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. 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. 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: 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: