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- Title
A study of optimization of injection molding process parameters for SGF and PTFE reinforced PC composites using neural network and response surface methodology.
- Authors
Tzeng, Chorng-Jyh; Yang, Yung-Kuang; Lin, Yu-Hsin; Tsai, Chih-Hung
- Abstract
This study analyzed variations of mechanical characteristics that depend on the injection molding techniques during the blending of short glass fiber and polytetrafluoroethylene reinforced polycarbonate composites. A hybrid method including back-propagation neural network (BPNN), genetic algorithm (GA), and response surface methodology (RSM) are proposed to determine an optimal parameter setting of the injection molding process. The specimens are prepared under different injection molding processing conditions based on a Taguchi orthogonal array table. The results of 18 experimental runs were utilized to train the BPNN predicting ultimate strength, flexural strength, and impact resistance. Simultaneously, the RSM and GA approaches were individually applied to search for an optimal setting. In addition, the analysis of variance was implemented to identify significant factors for the injection molding process parameters and the result of BPNN integrating GA was also compared with RSM approach. The results show that the RSM and BPNN/GA methods are both effective tools for the optimization of injection molding process parameters.
- Subjects
MATHEMATICAL optimization; MACHINE molding (Founding); POLYTEF; POLYCARBONATES; ARTIFICIAL neural networks; RESPONSE surfaces (Statistics); GENETIC algorithms; GLASS fibers; MECHANICAL behavior of materials
- Publication
International Journal of Advanced Manufacturing Technology, 2012, Vol 63, Issue 5-8, p691
- ISSN
0268-3768
- Publication type
Article
- DOI
10.1007/s00170-012-3933-6