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.
