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題名:LED照明模組射出成形最佳化系統之研究
作者:戴鎰家
作者(外文):Tai, Yi-Chia
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:2013
主題關鍵詞:LED照明模組田口方法倒傳遞神經網路基因演算法粒子群演算法混合演算法射出成形LED lighting modulusTaguchi orthogonal arrayBPNNGAPSOHybrid GA-PSOInjection molding
原始連結:連回原系統網址new window
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LED照明模組之製造大部分經由射出成形製程產出,目前產業界對於射出成形製程參數設定,大部分憑藉著工程師或資深技師的經驗與直覺,在經過多次的試誤法(trial-and-error)或實驗設計後來決定其製程參數組合,往往會耗費大量的時間與成本,而影響射出成形產品品質的參數很多,各參數之間具有高度非線性(nonlinear)關係,因此所找出之射出成形製程參數組合並非最佳。
故本研究針對LED照明模組之射出成形製程提出了最佳化系統,對多品質特性之射出成形製程進行參數最佳化之研究,找出一組符合品質特性為發光角度與發光強度的製程參數組合。製程參數為模具溫度(Mold temperature)、融膠溫度(Melt temperature)、射出速度(Injection velocity)、保壓壓力(Packing pressure) 及保壓切換(Holdingswitch)。本製程參數最佳化系統分為兩個階段,第一階段先進行S/N 比最佳化,將依田口方法進行照明模組之射出成形實驗,並以實驗數據計算S/N 比值,利用倒傳遞類神經網路建構S/N 比預測器與品質預測器,使用 S/N 比預測器結合基因演算法(GA)進行全域搜尋,找出最佳製程參數組合,使各品質特性之S/N 比值最大化,結果將使製程變異降至最低。第二階段進行製程最佳化,將製程品質逼近目標規格,首先運用ANOVA找出產品品質與製程參數之關係,並找出最佳化數值分析之控制因子,利用品質預測器與S/N 比預測器結合混合演算法(Hybrid GA-PSO)進行數值分析,以S/N 比預測器結合基因演算法之最佳製程參數組合為初始值,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。研究結果顯示最佳化系統找出之製程參數組合,不僅提升整個LED照明模組製程之穩定性且發光角度與發光強度符合品質規格,有效提升產品品質及降低成本。
In the injection molding process of a LED lighting modulus, trial-and-error processes and the design of experiments are frequently employed to determine initial process parameter settings, which depend on the engineers’ experience and intuition. However, since many different parameters could influence a finished product and a high level nonlinear relationship stands between each parameter, it takes a large number of manpower, devices, and other expenses to figure out a better combination of process parameters.
Thus, this study presents a novel optimization system for injection molding with multiple performance characteristics through data mining and analysis to effectively determine the optimal process parameter settings. The quality characteristics of the LED lighting modulus can be categorized into the beam angle and the luminous intensity. The control factors for the process are mold temperature, melt temperature, injection velocity, packing pressure and VP switch. The proposed parameter optimization system is divided into two stages. In the first stage, the Taguchi method is employed to conduct signal-to-noise (S/N) ratio optimization. Taguchi orthogonal array experiments are performed, and then the experimental data are trained and tested by back-propagation neural networks to create a S/N ratio predictor and a quality predictor. In addition, the S/N ratio predictor is combined with genetic algorithms (GA) to obtain the process parameter combination on maximum S/N ratio for both beam angle and luminous intensity. As a result, the quality variance could be reduced to minimum. In the second stage, optimization of quality characteristics is carried out. Analysis of variance (ANOVA) is employed to determine the control factors of numerical analysis; the afore-mentioned quality predictor and S/N ratio predictor along with hybrid GA-PSO is to implement the global search and to draw close to the target of specification, and available to generate the most stable and low-defective ratio product. The proposed novel optimization system can create the best process parameter settings which can not only be more robust and meet the dimension specification of a LED lighting modulus, but also enhance the stability of injection process and its product quality characteristics.
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