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Analysis of Equipment Factor for Smart Manufacturing System  

Ahn, Jae Joon (Division of Data Science, Yonsei University)
Sim, Hyun Sik (Department of Industrial & Management Engineering, Kyonggi University)
Publication Information
Journal of the Semiconductor & Display Technology / v.21, no.4, 2022 , pp. 168-173 More about this Journal
Abstract
As the function of a product is advanced and the process is refined, the yield in the fine manufacturing process becomes an important variable that determines the cost and quality of the product. Since a fine manufacturing process generally produces a product through many steps, it is difficult to find which process or equipment has a defect, and thus it is practically difficult to ensure a high yield. This paper presents the system architecture of how to build a smart manufacturing system to analyze the big data of the manufacturing plant, and the equipment factor analysis methodology to increase the yield of products in the smart manufacturing system. In order to improve the yield of the product, it is necessary to analyze the defect factor that causes the low yield among the numerous factors of the equipment, and find and manage the equipment factor that affects the defect factor. This study analyzed the key factors of abnormal equipment that affect the yield of products in the manufacturing process using the data mining technique. Eventually, a methodology for finding key factors of abnormal equipment that directly affect the yield of products in smart manufacturing systems is presented. The methodology presented in this study was applied to the actual manufacturing plant to confirm the effect of key factors of important facilities on yield.
Keywords
Smart manufacturing system; Equipment factor analysis; Stepwise regression; Printed circuit board;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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