Elimination of voids in crankshaft through a hybrid of back propagation neural network and genetic algorithm

doi:10.3969/j.issn.0438-1157.2013.10.026

CIESC Journal ›› 2013, Vol. 64 ›› Issue (10): 3673-3678.DOI: 10.3969/j.issn.0438-1157.2013.10.026

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Elimination of voids in crankshaft through a hybrid of back propagation neural network and genetic algorithm

WANG Menghan¹, YANG Hai¹, LI Yanzhao¹, ZHOU Jie¹, Huang Qianglin²,YAO Xiaobing²

1. School of Material Science and Engineering, Chongqing University, Chongqing 400030, China;
2. Gree Electric Appliances, Ltd.of Chongqing, Chongqing 400039, China

Received:2013-03-20 Revised:2013-06-26 Online:2013-10-05 Published:2013-10-05
Supported by:
supported by the Fundamental Research Funds for the Central Universities(CDJZR11130003).

前馈神经网络与遗传算法相结合解决曲轴中心缩孔

王梦寒¹, 杨海¹, 李雁召¹, 周杰¹, 黄强林²,姚小兵²

1. 重庆大学材料科学与工程学院, 重庆 400030;
2. 格力电器(重庆)有限公司, 重庆 400039

通讯作者: 杨海
作者简介:王梦寒(1975-),女,博士,副教授。
基金资助:
中央高校基本科研业务费资助项目(CDJZR11130003)

Abstract

Abstract: A method of combining back propagation neural network (BP neural network)and genetic algorithm was proposed to optimize the process parameters and eliminate the voids in crankshaft.Mold temperature,melt temperature,packing pressure and gate size were taken as design variables and sink marks were taken as optimization goal.Computer aided engineering(CAE)simulation was performed based on the Taguchi method.A BP neural network model was developed to obtain the mathematical relationship between optimization goal and design variables,and genetic algorithm was used to optimize the process parameters.The optimal process parameters were mold temperature 80℃,melt temperature 210℃,packing pressure 110 MPa,gate size 1mm.Finally,the voids in the crankshaft could be eliminated by using the optimized process parameters in actual factory production.

Key words: crankshaft, voids, back propagation neural network, genetic algorithm, Taguchi method

关键词: 曲轴, 缩孔, BP神经网络, 遗传算法, 田口方法

CLC Number:

TQ320.66

WANG Menghan, YANG Hai, LI Yanzhao, ZHOU Jie, Huang Qianglin, YAO Xiaobing. Elimination of voids in crankshaft through a hybrid of back propagation neural network and genetic algorithm[J]. CIESC Journal, 2013, 64(10): 3673-3678.

王梦寒, 杨海, 李雁召, 周杰, 黄强林, 姚小兵. 前馈神经网络与遗传算法相结合解决曲轴中心缩孔[J]. 化工学报, 2013, 64(10): 3673-3678.

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Elimination of voids in crankshaft through a hybrid of back propagation neural network and genetic algorithm

前馈神经网络与遗传算法相结合解决曲轴中心缩孔

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