射 出 成 型 模 流 分 析 與 製 程 參 數 優 化

Injection Mold Flow Analysis and Optimization of Process Parameters

吳學霖(Hsueh-Lin wu)

指導教授 ： 翁芳柏

元智大學/工程學院/機械工程學系/碩士(2012年)

摘要

本研究對象為塑膠裝配零件-壓板，射出成型模流分析及控制製程參數來改善翹曲變形量之情形。利用3D繪圖軟體Pro/E 設計產品形狀，再利用Moldflow軟體進行模流分析。以塑料熔融溫度、模具溫度、射出時間、保壓壓力、保壓時間及冷卻時間等六個製程參數作為初始控制因子，以 Moldflow 軟體進行模擬並搭配田口L25 (56) 直交表及變異數分析找出製程之顯著控制因子，接著透過田口法結合類神經網路方法訓練出一射出成型輸入輸出關係之函數，便透過輸入製程參數便可獲得一組對應之翹曲變形值，應用類神經網路搭配具備全域求解之特性之基因演算法，搜尋出最小翹曲變形量製程參數組合。這組最佳化參數可以作未來射出成型的製程參數條件的參考，減少試模時間及降低生產成本，產品品質才能穩定量產。

關鍵字

射出成形 ； 模流分析 ； 田口實驗設計法 ； 類神經網路 ； 基因演算法

並列摘要

In this study, optimum values of process parameters in injection molding of a plastic assembly parts Pressure plate to achieve minimum warpage are determined. We use the 3D CAD software Pro/E to design the shape of the product, and then combine moldflow simulation analysis. Melt temperature, mold temperature, injection time, packing pressure, packing time, cooling time of six control process parameters as an initial factor, In finding the optimum values, finite element software MoldFlow, Taguchi method with L25 (56) Orthogonal Array and ANOVA, artificial neural network and genetic algorithm are exploited.A predictive model for warpage is created using 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.

This set of optimized parameters can be done to the future reference of the injection molding process parameters conditions, reduce tryout time and reduce production costs, product quality in order to stabilize production.

並列關鍵字

Genetic algorithms ； Injection molding ； Moldflow ； Taguchi method ； Artificial neural network

參考文獻

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