台灣的高分子薄膜加工，於早期60年代開始使用PVC、PE、PET等基材薄膜，主要應用於包裝、文具、膠帶等日常用品。在光學膜的應用領域上，則開始於70年代汽車隔熱紙，之後在90年代LCD產業需求上出現前所未有的大量應用，舉凡導光膜、擴散膜、增亮膜、偏光板、廣視角膜等等。而高分子薄膜加工最主要仰賴卷對卷設備，其優勢為連續性大面積塗佈生產，可以提高生產量能並大幅降低成本。在2010年以來，伴隨著智慧科技如軟性電子的發展，對塗佈加工產品的規格要求日益嚴苛，甚至要求極度薄化，本研究個案公司主要以卷對卷生產光學膜產品為主，採用可進行超薄塗層的微凹塗佈技術進行生產，然而在過往的生產經驗中，並無法有效且確實的控制塗層厚度，將造成開線生產不順，產品品質異常等狀況。為了改善此問題，本研究將利用田口內外直交表進行微凹塗佈之穩健參數設計，內直交表放置控制因子，外直交表放置干擾因子，藉以找出最接近目標值，卻受環境影響最小之因子組合。在找到關鍵因子後，考量田口方法之因子水準並非最為接近目標值的結果，因此我們可透過使用反應曲面法來建立一個最佳化實驗，首先以關鍵因子建立一階反應模型，檢驗模型曲率效果，當效果不明顯時需增加最陡上升(下降)實驗，來逐步找出最佳反應區域;直到曲率效果顯著後，再以擴充實驗建立二階反應模型，進而透過該模型預測出反應值最佳化的水準。最後，為證明本研究的有效性與實用性，將比較個案公司以經驗法則所設立的原條件塗佈結果，並以製程綜合能力指標來同時衡量原條件與最佳化條件之間的差異，其結果可作為後續製程調機參考。

The high-poly film processing in Taiwan began in the early 1960s with PVC, PE, PET and other substrate films, mainly used in packaging, stationery, tapes and other daily necessities. In the field of optical film applications, it started in the 1970s with thermal insulation paper for automobiles. Then, in the 1990s, the LCD industry saw an unprecedented number of applications, such as light guide films, diffusion films, brightening films, polarizing films, wide view films, etc. Since 2010, with the development of smart technologies such as flexible electronics, the specifications for coating and processing products have become increasingly stringent, and even require extreme thinning. In this research, the company mainly produces optical film on a roll-to-roll processing and uses micro-gravure coating technology for ultra-thin coating. However, in the past, the thickness of the coating layer could not be controlled effectively and reliably, which resulted in poor production start-up and abnormal product quality. In order to improve this problem, this research will design experiments with orthogonal arrays to carry out robust parameter design of micro-gravure coating. The controllable factors are placed in the inner array, and the noise factors are placed in the outer array, to find the significance factors combination that is very closest to the target response (thickness) but have less sensitive to environment interference. After finding the key factors, we considered that the factor level of Taguchi's method was not the result closest to the target value, so we established an optimization experiment by using the response surface method. First, a first order model is established with key factors which find from Taguchi's method, then test the curvature of model. If test is not significant, the steepest ascent (descent) experiment should be built to gradually find the optimal response region. After the curvature is significant, the second order response model is developed by extension experiments, and the model is used to predict the optimal level of response that is closest to the target value. Finally, in order to understand the validity and applicability of this study, we compare the thickness with process capability index (Cpk) using the optimized parameters and the original parameters. The result and analysis can be used as a reference for finding good process conditions.

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