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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/105338

    Title: 應用類神經網路於隧道式烤爐製程最佳化
    Other Titles: Applying neural network to process optimization of a tunnel oven
    Authors: 張晉瑋;Chang, Chin-Wei
    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
    Keywords: 倒傳遞類神經網路;反應曲面法;最佳化;烤漆製程;back-propagation neural network;Response surface method;Optimization;Coating process
    Date: 2015
    Issue Date: 2016-01-22 14:53:33 (UTC+8)
    Abstract:   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。
      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.
    Appears in Collections:[管理科學學系暨研究所] 學位論文

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