레이저 분말 베드 용융법으로 제조된 AlSi10Mg 합금의 경도 예측을 위한 설명 가능한 인공지능 활용

서지정보

ㆍ발행기관 : 한국분말야금학회 ㆍ수록지정보 : 한국분말야금학회지 / 30권 / 3호
ㆍ저자명 : 전준협, 서남혁, 김민수, 손승배, 정재길, 이석재

목차

1. 서 론
2. 해석 모델 구축
3. 결과 및 고찰
4. 결 론
Acknowledgements

영어 초록

In this study, machine learning models are proposed to predict the Vickers hardness of AlSi10Mg alloys fabricated by laser powder bed fusion (LPBF). A total of 113 utilizable datasets were collected from the literature. The hyperparameters of the machine-learning models were adjusted to select an accurate predictive model. The random forest regression (RFR) model showed the best performance compared to support vector regression, artificial neural networks, and k-nearest neighbors. The variable importance and prediction mechanisms of the RFR were discussed by Shapley additive explanation (SHAP). Aging time had the greatest influence on the Vickers hardness, followed by solution time, solution temperature, layer thickness, scan speed, power, aging temperature, average particle size, and hatching distance. Detailed prediction mechanisms for RFR are analyzed using SHAP dependence plots.

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