Tool Wear Monitoring in Milling Processes Based on Time-Frequency Analysis of Acoustic Emission

Lu Zhang; Guo Feng Wang; Xu Da Qin; Xiao Liang Feng

doi:10.4028/www.scientific.net/AMM.141.574

Paper Titles

The Effect of the Restrictor Structural Parameters on the Static Properties of the Aerostatic Guide Way by Finite Element Analysis
p.554

The Machining Parameters Online Monitoring Method for Stability Prediction
p.559

The Online Monitoring of Surface Quality Based on Time-Frequency Spectrum Analysis of Acoustic Emission
p.564

The Optimization Design and Analysis of Motorcycle Frame Structure
p.569

Tool Wear Monitoring in Milling Processes Based on Time-Frequency Analysis of Acoustic Emission
p.574

Vehicle Frame Fatigue Life Prediction Based on Finite Element and Multi-Body Dynamic
p.578

Application of Growth Production Design DNA Calculation Inference on the Building Process of Packing Product Line Virtual Prototype
p.589

Reliability Analysis Based on Efficient Simulation
p.594

Research on Ride Comfort of Off-Road Vehicles
p.601

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 141Tool Wear Monitoring in Milling Processes Based on...

Tool Wear Monitoring in Milling Processes Based on Time-Frequency Analysis of Acoustic Emission

Abstract:

Tool wear monitoring plays an important role in the automatic machining processes. Therefore, it is necessary to establish a reliable method to predict tool wear status. In this paper, features of acoustic emission (AE) extracted from time-frequency domain are integrated with force features to indicate the status of tool wear. Meanwhile, a support vector machine (SVM) model is employed to distinguish the tool wear status. The result of the classification of different tool wear status proved that features extracted from time-frequency domain can be the recognize-features of high recognition precision.

You might also be interested in these eBooks

Functional Manufacturing and Mechanical Dynamics II

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 141)

Pages:

574-577

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.141.574

Citation:

Cite this paper

Online since:

November 2011

Authors:

Lu Zhang, Guo Feng Wang, Xu Da Qin, Xiao Liang Feng

Keywords:

Acoustic Emission (AE), Support Vector Machine (SVM), Time-Frequency Analysis (TFA), Tool Wear Monitoring

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Zhu Kunpeng, Wong Yoke San and Hong Geok Soon: J. International Journal of Machine Tool & Manufacturing Vol. 49 (2009), pp.537-553.

Google Scholar

[2] R. Du: J. Engineering Applications of Artificial Intelligence Vol. 12 (1999), pp.585-597.

Google Scholar

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

Google Scholar

[4] Wang Jisheng, Yu Junxi and Huang Weigong: J. Journal of Vibration, Measurement& Diagnosis Vol. 28 (2008), P. 274-276.

Google Scholar

[5] Yang Jie: Research of AE signal processing and analysis technique, D. Jilin University.

Google Scholar

[6] Yinhu Cui, Guofeng Wang and Dongbiao Peng: J. Mechanics and Materials Vol. 34-35 (2010), pp.1746-1751.

Google Scholar

[7] Xiao Jianhua: Method of intellectual mode recognition, first ed. (South China University of Technology Press, Guangzhou 2006).

Google Scholar

[8] WANG Guo-feng, LI Qi-ming et al: J. Journal of Tianjin University Vol. 44 (2011), pp.36-39.

Google Scholar

[9] Jianfei Dong, K.V.R. Subrahmanyam et al: J. The International Journal of Advanced Manufacturing Technology Vol. 30 (2006), pp.797-807.

Google Scholar