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題名：運用田口方法、倒傳遞類神經網路、基因演算法及混合粒子群演算法與基因演算法於塑膠射出成形最佳化系統之研究
作者：丹尼Denni Kurniawan
作者(外文)：Denni Kurniawan
校院名稱：中華大學
系所名稱：科技管理博士學位學程
指導教授：陳文欽
學位類別：博士
出版日期：2014
主題關鍵詞：塑膠射出成型；田口方法；變異數分析；倒傳遞類神經網路；基因演算法；混合粒子群演算法與基因演算法；plastic injection molding；Taguchi method；ANOVA；BPNN；GA；PSO-GA
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　　近年來，高分子材料應用於射出成形(PIM)之技術成長迅速。此外，因生產循環時間短、產品重量輕、高表面品質等優點，更讓塑膠射出成形成為產業競爭中的生存之道。除此之外，射出成形的製程甚為複雜，不適當之材質挑選、製程參數及模具設計，都會影響塑膠製品之品質。因此，如何提升產品品質仍是重要的議題。良好的製程參數設定是避免或減少塑膠製品缺陷的其中一種方法。過去射出成形的製程參數設定，全仰賴工程師的經驗與試誤法。然而，此法對於複雜的製程既無效也不合適。本論文提出兩階段最佳化系統，為找出射出成形多重品質特性之最佳製程參數組合。本研究使用田口方法、倒傳遞類神經網路(BPNN)、基因演算法(GA)及混合粒子群演算法與基因演算法(PSO-GA)以找出最佳參數設定。在本研究中將熔膠溫度、射出速度、保壓壓力、保壓時間及冷卻時間為製程控制參數，長度與翹曲為品質特性。首先，透過田口直交表進行L2556實驗。根據田口實驗法得來的結果計算信號雜訊比，依據產品品質找出製程參數組合。其次，使用變異數分析法(ANOVA)找出多品質特性之製程參數組合。第一階段S/N比最佳化，運用倒傳遞神經網路(BPNN)建構S/N比預測器，結合S/N比預測器與基因演算法(GA)進行全域搜尋，使各品質特性之S/N比值都最大化，此階段將使製程變異降至最低；第二階段品質最佳化，利用倒傳遞神經網路建構品質預測器，結合S/N比預測器、品質預測器與混合粒子群演算法與基因演算法(hybrid PSO-GA)進行區域局部搜尋，將品質逼近目標規格，找出最符合品質規格且製程最為穩定之最佳製程參數組合。最後進行驗證實驗以評估此系統之效能。驗證實驗結果顯示，本研究之最佳化系統不僅提升塑膠零件之品質，亦可有效降低製程變異。
　　Applications of polymer material in injection molding application are growing very fast in last decades. Moreover, several advantages, such as short cycle time production, light weight of products, and high surface quality, make plastic injection molding (PIM) work as a solution for industries to survive in competitive world. Besides these advantages, PIM is a more complex process than it is previously thought. Inappropriate material selection, process parameters, part and mold designs can affect the quality of plastic products. Therefore, investigation to improve product quality still becomes an important issue to be conducted. A well-controlled parameter setting is one of the solutions to avoid or reduce defect in plastic products. Previously, process parameters in PIM relied on the technician's experience using trial-and-error approach. However, this approach is not effective and unsuitable for complex manufacturing processes. This study presents a two- stage optimization system to find optimal process parameters of multiple quality characteristics in the PIM process. The Taguchi method, back-propagation neural network (BPNN), genetic algorithms (GA), and combination of particle swarm optimization and genetic algorithms (PSO-GA) are used to find optimum parameter settings. Melt temperature, injection velocity, packing pressure, packing time, and cooling time are selected as initial process parameters. Length and warpage are employed as the product quality. The experimental work is conducted using the Taguchi orthogonal array. According to the result from the Taguchi experiment, S/N ratio is calculated to find the best combination settings for product quality. Then, analysis of variance (ANOVA) is used to determine significant factors of the control parameters. Moreover, the S/N ratio predictor and quality predictor are constructed using BPNN. In the first stage optimization, S/N ratio predictor and GA are used to reduce variance of quality characteristic. In the second stage optimization, the S/N ratio predictor and quality predictor with hybrid PSO-GA are used to find optimal parameter settings for quality characteristic and stability of the process. Finally, three confirmation experiments are conducted to assess the effectiveness of the proposed system. Experimental results show that the proposed system not only improves the quality of plastic parts, but also reduces variability of process effectively.
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