射出成型是高分子材料最常使用的製造方法，而射出成型製程的參數設定，因太依賴設計者的經驗或試誤法，所以會引起時間上的浪費。因此有必要在加工前有設定加工參數的正確數據資訊。本論文採用苯乙烯三嵌段聚合物與聚丙烯之複合材料，探討射出成型最佳拉應力與硬度之加工參數，其中加工參數包含射出溫度、混煉比例、螺桿轉速以及射出速度；並應用類神經網路來預測加工參數與產品品質之間的交互影響。最後，以生產品質作為遺傳算法的目標函數，並以預測的產品品質作為遺傳算法的拘束條件，之後我們透過複製、交配以及突變的計算找出最大適應度值的加工參數。由研究結果顯示，結合類神經網路與基因遺傳演算法具有找出射出成型最佳加工參數的效果。

Abstract
The injection molding is the mostly used manufacturing method for Polymers. The processing parameters setting of injection molding heavily depends on designers' experience or trial-and-error procedures, so it consumes much time. Thus, it is prerequisite to set up the correct processing parameters before processing. This paper uses a compound material of SEBS and polypropylene to investigate the optimal processing parameters of injection molding for optimizing tensile stress and hardness. The parameters include injection temperature, mixing ratio, screw speed and injection speed. The neural network model is applied to predict the reciprocal effects between processing parameters and product quality. For the genetic algorithm, it takes the product quality as the basis of the objective function and the quality predictions as the basis of constraint conditions. We calculate the maximum fitness value of the processing parameters with reproduction, crossover and mutation. The results show that the proposed method can find the optimum processing parameters of injection molding for multiple qualities.

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