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Using Data Mining Technology to improve Manufacturing Quality - A Case Study of LCD Driver IC Packaging Industry

机译:利用数据挖掘技术提高制造质量 - 以液晶驱动器IC包装行业为例

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In recent year, because of the professional teamwork, to improve the qualification percentage of products, to accelerate the acknowledgement of product defects and to find out the solution, the LCD driver IC packaging factories have to establish an analysis mode for quality problems of product for more effective and quicker acquisition of needed information and to improve the customer’s satisfaction for information system. The past information system used neural network to improve the yield rate of production. In this research employs the star schema of data warehousing as the base of line analysis, and uses decision tree in data mining to establish a quality analysis system for the defects found in the production processes of package factories in order to provide an interface for problem analysis, enabling quick judgment and control over the cause of problem to shorten the time solving the quality problem. The result of research shows that the use of decision tree algorithm reducing the numbers of defected inner leads and chips has been improved, and using decision tree algorithm is more suitable than using neural network in quality problem classification and analysis of the LCD driver IC packaging industry.
机译:近年来,由于专业团队合作,提高产品的资格百分比,加快产品缺陷的确认和找出解决方案,LCD驱动器IC包装工厂必须为产品质量问题建立分析模式更有效和更快地获取所需信息,并提高客户对信息系统的满意度。过去的信息系统使用神经网络提高产量的生产率。在本研究中采用数据仓库的星形模式作为线路分析的基础,并在数据挖掘中使用决策树来建立一个质量分析系统,以便为包装工厂的生产过程中发现的缺陷为基础,以便提供问题分析的界面,实现快速判断和控制问题的原因,以缩短解决质量问题的时间。研究结果表明,使用决策树算法减少了缺陷的内线和芯片的数量,并且使用决策树算法比使用质量问题分类和液晶驱动器IC包装行业的分析更适合使用神经网络。

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