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題名：運用混合演算法於塑膠射出成形製程參數最佳化系統之研究
作者：劉朋熹
作者(外文)：Liou, Pen-Hsi
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：塑膠射出成形；最佳化；混合式演算法；田口方法；倒傳遞神經網路；基因演算法；粒子群演算法；模擬退火演算法；plastic injection molding；optimization；hybrid algorithms；Taguchi methods；back-propagation neural network；genetic algorithm；particle swarm optimization；simulated annealing
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塑膠射出成形(PIM)產業，提供各種產品以滿足不同消費者的需求。此外，目前對於射出成形參數的設定，大都是依據工程師的經驗和實驗設計。然而對產品產生影響的製程參數眾多，且各個參數之間彼此具有高度非線性的關係，故需要花費大量的時間、人力和成本，藉以找出製程參數的適當組合。
因此本研究提出塑膠射出成形製程參數最佳化系統，利用二種最佳化模式進行研究，首先使用田口方法(Taguchi method)進行實驗與資料分析，並經由信號雜訊比(S/N ratio)及變異數分析(ANOVA)，找出對製程影響最為顯著之製程參數，並求得一組製程參數組合。模型一為以迴歸模型為基礎之混合式演算法最佳化，將田口實驗所得之數據，建立品質之迴歸模式(品質預測器)與信號雜訊比之迴歸模式(S/N比預測器)，第一階段多目標S/N 比最大化即品質變異最小，利用S/N 比預測器以基因演算法或模擬退火法或粒子群演算法進行全域搜尋，找出製程參數組合，使各品質特性之S/N值最大，此階段將製程變異降至最低使製程穩定；第二階段多目標品質最佳化，利用S/N比預測器與品質預測器結合混合演算法(SA-PSO、GA-PSO、PSO-GA)進行全域搜尋，以第一階段之最佳製程參數組合為初始值，利用混合演算法，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。
模型二為以倒傳遞類神經網路為基礎之兩階段最佳化，將田口實驗所得之數據經由倒傳遞類神經網路(BPNN)進行訓練與測試，用以建立射出成形製程之S/N比預測器與品質預測器，第一階段多目標S/N 比最大化，以S/N 比預測器結合基因演算法進行全域搜尋，使各品質特性之S/N 比值達到最大值，此階段將使製程變異降至最低；第二階段多目標品質最佳化，結合S/N比預測器及品質預測器與混合演算法(hybrid GA-PSO)進行全域搜尋，以第一階段之最佳製程參數組合為初始值，利用混合演算法(hybrid GA-PSO)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。
實驗結果顯示模型二以倒傳遞類神經網路為基礎之兩階段最佳化系統，具有最佳效果，不僅提升整個射出成形製程之穩定性，而且產品之長度也符合規格與產品之翹曲度也降低，有效地提升塑膠射出成形產品的品質。
The plastic injection molding (PIM) industry offers a variety of products to satisfy diverse consumer needs. Nowadays, the parameter settings for the PIM industry are based on engineers’ experiences and design of experiments (DOE). However, it takes a large amount of time, manpower, and cost to figure out an appropriate combination of process parameters since parameter settings that could have a great impact on products are numerous and there are deep nonlinear relationships between the parameters.
This study proposes a system with optimal parameters for the PIM industry in order to solve those problems. Two models are taken along with the research work and each model is a two-stage multiple-objective optimization based on hybrid algorithms. The research starts with the Taguchi method employed for experiment and data analysis. Besides, the signal-to-noise (S/N) ratio and analysis of variance (ANOVA) are used to obtain a combination of parameter settings that is the most significant one in the process. The first model is an optimization based on regression model using hybrid algorithms. With the experimental data gained from the Taguchi’s experiment, a quality regression model (quality predictor) and an S/N ratio regression model (S/N ratio predictor) are created. The first stage is to optimize the multiple-objective S/N ratio. The S/N ratio predictor is associated with genetic algorithm (GA), simulated annealing (SA), and particle swarm optimization (PSO) for universal search to acquire the process parameters and to maximize the S/N ratio of each quality characteristics. This stage intends to minimize the variance in process. The second stage is to optimize the multiple-objective quality. The hybrid algorithms (SA-PSO, GA-PSO, PSO-GA) are associated with the S/N ratio predictor and the quality predictor for universal search. The process parameters gained from the first stage is set for the initial values. The hybrid algorithms are then used to find the optimal parameter settings that tally best with the quality standard and make it most stable in process.
