1. 인공지능 기법을 이용한 MCT 가공 제품 품질에 영향을 미치는 특성에 대한 연구.
- Author
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유익수, 송준혁, and 정희운
- Subjects
MACHINE learning ,ARTIFICIAL intelligence ,SUPERVISED learning ,DEEP learning ,ALGORITHMS ,STACKING machines - Abstract
In this paper, a study was conducted to predict the defects in MCT processed finished products using a total of eight types of supervised learning models, one of the representative deep learning algorithms for artificial intelligence technologies. As a result of comparing the performance indicators and required execution time among machine learning models, it was confirmed that XGBM, GBM, Light GBM and Stacking Ensemble showed the highest accuracy, precision, recall, F1 score and AUC. Above all, it was confirmed that the Stacking Ensemble model was the most suitable algorithm for fast defect prediction because it showed the shortest learning execution time of 0.53 seconds. Besides, as a result of the analyzation of the relative importance of characteristic variables using the XG Boost model, it was found that the W-axis absolute coordinate and the X-axis absolute coordinate have a crucial effect on the defect. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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