머신러닝을 이용한 레이저 용접부의 모델링 Part Ⅱ: 고강도강 겹치기 레이저용접부의 형상 및 기계적 거동

초록

In accordance with the requirements of lightweight automobiles, the application of high-strength steel sheets to car bodies is continuously increasing. The strength of the laser overlap welds is determined by the strength distribution of weldments and the bead width at the faying surface. In the case of high-strength steel sheets, it is difficult to predict the fracture load and fracture mode during the tensile shear test of the weldment owing to the high strength of the base material, softening of the heat affected zone (HAZ), and small bead width. In this study, we investigated machine learning algorithms, including artificial neural networks, to develop a fracture mode classification model and regression models for joint strength and bead width. Machine learning algorithms have shown excellent performance in predicting mechanical behaviors during tensile shear tests. Among the machine learning regression algorithms, Gaussian process regression showed the best regression ability. The R2 values for the bead width and fracture load models were 0.98 and 0.99, respectively. Several machine learning models, including shallow neural networks, have shown perfect estimates for fracture locations.

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