Optimization for injection molding process conditions of the refrigeratory top cover using combination method of artificial neural network and genetic algorithms
被引:15
作者:
Shen, Changyu
论文数: 0引用数: 0
h-index: 0
机构:Zhengzhou Univ, Natl Engn Res Ctr Adv Polymer Proc Technol, Zhengzhou 450002, Peoples R China
Shen, Changyu
Wang, Lixia
论文数: 0引用数: 0
h-index: 0
机构:Zhengzhou Univ, Natl Engn Res Ctr Adv Polymer Proc Technol, Zhengzhou 450002, Peoples R China
Wang, Lixia
Cao, Wei
论文数: 0引用数: 0
h-index: 0
机构:Zhengzhou Univ, Natl Engn Res Ctr Adv Polymer Proc Technol, Zhengzhou 450002, Peoples R China
Cao, Wei
Wu, Jinxing
论文数: 0引用数: 0
h-index: 0
机构:Zhengzhou Univ, Natl Engn Res Ctr Adv Polymer Proc Technol, Zhengzhou 450002, Peoples R China
Wu, Jinxing
机构:
[1] Zhengzhou Univ, Natl Engn Res Ctr Adv Polymer Proc Technol, Zhengzhou 450002, Peoples R China
[2] Zhengzhou Univ, Inst Chem Engn, Zhengzhou 450002, Peoples R China
genetic algorithm;
injection molding;
modeling;
neural network;
process optimization;
D O I:
10.1080/03602550601152853
中图分类号:
O63 [高分子化学(高聚物)];
学科分类号:
070305 ;
080501 ;
081704 ;
摘要:
The process conditions have important influence on final part quality in injection molding, and how to get optimum process conditions is the key to improving part quality. Sinkmarks on the surfaces of injection-molded parts is one of the problems that limit the overall success of injection molding technology, and the presence of sinkmarks significantly impairs the surface quality of injection molded parts. A combination method of artificial neural network and genetic algorithms is proposed to optimize the injection molding process, and the processing parameters of a refrigeratory top cover are optimized using the combining method to minimize the sinkmarks on the part. The results indicate the combining method is an effective tool for the process optimization of injection molding.
机构:
City Univ Hong Kong, Div Bldg Sci & Technol, Kowloon, Hong Kong, Peoples R ChinaCity Univ Hong Kong, Div Bldg Sci & Technol, Kowloon, Hong Kong, Peoples R China
Chow, TT
Zhang, GQ
论文数: 0引用数: 0
h-index: 0
机构:
City Univ Hong Kong, Div Bldg Sci & Technol, Kowloon, Hong Kong, Peoples R ChinaCity Univ Hong Kong, Div Bldg Sci & Technol, Kowloon, Hong Kong, Peoples R China
Zhang, GQ
Lin, Z
论文数: 0引用数: 0
h-index: 0
机构:
City Univ Hong Kong, Div Bldg Sci & Technol, Kowloon, Hong Kong, Peoples R ChinaCity Univ Hong Kong, Div Bldg Sci & Technol, Kowloon, Hong Kong, Peoples R China
Lin, Z
Song, CL
论文数: 0引用数: 0
h-index: 0
机构:
City Univ Hong Kong, Div Bldg Sci & Technol, Kowloon, Hong Kong, Peoples R ChinaCity Univ Hong Kong, Div Bldg Sci & Technol, Kowloon, Hong Kong, Peoples R China
机构:
City Univ Hong Kong, Div Bldg Sci & Technol, Kowloon, Hong Kong, Peoples R ChinaCity Univ Hong Kong, Div Bldg Sci & Technol, Kowloon, Hong Kong, Peoples R China
Chow, TT
Zhang, GQ
论文数: 0引用数: 0
h-index: 0
机构:
City Univ Hong Kong, Div Bldg Sci & Technol, Kowloon, Hong Kong, Peoples R ChinaCity Univ Hong Kong, Div Bldg Sci & Technol, Kowloon, Hong Kong, Peoples R China
Zhang, GQ
Lin, Z
论文数: 0引用数: 0
h-index: 0
机构:
City Univ Hong Kong, Div Bldg Sci & Technol, Kowloon, Hong Kong, Peoples R ChinaCity Univ Hong Kong, Div Bldg Sci & Technol, Kowloon, Hong Kong, Peoples R China
Lin, Z
Song, CL
论文数: 0引用数: 0
h-index: 0
机构:
City Univ Hong Kong, Div Bldg Sci & Technol, Kowloon, Hong Kong, Peoples R ChinaCity Univ Hong Kong, Div Bldg Sci & Technol, Kowloon, Hong Kong, Peoples R China