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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LCD驱动器IC封装行业为例

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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,由于液晶显示器驱动器IC封装工厂由于专业的团队合作,提高了产品的合格率,加快了对产品缺陷的认识,并找出了解决方案,因此不得不为产品的质量问题建立分析模式。更有效,更快速地获取所需信息,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来提高生产率。本研究采用数据仓库的星型模式作为生产线分析的基础,并使用数据挖掘中的决策树为包装工厂生产过程中发现的缺陷建立质量分析系统,以便为问题分析提供一个接口。 ,可以快速判断和控制问题原因,从而缩短解决质量问题的时间。研究结果表明,改进了决策树算法,减少了缺陷内部引线和芯片的数量,并且在液晶驱动器IC封装行业质量问题的分类和分析中,使用决策树算法比使用神经网络更合适。 。

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