A New Tool Wear Modeling Method Based on Project Pursuit Regression

Qian Jian Guo; Lei He; Guang Ming Zhu

doi:10.4028/www.scientific.net/AMR.239-242.298

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 239-242A New Tool Wear Modeling Method Based on Project...

A New Tool Wear Modeling Method Based on Project Pursuit Regression

Abstract:

Tool wear is a major contributor to the dimensional errors of a workpiece in precision machining. Tool wear modeling is an effective way to predict the tool wear loss in milling process. In this paper, different milling conditions are estimated as the input variables, tool wear loss is estimated as the output variable, and projection pursuit regression (PPR) method is proposed to establish the relationship between the input and the output variables. A real-time tool wear loss estimator is developed based on the PPR model, the experimental and estimated results are found to be in satisfactory agreement with average error lower than 10%.

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Periodical:

Advanced Materials Research (Volumes 239-242)

Pages:

298-301

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.239-242.298

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Online since:

May 2011

Authors:

Qian Jian Guo, Lei He, Guang Ming Zhu

Keywords:

CNC milling machine, Online Estimation, Project Pursuit Regression, Tool Wear Modeling

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References

[1] C. C. Jacob, C.C. Joseph: International Journal of Advanced Manufacturing Technology Vol. 25 (2005), p.427.

Google Scholar

[2] P. Palanisamy, I. Rajendran, S.Shanmugasundaram: International Journal of Machine Tools and Manufacture Vol. 37 (2008), p.29.

Google Scholar

[3] C. Zhang, X.H. Liu, J.W. Fang, L.S. Zhou: International Journal of Machine Tools and Manufacture Paper in press.

Google Scholar

[4] F. Girardin, D. Remond and J.F. Rigal: International Journal of Material Form Vol. 3 (Suppl 1), p.463.

Google Scholar

[5] G.C. Smith, S.S. Li: International Journal of Advanced Manufacturing Technology Vol. 25 (2005), p.270.

Google Scholar

[6] R. Quiza, L. Figueira and P. Davim: International Journal of Advanced Manufacturing Technology Vol. 37 (2008), p.641.

Google Scholar

[7] H.H. Shahabi, M.M. Ratnam: International Journal of Advanced Manufacturing Technology Vol. 38 (2008), p.718.

Google Scholar

[8] Y. Luo: International Journal of Advanced Manufacturing Technology Vol. 23 (2004), p.546.

Google Scholar