Treffer: 應用類神經網路於隧道式烤爐製程最佳化 ; Applying neural network to process optimization of a tunnel oven

Title:
應用類神經網路於隧道式烤爐製程最佳化 ; Applying neural network to process optimization of a tunnel oven
Contributors:
淡江大學管理科學學系碩士班, 時序時, 鄭啟斌, Shih, Hsu-Shih, Chen, Chi-Bin
Publication Year:
2015
Collection:
Tamkang University Institutional Repository (TKUIR) / 淡江大學機構典藏
Document Type:
other/unknown material
File Description:
144 bytes; text/html
Language:
Chinese
Relation:
參考文獻 中文文獻 1. 何明錦、黃然 (2003) ,鋼筋與混凝土表面處理效能之實驗研究,內政部建築研究所研究報告。 2. 呂美璉 (2008),反應曲面法尋找烘烤參數優化研究以改善基板翹曲-以封裝廠個案公司為例,元智大學工業工程與管理學系,碩士論文,桃園。 3. 李霞(民96), 彩色塗層港版技術及其發展趨勢,鋼鐵研究,35(4),59-62。 4. 林宜萱 (2007),應用雙反應曲面法於最佳參數設計之研究,國立臺灣科技大學工業管理系,碩士論文,台北。 5. 林惠玲、陳正倉 (2007),應用統計學,雙葉書廊有限公司,台北市。 6. 張家勤 (2009),結合反應曲面法、類神經網路與基因演算法於觸控面板雷射切割製程參數最佳化,國立清華大學工業工程與工程管理學系,碩士論文,新竹。 7. 陳永章、古東源、王嘉興 (2008),結合田口方法與TOPSIS於多重品質特性參數最佳化之研究。 8. 賀力行、林淑萍、蔡明春 (2006),統計學,前程企業管理有限公司,台北市。 9. 楊萬里、吳小蓉 (民88年) 。環氧樹脂塗層鋼筋的技術原理、發展過程及國內外應用情況,水運工程,8,1-7。 10. 葉怡成 (2000),類神經網路模式-應用與實作,第七版,儒林圖書公司。11.熊高生 (2007),MINITAB 14 資料統計與分析,文魁資訊股份有限公司,台北市。 11. 蔡家榮 (2011),應用反應曲面法於面成型快速原型系統製程參數最佳 化之研究,國立臺灣科技大學材料科學與工業工程學系,碩士論文,台北。 12. 羅華強(2001),類神經網路-MATLAB 的應用,第六版,高立圖書。 英文文獻 1. Alexander S. (1998). Theory of Linear and Integer Programming. New York, NY: John Wiley and Sons. 2. Baxt, G., and Shofer, F. (1999). Use an Artificial Neural Network for Data Analysis in Clinical Decision Making the Diagnosis for Acute Coronary Occlusion. Neural Computation, 2, 480-489. 3. Cavalieri, S., Maccarrone, P., and Pinto, R. (2004). Parametric vs. neural network model for the estimation of production costs: A case study in the automotive industry. International Journal of Production Economics, 91, 165-177. 4. Chen, C. B. (2002), Neuro-fuzzy and genetic algorithm in multiple response optimization. Computers & Mathematics with Applications, 44(12), 1503-1514 5. Chien, T., Lin, B., and Leo, W. (1999). A neural network-based approach for strategic planning. Information and Management. 35(6), 357-364. 6. Creese, R., and Li, L. (1995) Cost estimation of timber bridges using neural network. Cost Engineering, 37(5), 17-22. 7. Epperson, J. F. (2013). An Introduction to Numerical Methods and Analysis. Baker and Taylor Books. 8. Fan, J. (2006). Application of back propagation artificial neural network to real time analysis and prediction of the total suspended solids of the water in Shimen Reservoir. Journal of Chinese Soil and Water Conservation, 37(4), 367-376. 9. Haykin, S. (1999). Neural Networks : A Comprehensive Foundation 2nd Edition, Prentice Hall. 10. Hegazy, T., and Ayed, A. (1998). Neural network model for parametric cost estimation of highway projects. Journal of Construction Engineering and Management, 124, 210-218. 11. Hu, M., Zhang, G., Jiang, C. and Patuwo, B. (1999). A cross-validation analysis of neural network out-of-sample performance in exchange rate forecasting. Decision Sciences, 30(1), 197-216. 12. Kim, S., Park, T., and Yoo, J. (2001). Speed-sensor less vector control of an iduction motor using network speed estimation. Industrial Electronics, IEEE Transactions on, 48(3), 609-614. 13. Lauridsen, S., Vitali, R., Keulen, F. V. and Haftka, R. T. (2001) Response Surface Approximation using Gradient Information, The Proceedings of the Fourth World Structure and Multidisciplinary Optimization, 295-297. 14. Lewis, C. (1982). A practical guide to exponential smoothing and curve fitting .Industrial and Business Forecasting Methods. 15. Lippmann, R. (1987). An introduction to computing with neural nets. IEEE ASSP Magazine, 4-22. 16. Liu, W. and Batill, S. M. (2001) Implementation Issue in Gradient– Enhanced Neural Work Response Surface Approximations. The Proceedings of the Fourth World Structure and Multidisciplinary Optimization, 299-301. 