| Summary: | 碩士 === 中原大學 === 機械工程研究所 === 97 === To develop lamps with better uniformity of color difference and brightness, major domestic manufacturers for cold cathode fluorescent lamp（CCFL）, frequently conduct the factorial analysis method, a method commonly used to isolate the optimum mix for quality CCFL characters.
However, the quality inconsistency for each factor selected usually leads directly to redundant test runs and failed attempts researching the optimum mix. As the manufacturing systems today become more complicated and competition intensified in the marketplace, the stability requirements of the manufacturing systems are of the utmost importance to quality-conscious manufacturers. That is, if they are not able to immediately identify and fix incorrect parameters in the production processes, these manufacturers will incur huge losses as the defect products come out of the production line, and they have to shut down the entire production line just to look for causes.
This paper addresses the drawbacks of the factorial analysis method and proposes a composite analysis model to isolate the optimum mix for quality CCFL characters in the coating process. This model first adopts the artificial neural network approach. The artificial neural network approach is especially effective for identifying parameters, measuring their mixed effects for quality characters, and ensuring that the selected factors do not co-vary with one another. This composite analysis model also includes the orthogonal array and grey relational analysis used in Taguchi methods and intelligent genetic algorithm.
The advantages of this model include: a layered structure that can simplify the experimental designs to produce results more practical in the real situations; and greatly shortening test times for formulating the optimum parameter mix. With the simulated result from the coating process, this study also shows that the model can indeed produce the desired mix better and faster than that generated by the factorial analysis method.
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