The second model is an optimization based on back-propagation neural network (BPNN) using hybrid algorithms. The data gained from the Taguchi experiment are applied to train in BPNN so as to generate an S/N ratio predictor and quality predictor. The first stage is to optimize the S/N ratio. The S/N ratio predictor then works together with GA for universal search to acquire the process parameters and to maximize the S/N ratio of the quality responses. This stage intends to minimize the variance in process. As for the second stage, process optimization of the multiple-objective quality is investigated. Hybrid GA-PSO is associated with the S/N ratio predictor and the quality predictor for universal search. The process parameters gained from the first stage is set for the initial values. The Hybrid GA-PSO is then used to find the optimal parameter settings that tally best with the quality standard and make it most stable in process.
The confirmation results show that the second model can create the best performance. The best process parameter settings which not only enhance the stability in the whole injection molding process but also meet the length specification, reduce the parts’ warpage and effectively improve the PIM product quality.
方振瑞(2001)。應用參數設計於提昇印刷電路板縱橫比。未出版之碩士論文，私立逢甲大學工業工程研究所，台中市。
王興正(2012)。應用粒子群演算法於無人飛行載具結構系統之最佳化設計。未出版之碩士論文，私立淡江大學航空太空工程研究所，台北市。
白欣宜(2009)。整合Moldflow與GA於數位相框射出成形繞道最佳化設計。未出版之碩士論文，大同大學，台北。
李國榮(2006)。整合粒子群演算法與Moldflow於塑模冷卻水道最佳化。未出版之碩士論文，大同大學，台北。
李輝煌(2009)。田口方法：品質設計的原理與實務。台北：高立圖書有限公司。
辛宜芳(2003)。以CAD為平台之自動排版系統使用基因演算法。未出版之碩士論文，中華大學，新竹。
孫志強(1996)。塑膠世界：精密射出成形技術。台北：塑膠世界雜誌社。
范俊明(2012)。射出成形最佳化之模擬與驗證。未出版之碩士論文，國立虎尾科技大學，雲林。
張斐章、張麗秋、黃浩倫(2004)。類神經網路 理論與實務。台北：東華書局。
梁婉蓉(2004)。微射出成形參數對縫合線強度之影響。未出版之碩士論文，大葉大學，彰化。
莊信源(2000)。類神經模糊系統與遺傳演算法在加工參數最佳化之應用。未出版之碩士論文，台灣海洋大學。
許壽文(2003)。類神經網路應用於發光二極體檢測。未出版之碩士論文，中華大學，新竹。
陳啟峰(2001)。塑膠光學透鏡之射出成型製程探討。未出版之碩士論文，長庚大學，桃園。
傅于珊(2011)。短焦距非球面光學透鏡之射出成形。未出版之碩士論文，國立勤益科技大學機械工程系，台中市。
傅和彥、黃士滔(2001)。品質管理。台北：前程企業圖書有限公司。
黃臣鴻(2003)。PC/ABS 合膠機械性質之射出成型條件最佳化。未出版之博士論文，中央大學，桃園。
黃募甄(2012)。最佳化設計於結構被動控制之應用。未出版之碩士論文，中央大學，桃園。
楊景程(2001)。射出成型機最佳參數之預測。未出版之碩士論文，台灣科技大學，台北。
楊燕珠、林宛儒(2010)。混合式粒子群最佳化與基因演算法於動態分析。產業資訊管理學術暨新興科技實務研討會，台北。
葉怡成(2001)。實驗計畫法:製程與產品最佳化。台北：五南圖書出版股份有限公司。
蔡子琦(1998)。類神經網路與基因遺傳演算法於WEDM 加工參數最佳化之應用。未出版之碩士論文，臺灣大學，台北。
蘇木春、張孝德(1999)。機器學習：類神經網路、模糊系統以及基因演算法。全華書局。
Akbarzadeh, A., & Sadeghi, M. (2011). Parameter study in plastic injection molding process using statistical methods and IWO algorithm. Journal of Modeling and Optimization, 1(2), 141-145.
