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http://dx.doi.org/10.6109/jkiice.2022.26.5.641

Predicting defects of EBM-based additive manufacturing through XGBoost  

Jeong, Jahoon (Department of Mechanical and Systems Engineering, Korea Military Academy)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.5, 2022 , pp. 641-648 More about this Journal
Abstract
This paper is a study to find out the factors affecting the defects that occur during the use of Electron Beam Melting (EBM), one of the 3D printer output methods, through data analysis. By referring to factors identified as major causes of defects in previous studies, log files occurring between processes were analyzed and related variables were extracted. In addition, focusing on the fact that the data is time series data, the concept of a window was introduced to compose variables including data from all three layers. The dependent variable is a binary classification problem with the presence or absence of defects, and due to the problem that the proportion of defect layers is low (about 4%), balanced training data were created through the SMOTE technique. For the analysis, I use XGBoost using Gridsearch CV, and evaluate the classification performance based on the confusion matrix. I conclude results of the stuy by analyzing the importance of variables through SHAP values.
Keywords
EBM; Imbalanced data; SMOTE; XGBoost; SHAP;
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