huang, W., Yu, J., Wei, Y.: The Characteristics Identification of Tool Cutting Conditions Based on Wavelet Analysis. Sichuan University of Science and Technology, 31–34 (2005) (in Chinese)
Wang, H., Ma, C., Shao, H., Hu, D.: The Tool Wear and Breakage Monitoring in Turning Using Neural Network. Journal of Shanghai Jiaotong University (2006) (in Chinese)
Chen, H., Huang, S., Li, D., Fu, P.: Research on FCA-based monitoring of the CNC turning tool wear. Modern Manufacturing Engineering, 134–137 (2010)
Qi, G., Barhorst, A., Hashemi, J., Kamala, G.: Discrete wavelet deformation of acoustic emission signals from carbon-fiber-reinforced composites. Composites Science and Technology 57, 389–403 (1997)
Grabowska, J., Palacz, M., Krawczuk, M.: Damage identification by wavelet analysis. Mechanical Systems and Signal Processing 22, 1623–1635 (2008)
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© 2012 Springer-Verlag GmbH Berlin Heidelberg
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Dou, Y., Xu, X., Wu, G., Wang, S., Ren, B. (2012). A Tool Wearing Assessment Method Based on Wavelet Transform. In: Qian, Z., Cao, L., Su, W., Wang, T., Yang, H. (eds) Recent Advances in Computer Science and Information Engineering. Lecture Notes in Electrical Engineering, vol 129. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25778-0_67
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DOI: https://doi.org/10.1007/978-3-642-25778-0_67
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