17. Montgomery, D. (2001). Design and Analysis of Experiments 5th Edition,Wiley. 18. Munger, C. G. (1999). Corrosion prevention by protective coatings 2nd Edition. NACE International. 19. Neaupane, K., and Achet, S. (2004) Use of Back propagation Neural network for landside monitoring: A case study in the higher himalaya. Engineering Geology. 74,213-226. 20. Ricketson, R. C. and Wang, K. K. (1986) An Experimental Study of Injectable-Molded Part Thickness Using Response-Surface Methodology. Antec’86. Boston, Massachusetts. 21. Rikards, R. and Auzins, J. (2001) Approximation Techniques for Response Surface Method in Structural Optimization, The Proceedings of the Fourth World Structure and Multidisciplinary Optimization, 23,303-305. 22. Shu-Kai S. Fan. (2000) Quality improvement of chemical- mechanical wafer planarization process in semiconductor manufacturing using a combined generalized linear modelingnon-linear programming approach. International Journal of Production Research, 38(13), 3011- 3029. 23. Tsou, K. (2006). A forecast of building destruction in earthquakes: Applications of Artificial Neural Network. Journal of Housing Studies, 15(1), 21-41. 24. Wang, H. (2007) Application of BPN with feature-based models on cost estimation of plastic injection products. Computers & Industrial Engineering, 53, 79-94.; https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/105338; https://tkuir.lib.tku.edu.tw/dspace/bitstream/987654321/105338/1/index.html
Accession Number:
edsbas.38A14DFB
Database:
BASE

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碩士 ; 在自然界中所有結構材料皆會隨著外在環境的影響而產生衰變、劣化等材料性能退化的問題。材料性能退化的問題會影響其構成物的外觀還有強度,例如建築工程上的鋼骨結構,生鏽的時候會造成表面脫落影響美觀,耐久度也會變差。為了避免材料性能退化的現象,我們常對設備與結構物施予保護措施,例如:塗層、防蝕工程、遮雨設計及緩蝕劑等,其中又以烤漆的塗層技術較為成熟且被普遍使用。 本研究探討隧道式烤漆系統製程最佳化問題,在不同的烤爐溫度、空氣濕度及溶劑比例之下進行試驗,收集產品塗層厚度之資料並且透過倒傳遞類神經網路建立隧道式烤漆系統反應曲面法模型,以了解以上加工條件對塗層厚度的影響。 然後以數學規劃建置烤漆製程反應曲面法的最佳化模型,並透過數值方法求解最佳之加工條件設定。研究結果顯示,倒傳遞類神經網路所建立的烤漆製程模型具高準確性的預測能力;而反應曲面法最佳化方法則可在不同的空氣濕度條件下,找到適切的烤爐溫度與溶劑比例設定值,以獲得較佳的塗層厚度結果。 ; The mechanics of materials can be weaken by the outside environment and then problems of material decay and deterioration occur. Material degradation not only affects its appearance but also its strength. For example, in construction engineering, rust on the steel structure of a building peels the surface layer and reduces the endurance of the steel as well. To avoid fast material degradation, protection such as coating, anti-erosion procedures, eaves designing and inhibitors, are often allied to equipment or construction structures. Among which, coating is particularly popular for being a sophisticated technology, where the coating process is usually done by a paint baking oven. This research aims to find the optimum settings of a tunnel oven to produce desired coating quality. The factors considered in this study include oven temperature, ratio of solution to paint, and the environmental humidity. Experiments are carried out to obtain the resulting thickness of coating under different settings of the aforementioned three factors. The response surface of the coating process by the tunnel oven is modeled by training a back-propagation neural network with the collected data. The optimization of the response surface of the coating process is formulated as a linear programming problem and solved by a numerical method. The result shows that the back-propagation neural network well models the surface response of the coating process, and optimization procedure is able to find reasonable settings of the factors to obtain desired coating quality. ; 目錄 第1章 緒論 1 1.1 研究背景與動機 1 1.2 研究目的 5 第2章 文獻回顧 6 2.1 ...