Application of Graphical Gaussian Models to Dependency Analysis with Automated Manufacturing Data

• Main Authors: Yu-Hao Kuo, 郭宇豪
• Other Authors: Chi-Cheng Jou
• Format: Others
• Language: zh-TW
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/sz5ej8

碩士 === 國立交通大學 === 電機與控制工程系所 === 92 === There are hundreds of steps in the process of automated manufacture operation. Every step contains lots of measurements. As a result a tremendous amount of data is available. These data have a great deal of variables, which are highly correlated. According redundancy exits. In order to provide analysts the influence of predictors upon dependents, and to explain the correlations of variables, we use Graphical Gaussian Models(GGMs) to establish models based on the characteristic of the gathered data. Take manufacture of silicon wafers for example, data will be preprocessed first. Then we will discuss the measured limits and the factors of the general GGMs. Through the combination between factor analysis (FA)and multidimensional scaling (MDS), clusters of variables will be proceeded. According to the clusters, he procedure of modeling will be simplified and an improved method will be introduced to analyze more variables while maintaining the requested deviance. This method also can be applied to the massive data gathered by the similar procedure like automated manufacturing operation. Combining Expert system or Bayesian Network, we can prognosis and diagnosis results after a model is built.