Alam, K., & Kamal, M. R. (2004). Runner Balancing by a Direct Genetic Optimization of Shrinkage. Polymer Engineering and Science, 44(10), 1949-1959
Altan, M. (2010). Reducing shrinkage in injection moldings via the Taguchi ANOVA and neural network method. Mater Des , 31. 599-640.
Chen, W. C., Chen, T. C., Tsai, C. H., & Ho, T. H. (2006). The Neural Network Implementation in Pattern Recognition of Semiconductor Etching Process. Journal of the Chinese Institute of Industrial Engineering, 23(4), 269-279.
Chen, W. C., Fu, G. L., Tai, P. H., & Deng, W. J. (2009). Process parameter optimization for MIMO plastic injection molding via soft computing. Expert Systems with Applications, 36(2), 1114-1122.
Chen, W. C., Kurniawan, D., & Fu, G. L. (2012). Optimization of process parameters using DOE, RSM and GA in plastic injection molding. Advanced Materials Research, 472-475, 1220-1223.
Chris, M. S., & George, F. (1994). Multiobjective Optimization of a Plastic Injection Molding Process. IEEE Transactions on Control Systems Technology, 2(3), 157-168.
Cook, D. F., Ragsdale, C. T., & Major, R. L. (2000). Combining a neural network with a genetic algorithm for process parameter optimization. Engineering Applications of Artificial Intelligence, 13(4).
Costa, N., & Ribeiro, B. (1999). Artificial Neural Network For Data Modeling of Plastic Injection Process. IEEE 1999.
Covas, J. A., Cunha, A. G., & Oliveira, P. (1999). An optimization Approach to Practical Problem in Plasticating Single Screw Extrusion. Polymer Engineering and Science, 39.
Deng, W. J., Chen, C. T., Sun, C. H., Chen, W. C., & Chen, C. P. (2008). An effective approach for process parameter optimization in injection molding of plastic housing components. Polym-Plast Technol ,47(9), 910-919.
Deng, W. J., Chen, W. C., & Wen, P. (2008). Back-propagation neural network based importance-performance analysis for determining critical service attributes. Expert Systems with Applications, 34(2), 1115-1125.
Du, S., Li, W., & Cao, K. (2006). A learning algorithm of artificial neural network based on GA - PSO. Journal of Intelligent Control and Automation, 1, 3633-3637.
Fu, G. L. (2010). The Process Parameter Optimization System for Plastic Injection Molding. Paper presented at the Innovative Computing, Information and Control, Taiwan, Chung Hua University.
Gao, F. (2003). Injection Molding Packing Profile - Toward Closed-Loop Quality Control. Paper presented at the Innovative Computing, Information and Control, Taiwan, Chung Yuan University.
Han, Y., Liu, X., Lv, G. (2013). The Intelligent Control Method of the Density of the Metal Injection Molding Billet Based on ANN. Journal of Materials Science Forum , 5, 161-167.
He, W., Zhang, Y. F., Lee, K. S., Fuh, J. Y. H., & Nee, A. Y. C. (1998). Automated Process Parameter Resetting for Injection Molding : a Fuzzy-neuro Approach. Journal of Intelligent Manufacturing, 9(1), 17-27.
Kane, V. E. (1986). Process capability indices. Journal of Quality Technology, 18(1), 41-52.
