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題 名 | Neural-based Manufacturing Process Optimization of Short Glass Fiber Reinforced Polycarbonate Composites in Injection Molding by Genetic Algorithm and Extension Set Analysis=短纖維強化複合材料於可拓基因演算式射出成形的類神經製程最佳化 |
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作 者 | 張士行; | 書刊名 | 中華技術學院學報 |
卷 期 | 23 2001.09[民90.09] |
頁 次 | 頁145-156 |
分類號 | 467.42 |
關鍵詞 | 短纖維強化複合材料; Optimization; Polycarbonate; Composites; Injection molding; Extension set; Genetic algorithm; Neural networks; CAE; |
語 文 | 英文(English) |
中文摘要 | 本文表示了一種快速且有效的方法對短纖維強化聚碳酸酯複合材料的製程無數最佳化的研究。眾人皆知,不同的射出參數如充填時間,融膠溫度,模具溫度和射出速度是被考慮的首要因素。本方法結合了基因演算法和可拓集合模型藉著多層類神經網路和電腦輔助模流分析軟體來模擬射出成形製程來預測纖維方向。這種方法能夠取代傳統的"一次調一個參數"的嘗試錯誤法。傳統的方法成本高耗時且不實在,故不易獲得最佳解。同時,纖維方向可由電腦輔助模擬預測剪力層厚度,再根據纖維排列方向,透過可拓論的分析,可求取最佳的製程參數。 |
英文摘要 | This paper presents a fast and effective methodology for optimization injection molding process parameters of short glass fiber reinforced polycarbonate composites. Various injection molding parameters, such as filling time, melt temperature, mold temperature and ram speed were considered. The methodology combines the use of genetic algorithm and Extension Set method processes modeled by multi-layer neural networks and a CAE flow simulation software which was used to simulate the injection molding process and to predict the fiber orientation. This method can replace the traditional "change-one-parameter-at-a-time" approach which is very inefficient, costly, time consuming and almost impracticable to yield an optimum solution. In the mean while, the fiber orientation were examined by CAE simulation to forecast shear layer thickness, simultaneously to check the accuracy of Extension Set. The results indicated that three distinct layers (frozen layer, shear layer and core layer) are observed from surface to core at various injection molding conditions. The fiber orientation is perpendicular to the melt flow direction in frozen layer and core layer, but it has opposite direction in shear layer. From the CAE analysis, we have got the optimum process parameters to obtain the thickest shear layer that is our target. |
本系統中英文摘要資訊取自各篇刊載內容。