首页|Findings from China Iron and Steel Research Institute Group Broaden Understandin g of Machine Learning (Prediction and Rational Design of Stacking Fault Energy o f Austenitic Alloys Based On Interpretable Machine Learning and Chemical Composi tion)
Findings from China Iron and Steel Research Institute Group Broaden Understandin g of Machine Learning (Prediction and Rational Design of Stacking Fault Energy o f Austenitic Alloys Based On Interpretable Machine Learning and Chemical Composi tion)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ma chine Learning. According to news reporting from Beijing, People’s Republic of C hina, by NewsRx journalists, research stated, “Accurately predicting the stackin g fault energy (SFE), as one of the crucial factors influencing the material def ormation mechanism, is a focal point in research. This study utilizes measured S FE values from the literature on austenitic alloys to establish a predictive mod el for the relationship between chemical composition and SFE using machine learn ing techniques.” Financial support for this research came from National Key Research and Developm ent Program of China.
BeijingPeople's Republic of ChinaAsi aAlloysCyborgsEmerging TechnologiesMachine LearningChina Iron and Stee l Research Institute Group
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