Kao, Y. T., & Zahare, E. (2008). A hybrid genetic algorithm and particle swarm optimization for multimodal functions . Journal of Applied Soft Computing, 8(2), 849-857.
Karmal, M. R., Varela, A.E., & Patterson, W. I. (1996). Control of Part Weight in Injection Molding of Amorphous Thermoplastics. Polymer Engineering and Science, 39(5), 940-952.
Khaw, F. C., Lim, B.S., & Lim, E. N. (1995). Optimal design of neural network using the Taguchi method. Neurocomputing, 7(3), 225-245.
Kim, B., & May, G. S. (1995). Real-Time Diagnosis of Semiconductor Manufacturing Equipment. Electronics Manufacturing Technology Symposium, 224-231
Kurt, M., Bagci, E., & Kaynak, A. (2009). Application of Taguchi methods in the optimization of cutting parameters for surface finish and hole diameter accuracy in dry drilling processes. International Journal of Advanced Manufacturing Technology, 40(5-6), 458–469.
Kurtaran, H., & Erzurumlu, T. (2006). Efficient warpage optimization of thin shell plastic parts using response surface methodology and genetic algorithm. International Journal of Advanced Manufacturing Technology, 27(5-6), 468–472.
Lau, H. C. W., Ning, A., Pun, K. F., & Chin, K. S. (2001). Neural networks for the dimensional control of molded parts based on a reverse process model. Journal of Materials Processing Technology, 117, 89-96.
Li, E., Jia, L., & Yu, J. (2002). A genetic neural fuzzy system-based quality prediction model for injection process. Computers and Chemical Engineering. 26 , 1253-1263.
Liang, J. M., & Wang, P. J. (2002). Self-learning control for injection molding based on neural networks optimization. Journal of Injection Molding Technology. 6, Iss. 1, 58-72.
Liou, S. R., & Yang, C. D. (2000). Application of Genetic Algorithms and Taguchi Methods to Flight Control Design. Transactions of the Aeronautical and Astronautical Society of the Republic of China, 32 (3), 223-236.
Liu, X. H., & Lin, J. (2011). Scheduling optimization in supply chain based on GA-PSO hybrid algorithm. Control and Decision, 26(4).
Lotti, C., Ueki, M. M., & Bretas, R. E. S. ( 2002). Prediction of the shrinkage of injection molded iPP plaques using artificial neural networks. Journal of Injection Molding Technology. 6(3), 157-176.
Mathur, R., Advani, S. G., & Fink, B. K. (1998). Optimization of Gate and Vent Locations for Resin Infusion Processes Using Genetic Algorithms. Proceedings of the American Control Conference Philadelphia, Pennsylvania, 2176-2180
Min, B. H., & Shin, B. C. (2001). A study on volumetric shrinkage of injection molded parts based on neural networks. Journal of Injection Molding Technology. 5(4), 201-207
Mostafa, J., Mohammad, M. A., & Ehsan, M. (2011). A hybrid response surface methodology and simulated annealing algorithm: A case study on the optimization of shrinkage and warpage of a fuel filter. World Applied Sciences Journal, 13(10), 2156-2163.
Ng, C. F., Kamaruddin, S., Siddiquee, A. N., & Khan, Z. A. (2011). Experimental investigation on the recycled HDPE and optimization of injection moulding process parameters via Taguchi method. International Journal of Mechanical and Materials Engineering, 6(1), 81-91.
Öktem, H. (2012). Optimum process conditions on shrinkage of an injected-molded part of DVD-ROM cover using Taguchi robust method. International Journal of Advanced Manufacturing Technology, 61, 518-528.
Ozcelik, B., & Erzurumlu, T. (2006). Comparison of the warpage optimization in the plastic injection molding using ANOVA, neural network model and genetic algorithm. Journal of Materials Processing Technology, 171(3), 437-445.
Petrova, T., & Kazmer, D. (1999). Incorporation of Phenomenological Models in a Hybrid Neural for Quality Control of Injection Molding. Polymer Plastic Technolgy Engineer, 38(1), 1-18.
