Research on the Technique of Tool Wear Monitoring in Plunge Milling

Xu Da Qin; X.L. Ji; X. Yu; S. Hua; Wei Cheng Liu; Wang Yang Ni; Yi Xiong Liu

doi:10.4028/www.scientific.net/KEM.426-427.468

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HomeKey Engineering MaterialsKey Engineering Materials Vols. 426-427Research on the Technique of Tool Wear Monitoring...

Research on the Technique of Tool Wear Monitoring in Plunge Milling

Abstract:

The technique of tool wear monitoring in plunge milling is studied. The mean of cutting force signals and the root mean square (RMS) of vibration signals are selected as characteristic quantities. The model between tool wear and the characteristic quantities is built using BP artificial neural network. The result of experiment shows that the module is fit for plunge milling wear’s testing under cutting condition, and it is helpful to monitoring plunge milling tool strong wear.

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Periodical:

Key Engineering Materials (Volumes 426-427)

Pages:

468-471

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.426-427.468

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Online since:

January 2010

Authors:

Xu Da Qin, X.L. Ji, X. Yu, S. Hua, Wei Cheng Liu, Wang Yang Ni, Yi Xiong Liu

Keywords:

BP Artificial Neural Network, Plunge Milling, Tool Wear Monitoring

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