Material Model Calibration Using Machine Learning: A Comparative Study
Keywords:neural network, material properties, Duplex Stainless Steel, multiscale modeling
A methodology based on Machine Learning, namely Fully Connected Neural Networks, is proposed to replace traditional parameter calibration strategies. In particular, the relation between hardness, yield strength and tensile strength is explored. The proposed methodology is used to predict the yield strength and the tensile strength of a Super Duplex Stainless Steel that was not included in the neural network training data base. Moreover, it is also used to determine such material parameters for individual microstructural phases, which feed a multiscale Finite Element simulation. The methodology is experimentally validated.
The high-throughput highway to computational materials design.
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