Study on Injection Molding Parameters for Thin-Shell Plastic Parts Using a Neural Network-Based Approach

NCUIR > Engineering College > Departmant of Mechanical Engineering > journal & Dissertation > Item 987654321/50166

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/50166

Title:	Study on Injection Molding Parameters for Thin-Shell Plastic Parts Using a Neural Network-Based Approach
Authors:	Lin,JC;Yang,YK;Hsiao,YH;Jeng,MC
Contributors:	機械工程學系
Date:	2010
Issue Date:	2012-03-27 17:05:08 (UTC+8)
Publisher:	國立中央大學
Abstract:	This study analyzed variation of warpage and tensile properties depending on injection molding parameters during production of thin-shell plastic components. A hybrid method integrating back-propagation neural network (BPNN), genetic algorithm (GA), and simulated annealing algorithm (SAA) are proposed to determine an optimal parameter setting of the injection-molding process. The results of 18 experimental runs were utilized to train the BPNN predicting warpage and tensile properties at various injection-molding conditions and then the GA and SAA approaches were applied to individual search for an optimal setting. The results show that the combinations of BPNN/ GA and BPNN/SAA methods are effective tool for the optimization of injection molding parameter.
Relation:	POLYMER-PLASTICS TECHNOLOGY AND ENGINEERING
Appears in Collections:	[Departmant of Mechanical Engineering ] journal & Dissertation

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