Application of Multivariate Statistical Process Control in a Golf Head Process

• Main Authors: Jian-Gang Chew, 周建綱
• Other Authors: Chung-Yu Pan
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/20159209852978731259

碩士 === 東海大學 === 工業工程與經營資訊學系 === 96 === The manufacturing process today is very complicated for the quality of products is usually decided by two or more related characteristics. Therefore it is necessary and important to develop a multi-variables control charts on monitoring several characteristics of product. Principal Component Analysis (PCA) is one of the multivariate projection methods, using principles of statistics and linear algebra to project variable spaces of samples in possible to Latent spaces which have low dimensions and linear independent. We set variables and transfer to eigenvector space, which corresponding eigenvalues mean statistic weighting, these space configured by so-call principle components. Through building and verifying models in raw data, it could determine fault process occurrence. PCA is applied for a golf head process to investigate the impact of process parameters. Then, Hotelling’s T2 control charts are utilized to detect which golf head is defective and MYT method is implemented to reveal which parameter is the key factor for the defect.