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

WANG Menghan; YANG Hai; LI Yanzhao; ZHOU Jie; Huang Qianglin; YAO Xiaobing

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

更新时间：2025-12-10

- Elimination of voids in crankshaft through a hybrid of back propagation neural network and genetic algorithm
- CIESC Journal Issue 10, Pages: 3673-3678(2013)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0438-1157.2013.10.026
  CLC： Q320.66
- Online First：05 October 2013，
  
  Published：05 October 2013
- 稿件说明：
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WANG Menghan, YANG Hai, LI Yanzhao, et al. Elimination of voids in crankshaft through a hybrid of back propagation neural network and genetic algorithm[J]. CIESC Journal, 2013, (10): 3673-3678.
DOI：

WANG Menghan, YANG Hai, LI Yanzhao, et al. Elimination of voids in crankshaft through a hybrid of back propagation neural network and genetic algorithm[J]. CIESC Journal, 2013, (10): 3673-3678. DOI： 10.3969/j.issn.0438-1157.2013.10.026.

摘要

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.

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