Milling Force Forecast of the Matching of Lengthened Shrink-Fit Holder and Cutter in High Speed Machining

Hou Ming Zhou; Jian Xin Deng; Wen Wei Xie

doi:10.4028/www.scientific.net/AMR.139-141.827

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 139-141Milling Force Forecast of the Matching of...

Milling Force Forecast of the Matching of Lengthened Shrink-Fit Holder and Cutter in High Speed Machining

Abstract:

Present work of this paper focus on developing a milling force model according to the characteristic of the matching of lengthened shrink-fit holder (LSFH) and cutter using back propagation neural network (BPNN). Time parameter is taken as a factor of the input vector besides 6 processing conditions which mainly affect the milling force, and then the forecasting of 3D transient milling forces are achieved. A lot of milling experiments were performed to get training and testing samples and a Matlab program was designed to evaluate and optimize the network. The test experiments show that the forecasting results are well agreed with the experimental results and the errors of 3D force components are less than 0.18. Besides an extended performance, the BPNN model has higher efficiency and higher accuracy than the customary analytical model.

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Periodical:

Advanced Materials Research (Volumes 139-141)

Pages:

827-830

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.139-141.827

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Online since:

October 2010

Authors:

Hou Ming Zhou, Jian Xin Deng, Wen Wei Xie

Keywords:

Artificial Neural Network (ANN), High Speed Milling (HSM), Lengthened Shrink-Fit Holder, Milling Force

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References

[1] G.M. Kim, B.H. Kim: Int J Machine Tools Manuf. Vol. 43 (2003), pp.917-924.

Google Scholar

[2] M. Yang, H. Park: Int J Machine Tools Manuf. Vol. 31 (1991), pp.45-54.

Google Scholar

[3] K. Yamazaki, N. Kojima, et al: Annals of the CIRP40. Vol. 1 (1991), pp.479-482.

Google Scholar

[4] W. Wang: Journal of Manufacturing Systems. Vol. 1 (1998), pp.57-65.

Google Scholar

[5] H.S. Feng, C.H. Menq: Int J Machine Tool Manuf. Vol. 34 (1994), pp.697-6[] H.S. Feng, C.H. Menq: Int J Machine Tool Manuf. Vol. 34 (1994), pp.711-719.

DOI: 10.1016/0890-6955(94)90053-1

Google Scholar

[7] M. Matjaz, C. Franci: Robotics and Computer Integrated Manufacturing. Vol. 19(2003), pp.99-106.

Google Scholar

[8] A. Lamikiz, L.N. Lo'pez, J.A. Sa'nchez: Int J Machine Tool Manuf. Vol. 44 (2004), pp.1511-1526.

Google Scholar

[9] FEISI Technology production: Design center Neural Network Theory and MATLAB7 implements (Beijing Electronic Industry Press, China 2005).

Google Scholar

[10] L. Hontoria, J. Aguilera, P. Zufiria: Solar Energy. Vol. 79 (2005), pp.523-530.

Google Scholar

[11] H.M. Zhou: Journal of Materials Processing Technology. Vol. 207 (2008), pp.154-162.

Google Scholar