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

被引：0

作者：

机构：

[1] School of Material Science and Engineering, Chongqing University

[2] Gree Electric Appliances, Ltd. of Chongqing

来源：

Yang, H. (yang_hai200888@126.com) | 1600年 / Chemical Industry Press, No. 3 Huixinli, Chaoyangqu, Beijing, 100029, China卷 / 64期

关键词：

Back propagation neural network; Crankshaft; Genetic algorithm; Taguchi method; Voids;

D O I：

10.3969/j.issn.0438-1157.2013.10.026

中图分类号：

学科分类号：

摘要：

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°C, melt temperature 210°C, 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. © All Rights Reserved.

引用

页码：3673 / 3678

页数：5

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