LAPSE:2021.0059
Published Article
LAPSE:2021.0059
Quality Prediction and Yield Improvement in Process Manufacturing Based on Data Analytics
Ji-hye Jun, Tai-Woo Chang, Sungbum Jun
February 22, 2021
Quality management is important for maximizing yield in continuous-flow manufacturing. However, it is more difficult to manage quality in continuous-flow manufacturing than in discrete manufacturing because partial defects can significantly affect the quality of an entire lot of final product. In this paper, a comprehensive framework that consists of three steps is proposed to predict defects and improve yield by using semi-supervised learning, time-series analysis, and classification model. In Step 1, semi-supervised learning using both labeled and unlabeled data is applied to generate quality values. In addition, feature values are predicted in time-series analysis in Step 2. Finally, in Step 3, we predict quality values based on the data obtained in Step 1 and Step 2 and calculate yield values with the use of the predicted value. Compared to a conventional production plan, the suggested plan increases yield by up to 8.7%. The production plan proposed in this study is expected to contribute to not only the continuous manufacturing process but the discrete manufacturing process. In addition, it can be used in early diagnosis of equipment failure.
Keywords
classification, process manufacturing, semi-supervised learning, time-series analysis, yield improvement
Suggested Citation
Jun JH, Chang TW, Jun S. Quality Prediction and Yield Improvement in Process Manufacturing Based on Data Analytics. (2021). LAPSE:2021.0059
Author Affiliations
Jun JH: Department of Industrial and Management Engineering/Intelligence and Manufacturing Research Center, Kyonggi University, Suwon, Gyeonggi 16227, Korea
Chang TW: Department of Industrial and Management Engineering/Intelligence and Manufacturing Research Center, Kyonggi University, Suwon, Gyeonggi 16227, Korea [ORCID]
Jun S: Department of Industrial and Systems Engineering, Dongguk University, Seoul 04620, Korea
Journal Name
Processes
Volume
8
Issue
9
Article Number
E1068
Year
2020
Publication Date
2020-09-01
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr8091068, Publication Type: Journal Article
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LAPSE:2021.0059
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doi:10.3390/pr8091068
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Feb 22, 2021
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CC BY 4.0
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Feb 22, 2021
 
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Feb 22, 2021
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Original Submitter
Calvin Tsay
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