Sadeghi, B. H. M. (2000). A BP-neural network predictor model for plastic injection molding process. Journal of Materials Processing Technology, 103, 411-416.
Sanschagrin, B. (1983). Process Control of Injection Molding. Polymer Engineering and Science, 23(8), 431-438.
Shen, C., Yu, X., Wang, L., & Tian, Z. (2004). Gate location optimization of plastic injection molding”, Journal of Chemical Industry and Engineering, 55(3), 445-449
Shi, F., Lou, Z. L., Lu, J. G., & Zhang, Y. Q. (2003). Optimization of Plastic Injection Molding Process with Soft Computing. International Journal of Advanced Manufacturing Technology, 21, 656-661
Shie, J. R. (2008). Optimization of injection molding process for contour distortions of polypropylene composite components by a radial basis neural network. International Journal of Advanced Manufacturing Technology, 36, 1091-1103.
Su, C. T., & Wong, J. T. (2007). Designing MIMO controller by neuro-traveling particle swarm optimizer approach. Expert Systems with Applications, 32, 848-855.
Tsai, K. M., Hsieh, C. Y., & Lo, W. C. (2008). A study of the effects of process parameters for injection molding on surface quality of optical lenses. Journal of Materials Processing Technology. 209 (7), 3469-3477.
Tzeng, C. J., & Chen, R. Y. (2013). Optimization of electric discharge machining process using the response surface methodology and genetic algorithm approach. Journal of Precision Engineering and Manufacturing, 14(5), 709-717.
Tzeng, C. J., Yang, K. Y., Lin, Y. H., & Tsai, C. H. (2012). A study of optimization of injection molding process parameters for SGF and PTFE reinforced PC composites using neural network and response surface methodology. The International Journal of Advanced Manufacturing Technology, 63, 691-704.
Wang, G. J., Tsai, J. C., Tseng, P. C., & Chen, T. C. (1998). Neural-Taguchi Method for Robust Design Analysis. Journal of the Chinese Society of Mechanical Engineers, 19(2), 223-230.
Wang, K. K. (1980). System Approach to Injection Molding Process. Polymer-Plastics Technology and Engineering, 14 (1), 75-93.
Wang, X., Zhao, G., & Wang, G. (2013). Research on the reduction of sink mark and warpage of the molded part in rapid heat cycle molding process. Materials & Design, 47, 779-792
Wu, C. H., & Chen, W. S. (2006). Injection molding and injection compression molding of three-beam grating of DVD pickup lens. Sensors & Actuators: A. Physical, 125(2), 367-375.
Xu, G., Yang, Z., & Long, G. (2012). Multi-objective optimization of MIMO plastic injection molding process conditions based on particle swarm optimization. International Journal of Advanced Manufacturing Technology, 58, 521-531.
Yang, Y. K., Shi, J. R., Yang, R. T., & Chang, H. A. (2006). Optimization of injection molding process for contour distortion of polypropylene composite components via design of experiments method. The Journal of Reinforced Plastics and composites, 25(15), 1585-1599.
Yin, F., Mao, H., Hua, L., Guo, W., & Shu, M. (2011). Back-propagation neural network modeling for warpage prediction and optimization of plastic products during injection molding. Materials & Design, 32(4), 1844-1850.
Zhai, M., & Xie, Y. (2010) A study of gate location optimization of plastic injection molding using sequential linear programming. The International Journal of Advanced Manufacturing Technology, 49(14), 97-103.
Zhang, L., & Wang, R. (2013). An intelligent system for low-pressure die-cast process parameters optimization. The International Journal of Advanced Manufacturing Technology, 65(14), 517-524.
Zhao, C., & Gao, F. (1999). Melt Temperature Profile Prediction for Thermoplastic Injection Molding. Polymer Engineering and Science, 39 (9), 1787-1801.
 
 
 
 
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