首页|Findings from University of Science and Technology Beijing Has Provided New Data on Machine Learning (Selection of Mechanical Properties of Uranium and Uranium Alloys After Corrosion Based On Machine Learning)
Findings from University of Science and Technology Beijing Has Provided New Data on Machine Learning (Selection of Mechanical Properties of Uranium and Uranium Alloys After Corrosion Based On Machine Learning)
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NETL
NSTL
Elsevier
Investigators discuss new findings in Machine Learning. According to news reporting from Beijing, People's Republic of China, by NewsRx journalists, research stated, "Uranium and uranium alloys play a vital role as service materials in strategic equipment, but their mechanical properties can be adversely affected by corrosion. Therefore, accurately predicting the mechanical properties of uranium and uranium alloys after corrosion holds significant importance." Financial support for this research came from NATIONAL KEY R & D PROGRAM OF CHINA. The news correspondents obtained a quote from the research from the University of Science and Technology Beijing, "In this study, we created a database to investigate the impact of oxygen corrosion on the mechanical properties of uranium and uranium alloys. Uti-lizing this database, we developed a featureguided decision tree algorithm to predict various tensile properties, including yield strength, tensile strength, elongation, and cross-section shrinkage. Our research highlights three key findings. Firstly, we established a machine learning modeling framework that effectively predicts tensile properties and exhibits potential for predicting other properties of uranium and uranium alloys. Secondly, through feature engineering, we uncovered crucial correlations involving reaction time, reaction temperature, alloy type, phase structure composition, and phase number. These correlations significantly enhanced the per-formance of machine learning models in predicting tensile properties after oxygen corrosion." According to the news reporters, the research concluded: "Lastly, by employing the decision tree algorithm guided by feature engineering, we successfully predicted the mechanical properties of uranium and uranium alloys after oxygen corrosion with a prediction error of less than 5%." This research has been peer-reviewed.
BeijingPeople's Republic of ChinaAsiaActinoid Series ElementsAlloysCyborgsEmerging TechnologiesEngineeringMachine LearningUraniumUniversity of Science and Technology Beijing
NETL
NSTL
Elsevier
