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http://dx.doi.org/10.15207/JKCS.2022.13.02.249

Prediction Model of CNC Processing Defects Using Machine Learning  

Han, Yong Hee (Department of Entrepreneurship and Small Business, Soongsil University)
Publication Information
Journal of the Korea Convergence Society / v.13, no.2, 2022 , pp. 249-255 More about this Journal
Abstract
This study proposed an analysis framework for real-time prediction of CNC processing defects using machine learning-based models that are recently attracting attention as processing defect prediction methods, and applied it to CNC machines. Analysis shows that the XGBoost, CatBoost, and LightGBM models have the same best accuracy, precision, recall, F1 score, and AUC, of which the LightGBM model took the shortest execution time. This short run time has practical advantages such as reducing actual system deployment costs, reducing the probability of CNC machine damage due to rapid prediction of defects, and increasing overall CNC machine utilization, confirming that the LightGBM model is the most effective machine learning model for CNC machines with only basic sensors installed. In addition, it was confirmed that classification performance was maximized when an ensemble model consisting of LightGBM, ExtraTrees, k-Nearest Neighbors, and logistic regression models was applied in situations where there are no restrictions on execution time and computing power.
Keywords
Machine Learning; Artificial Intelligence; CNC; Defect; Milling;
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Times Cited By KSCI : 1  (Citation Analysis)
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