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Conclusions In this study, best injection molding process conditions for a bus ceiling lamp base are determined to enable minimum warpage. In finding optimum values, power of FE software MoldFlow, artificial neural network, statistical design of experiments and genetic algorithm is exploited. Mold temperature, melt temperature, packing pressure, packing pressure time and cooling time are considered as process parameters. FE analyses are conducted for combination of process parameters designed using statistical full factorial experimental design. An efficient predictive model for warpage is created using feed forward artificial neural net- work exploiting FE analysis results. Neural network model is integrated with an effective genetic algorithm to find the optimum process parameter values. Genetic optimization reduces the maximum warpage of the initial model by46.5%. Findings in this study also indicate that warpage is inversely proportional with mold temperature, melt temperature, packing pressure and packing pressure time but directly pro- portional with cooling time at least for the problem of interest. The optimization methodology in this study can be employed to improve part design and determine appropriate material properties ininjection molding of other plastic parts as well.
参考答案:这研究的结论, 对于一个公共汽车天花板灯基础的最好注入成型程序情况决定使最小的翘曲能够。 在发现最适宜的价值、 FE 软件 MoldFlow 的力量、人造的类神经网路方面，实验和遗传基因的运算法则的统计设计被开发。形成温度,融化温度, 包装压力, 包装压力计时，而且冷却的时间被考虑如程序叁数。 FE 分析为程序叁数 designe 的组合被引导给翘曲的一个有效率的预言性的模型被产生使用饲养向前的人造神经的网- 工作开发 FE 分析结果。 类神经网路模型与一个有效的遗传基因的运算法则整合找最适宜的程序叁数价值。 遗传基因的最佳化减少开始模型的最大翘曲 46.5% 。这研究的调查结果也指出翘曲对模子温度、融化温度感到反的比例,包装压力而且包装压力除了 directl 之外计时 职业者- portional 用冷却的时间至少为重要问题。 这研究的最佳化方法学能被雇用也改善部份设计而且决定其他塑料部份的适当物质财产 ininjection 成型。