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http://dx.doi.org/10.5762/KAIS.2021.22.6.36

A Development of Defeat Prediction Model Using Machine Learning in Polyurethane Foaming Process for Automotive Seat  

Choi, Nak-Hun (Dep. of Future Convergence Engineering, Graduate School, Kongju National University)
Oh, Jong-Seok (Div. of Mechanical & Automotive Engineering, Kongju National University)
Ahn, Jong-Rok (Eastern FTC)
Kim, Key-Sun (Eastern FTC)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.22, no.6, 2021 , pp. 36-42 More about this Journal
Abstract
With recent developments in the Fourth Industrial Revolution, the manufacturing industry has changed rapidly. Through key aspects of Fourth Industrial Revolution super-connections and super-intelligence, machine learning will be able to make fault predictions during the foam-making process. Polyol and isocyanate are components in polyurethane foam. There has been a lot of research that could affect the characteristics of the products, depending on the specific mixture ratio and temperature. Based on these characteristics, this study collects data from each factor during the foam-making process and applies them to machine learning in order to predict faults. The algorithms used in machine learning are the decision tree, kNN, and an ensemble algorithm, and these algorithms learn from 5,147 cases. Based on 1,000 pieces of data for validation, the learning results show up to 98.5% accuracy using the ensemble algorithm. Therefore, the results confirm the faults of currently produced parts by collecting real-time data from each factor during the foam-making process. Furthermore, control of each of the factors may improve the fault rate.
Keywords
Smart Factory; Foaming Process; Machine Learning; Defect Prediction; 4th Industrial Revolution;
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Times Cited By KSCI : 1  (Citation Analysis)
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