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題名：多顆LED照明模組成形參數最佳化系統之研究
作者：賴東燦
作者(外文)：LAI,Dong-Can
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
王珉玟
學位類別：博士
出版日期：2012
主題關鍵詞：反應曲面法；倒傳遞類神經網路；基因演算法；模流分析；粒子群演算法；擬牛頓法；response surface methodology；back-propagation neural networks；genetic algorithms；mold flow analysis；Particle swarm optimization；Quasi-Newton's method
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發光二極體LED(Light Emitting Diode, LED)在燈具模組的運用設計上，往往都僅是著重於LED模組的排列，用以取得發光照明的效果，在光學透鏡模組研究上亦是缺乏多品質特性的成形參數最佳化研究，因此本研究將利用電腦輔助工程 (Computer-Aided Engineering, CAE)配合實驗設計(Design of Experiment, DOE)及多種最佳化演算法找出一組符合品質特性為發光角與照度的多顆LED照明模組成形最佳化參數組合，輔以機台射出，並量測實際之發光角度與照度，加以驗證其可行性，這是本研究的目的。
本研究提出一個LED透鏡設計與成形參數最佳化系統，以多顆LED照明的透鏡為研究載具，研究主要在闡述一個完整的照明模組成形最佳化開發系統，結合實驗設計(Design of Experiment, DOE)透過電腦輔助工程(Computer-Aided Engineering, CAE)模流分析(Moldex3D) 實驗與變異數分析(Analysis of Variance, ANOVA)及反應曲面法(Response Surface Methodology, RSM)探討多目標品質特性，並利用倒傳遞類神經網路(Back-Propagation Neural Network, BPNN) 建立LED透鏡成形參數的品質預測器再結合基因演算法(Genetic Algorithms, GA) 粒子族群最佳化演算法(Particle Swarm Optimization, PSO) 及基因演算法結合擬牛頓法(Davidon-Flecher-Powell, DFP)輔助設計成形參數最佳化探討與製程參數之關係，找出實際射出成形參數最佳組合。最後以實際開模方式製成射出成形模具，並以成形最佳化所得的多目標品質特性射出成形參數，做為CAE與實際射出成形機的參數，執行透鏡成品的模流分析與實際射出，並量取其發光角與照度用以驗證本研究之正確性。
本研究結果顯示，使用RSM結合GA-DFP之數值模擬成形參數最佳化法比純GA最佳化法減少近30倍的搜尋世代數(1100:40)，大量的減少電腦搜尋的次數，而且對品值特性約有5%的提昇，將可使LED透鏡光學設計與生產技術提昇，大幅減少LED透鏡的開發時間。
The application of the light-emitting diode (LED) lamp-module design normally focuses on LED arrangements of lamp modules which will unveil the illumination effectiveness of lamp lighting modules. However, it almost lacks the study of the process parameters optimization of optical lens module for the multi-quality characteristics. This dissertation is dedicated to obtaining the optimal molding parameter settings of multi-quality characteristics (i.e., view angle and luminous uniformity) integrating computer-aided engineering (CAE) and design of experiment (DOE) with a variety of optimization algorithms; and then verifying their feasibility by way of measuring the practical view angles and the values of luminous uniformity in a plastic injection molding machine.
This research herein proposes a molding parameter optimization system based on multi-LED lighting lens design, which illustrates a complete and developed illumination system using DOE for screening the parameters, CAE for mold flow analysis, ANOVA for determining the significant factors, and RSM (response surface methodology) for optimizing the parameter settings in terms of multi-objective quality characteristics. In addition, the proposed system adopts the back-propagation neural network (BPNN) to create a quality predictor of LED-lens molding parameters. Moreover, two kinds of optimization algorithms: the GA (genetic algorithms) combined with PSO (particle swarm optimization) and the GA combined with DFP (Davidon-Flecher-Powell) employed to generate the optimal molding parameter settings and identify the practical (on-line) molding parameter settings. Finally, the realistic multi-LED lighting lens mold of injection molding can be developed, and the mold flow analysis and injection molding process can be implemented through CAE and practical molding parameter setting delved from the underlying optimization algorithms. The results of measuring the view angles and the values of luminous uniformity can be further used in verifying the validity of the study.
In this research, the results show the ratio of search iterations between RSM combined with GA-DFP and RSM combined with GA is 1100 to 40 (1100:40); therefore, the search efficiency of RSM combined with GA is 30 times lower than RSM combined with GA-DFP. On the other hand, the promotion of quality characteristics for RSM combined with GA-DFP is 5 percents greater than RSM combined with GA. As mentioned achievements of the study, the proposed research is beneficial to enhance the technologies of design and production, and dramatically reduce the development cycle of multi-LED lighting lens
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