Research on the Technique of Tool Wear Monitoring in Plunge Milling

Abstract:

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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.

Info:

Periodical:

Key Engineering Materials (Volumes 426-427)

Edited by:

Dunwen Zuo, Hun Guo, Guoxing Tang, Weidong Jin, Chunjie Liu and Chun Su

Pages:

468-471

DOI:

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

Citation:

X. D. Qin et al., "Research on the Technique of Tool Wear Monitoring in Plunge Milling", Key Engineering Materials, Vols. 426-427, pp. 468-471, 2010

Online since:

January 2010

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Price:

$35.00

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