Wavelet Packet analyses of Acoustic Emission Signal for Tool Wear in High Speed Milling

Abstract:

Tool wear condition monitoring technology is one of the main parts of advanced manufacturing technology and is a hot research direction in recent years. A method based on the characteristics of acoustic emission signal and the advantages of wavelet packets decomposition theory in the non-stationary signal feature extraction is proposed for tool wear state monitoring with monitor the change of acoustic emission signal feature vector. In this paper, through the method, firstly, acoustic emission signal were decomposed into 4 layers with wavelet packet analysis, secondly, the frequency band energy of the have been decomposed signal were extracted, thirdly, the frequency band energy that are sensitive to tool wear were selected as feature vector, and then the corresponding relation between feature vector and tool wear was established , finally, the state of the tool wear can be distinguished according to the change of feature vector. The results show that this method can be feasibility used to monitor tool wear state in high speed milling.

• Periodical: Key Engineering Materials (Volumes 589-590)
• Pages: 600-605
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.589-590.600
• Online since: October 2013
• Authors: Shun Xing Wu, Peng Nan Li, Zhi Hui Yan, Li Na Zhang, Xin Yi Qiu, Jin Yang
• Keywords: Acoustic Emission (AE), Feature Extraction, High Speed Milling, Tool Wear, Wavelet Packet Analysis

References

[1] Gao Hongli, Xu Mingheng, Fu Pan: China Mechanical Engineering. Vol. 16 (2005), pp.1069-1072.

[2] Chen Lin, Tian Zhengfang, Hou Dezheng: Hydromechatronics Engineering. Vol. 40 (2012), pp.111-113.

[3] Zhou Feng, Huang min: Machinery Design & Manufacture. Vol. 10 (2011), pp.167-169.

[4] Z. Jun-Hong, Chee Khiang, Z. Zhou-Wei, F.L. Lewis: Instrumentation and Measurement. Vol. 60 (2011), pp.547-549.

[5] K. Jemielniak, P.J. Arrazola: CIRP Journal of Manufacturing Science and Technology. Vol. 1 (2008), pp.97-102.

[6] Nie Peng. Chen Xin, Xu tao, Sun Baolin: Journal of Beijing University of Aeronautics and Astronautics. Vol. 37 (2001), pp.101-107.

[7] Wang Zhongmin, Wang Xinyi, Chen Andi, Jia Yupin: Journal of Beijing Institute of Technology. Vol. 10 (2001), pp.101-107.

[8] Tang Wei, Wang Haili, Zhuang Zijie, Hu Dejin: Tool Engineering. Vol. 43 (2009), pp.15-19.

[9] Liu Ping: Coal Mine Machinery. Vol. 34 (2013), pp.282-284.

[10] Sang-Gil Park, Hyoun-Jin Sim, Hae-Jin Lee, Jae-Eung Oh: Journal of Mechanical Science and Technology, Vol. 22 (2008), pp.2122-2133.