In this study, optimum values of process parameters in injection molding of a bus ceiling lamp base to achieve minimum warpage are determined. Mold temperature, melt temperature, packing pressure, packing pressure time and cooling time are considered as process parameters. In finding optimum values, advantages of finite element software MoldFlow, statistical design of experiments, artificial neural network and genetic algorithm are exploited. Finite element analyses are conducted for combination of process parameters designed using statistical three-level full factorial experimental design. statistical three-level full factorialexperimental design. A predictive model for warpage is created using feed forward artificial neural network exploiting finite element analysis results. Neural network model is validated for predictive capability and then interfaced with an effective genetic algorithm to find the optimum process parameter values. Upon optimization,it is seen that genetic algorithm reduces the warpage of the initial model of the bus ceiling lamp base by 46.5%.
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在该研究中, 确定了汽车吊灯座注射成型过程参数最优值，以达到最小翘曲效果。成型温度, 熔化温度, 保压压力, 保压压力时间和冷却时间亦一并视为过程参数. 在探球最优值的过程中，也对有限元软件MoldFlow的优点，试验统计设计, 人工神经元网络基因算法等进行了探索.使用统计学上的三级全因子实验设计法进行有限元分析来研究过程因子。使用向前给料的采用无限元分析结果的人工神经元网络方法制作翘曲预测模型。用人工神经元网络模型预测过程性能，同时用有效的基因算法来寻找最佳过程参数值. 通过优化，相对原先的汽车吊灯座注射成型过程，基因算法将翘曲程度减少了46.5%.
