應用田口法與適應性類神經模糊推論系統於塑膠射出成型之多目標最佳化研究

機構典藏 > College of Engineering > Graduate Institute & Department of Mechanical and Electro-Mechanical Engineering > Thesis > Item 987654321/114738

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Title:	應用田口法與適應性類神經模糊推論系統於塑膠射出成型之多目標最佳化研究
Other Titles:	Application of Taguchi method and adaptive network-based fuzzy inference system to multi-objective optimization of plastic injection molding
Authors:	周格仲;Zhou, Ge-Zhong
Contributors:	淡江大學機械與機電工程學系碩士班葉豐輝;Yeh, Fung-Huei
Keywords:	田口法;適應性類神經模糊推論系統;塑膠射出成型;最佳化;Taguchi method;Adaptive Network-Based Fuzzy Inference System;Plastic injection molding;Optimization
Date:	2017
Issue Date:	2018-08-03 15:02:23 (UTC+8)
Abstract:	本文係使用田口法與適應性類神經模糊推論系統於探討塑膠射出成型最佳化問題，並應用多目標決策使最佳化結果能夠選擇多樣性，以達到多目標最佳化之目的。研究中使用繪圖軟體進行實體繪製，接而使用Moldex3D執行模流分析，選擇材料溫度、模具溫度、填充時間、保壓壓力、保壓時間與冷卻時間做為控制因子，針對縫合線長度、翹曲變形、體積收縮率與週期時間等目標做探討。最佳化首先導入田口法利用直交表配合信號雜訊比與變異數分析，獲得一組較佳射出參數與因子的影響程度，由分析結果顯示，保壓時間為這六個因子中影響最為顯著的因子。其次將所得到之結果，再導入適應性類神經模糊推論系統，以得到最佳化加工參數組合。最後運用多目標決策方法獲取各目標間得以平衡的加工參數組合，以達多目標最佳化研究之目的。　　由模擬分析結果顯示，翹曲變形最佳化可同時達成體積收縮率最佳化，但週期時間須較長，如翹曲變形最佳化為0.089mm，體積收縮率僅為0.874%，但週期時間為51.909秒。由於多目標無法達成所有目標值同時最佳化，故使用多目標決策法，利用每個單一目標下所得之最佳化結果，在翹曲變形與體積收縮率可接受的條件下，降低週期時間，得到一組多目標最佳化加工參數，如翹曲變形為0.251mm，體積收縮率為1.860%，而週期時間降為46.842秒。故本研究可證實田口法與適應性類神經模糊推論系統和多目標決策能有效應用於塑膠射出成型之多目標最佳化。 The thesis employs Taguchi Method and Adaptive Network-Based Fuzzy Inference System (ANFIS) to explore the optimization of plastic injection molding, and uses multi-objective decision-making method to diversify the optimization results and thereby achieve the purpose of multi-objective optimization. In the study, the drawing software is used to perform the tangible objects drawing, and then Moldex3D is used to perform the mold flow analysis. The material temperature, mold temperature, filling time, packing pressure, holding time and cooling time are selected as control factors to investigate the warpage, volume shrinkage and cycle time. As to the optimization, the orthogonal array of Taguchi method is applied to examine signal-to-noise ratio and analysis of variance. The influence degree of factors and the better parameters of injection molding are obtained. According to the analysis results, holding time had the most significant influence out of the six factors. Besides, ANFIS is incorporated into the acquired results to obtain an optimized parametric combination. Finally, the multi-objective decision-making method is used to acquire a parametric combination that serves to balance all objectives and to achieve the purpose of multi-objective optimization research. The results of the simulation analysis show that the optimization of the warpage can be achieved at the same time to optimize the volume shrinkage. But the cycle time grows longer. For example, the warpage is optimized to be 0.089mm, the volume shrinkage is only 0.874%, but the cycle time is 51.909 seconds. With many objectives to be optimized, it is unlikely to optimize all objective values simultaneously. Therefore, using the optimization results acquired from each objective, the multi-objective decision-making method is applied to minimize the cycle time under the condition that the warpage and the volume shrinkage are acceptable, and a set of multi-objective optimization parameters are obtained. For example, the warpage is 0.251mm, the volume shrinkage is 1.860%, and the cycle time is reduced to 46.842 seconds. As proven by this study, Taguchi Method, ANFIS and multi-objective decision-making method can be effectively applied to multi-objective optimization of plastic injection molding.
Appears in Collections:	[Graduate Institute & Department of Mechanical and Electro-Mechanical Engineering] Thesis

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