Optimal Design of the Grinding Parameter on Zr-4 Cladding Tubes Abrasive Belt Grinding Based on BP and GA

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Abstract:

This paper used BP(Neural Networks) to establish parameter optimization design method for Zr-4 clad tube belt grinding. The BP can get strong nonlinear mapping capability through training, obtain better grinding parameter model; GA(Genetic Algorithm) is not dependent on the specific situation of the problem and it has strong robustness, so it can provides optimization framework for Zr-4 clad tube belt grinding parameter optimization design of nonlinear optimization. This paper optimized the single objective and multi-objective of the abrasive belt life n and grinding roughness Rz, obtained satisfied optimization results and the corresponding grinding conditions.

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82-87

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September 2012

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] JianZhang Liu. Nuclear structure materials [M]. Beijing: chemical industry press, (2007).

Google Scholar

[2] Zhi Bo Ceng, En Bao Ding, Deng Hai Tang etc. Based on the BP neural network and genetic algorithm of the ship design optimization propellers[J]. Ship mechanics, 2010, 14 (1): 20-27.

Google Scholar

[3] Yun Huang, Zhi Huang. Modern belt grinding technology and engineering applications[M]. Chongqing: Chongqing university press, (2009).

Google Scholar

[4] HongBo Li, YuanMing Xu, YingLan Huang etc. Experimental design method and the BP-GA algorithm in composite structure optimization of application[J]. The plane design, 2007, 27 (3): 23-27.

Google Scholar

[5] GongMei Xie, Wei Huang. Based on artificial neural network of grinding surface roughness prediction research[J]. Precision manufacturing and automation, 2001, (4): 30-31.

Google Scholar

[6] WenTao Cheng. Based on neural network for form grinding surface roughness[D]. Hunan university, (2006).

Google Scholar

[7] Hu Deng. Based on neural network and genetic algorithm is proposed to the camshaft CNC grinding process parameters optimization[D]. Hunan university, (2008).

Google Scholar