A study of optimization of injection molding process parameters for SGF and PTFE reinforced PC composites using neural network and response surface methodology
| Autor: | Yung-Kuang Yang, Chorng-Jyh Tzeng, Chih-Hung Tsai, Yu-Hsin Lin |
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| Rok vydání: | 2012 |
| Předmět: |
Engineering
Artificial neural network business.industry Mechanical Engineering Molding (process) Industrial and Manufacturing Engineering Computer Science Applications Injection molding process Flexural strength Control and Systems Engineering visual_art Ultimate tensile strength Genetic algorithm visual_art.visual_art_medium Response surface methodology Composite material Polycarbonate business Software |
| Zdroj: | The International Journal of Advanced Manufacturing Technology. 63:691-704 |
| ISSN: | 1433-3015 0268-3768 |
| DOI: | 10.1007/s00170-012-3933-6 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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