Application of Bayesian Networks to Diagnosis and Prognosis of Manufacturing Process

• Main Authors: Chin-Hsien Lin, 林志憲
• Other Authors: Chi-Cheng Jou
• Format: Others
• Language: zh-TW
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/r4zm22

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 manufacturing operation. Every step contains lots of measurements. As a result a tremendous amount of data is available. These data maybe contain reasons that case abnormal states of manufacturing process. We use Junction Tree Algorithm to establish Junction Tree Models, in order to provide engineers or analysts to diagnose abnormal states. Take manufacture of silicon wavers for example. After establishing the junction tree model, we use the model to find abnormal states. Then we will discuss factors such as number of cases and variables and the limit of calculation time and the quantization of yield variable. At last we will verify the junction tree model. This method also can be applied to many areas that need to handle a tremendous amount of data, for example in the industrial, medical or meteorological area etc.. We can use this method to diagnose and prognose results after the junction tree is built.