Quality Prediction and Yield Improvement in Process Manufacturing Based on Data Analytics

• Published in: Processes 2020-09, Vol.8 (9), p.1068
• Main Authors: Jun, Ji-hye, Chang, Tai-Woo, Jun, Sungbum
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Classification; Continuous flow; Data analysis; Data mining; Deep learning; Defects; Internet of Things; Labeling; Machine learning; Manufacturing; Neural networks; Powder metallurgy; Product quality; Quality management; Repair & maintenance; Semi-supervised learning; Sensors; Time series
• ISSN: 2227-9717; 2227-9717
• DOI: 10.3390/pr8091068

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.