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Optimization of injection‐molding process for mechanical properties of polypropylene components via a generalized regression neural network
Author(s) -
Shie JieRen
Publication year - 2008
Publication title -
polymers for advanced technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.61
H-Index - 90
eISSN - 1099-1581
pISSN - 1042-7147
DOI - 10.1002/pat.976
Subject(s) - taguchi methods , molding (decorative) , polypropylene , artificial neural network , materials science , sequential quadratic programming , orthogonal array , ultimate tensile strength , design of experiments , process (computing) , composite material , computer science , quadratic programming , artificial intelligence , mathematical optimization , mathematics , statistics , operating system
This study analyzed contour distortions, wear and tensile properties of polypropylene (PP) components applied in the interior coffer of automobiles. A hybrid method integrating a trained generalized regression neural network (GRNN) and a sequential quadratic programming (SQP) method is proposed to determine an optimal parameter setting of the injection‐molding process. The specimens were prepared under different injection‐molding conditions by changing melting temperatures, injection speeds, and injection pressures. Average contour distortions at six critical locations, wear and tensile properties were selected as the quality targets. Sixteen experimental runs, based on a Taguchi orthogonal array table, were utilized to train the GRNN and then the SQP method was applied to search for an optimal setting. The trained GRNN was capable of predicting average contour distortions, wear and tensile properties at various injection‐molding conditions. In addition, the analysis of variance (ANOVA) was implemented to identify significant factors for the molding process and the proposed algorithm was compared with traditional schemes like the Taguchi method and the design of experiments (DOE) approach. Copyright © 2007 John Wiley & Sons, Ltd.