初歩的な人工知能によるDP鋼の高次元組織データ駆動型応力−ひずみ曲線の予測

抄録

Prediction of a stress-strain curve of ferrite-martensite DP steels was studied by a combined technique of Bayesian inference and artificial neural network. To screen a descriptor to be used for neural network analysis, material genomes such as volume fraction, micro-hardness, handle, and void of martensite phase, and micro-hardness of ferrite phase were examined by Bayesian inference. In a case of small data set, a machine learning method to predict mechanical properties reliably was proposed.