首页|Findings from Hebei University of Technology Has Provided New Data on Machine Le arning (Determination of Hardness and Young's Modulus In Fcc Cu-ni-sn-al Alloys Via High-throughput Experiments, Calphad Approach and Machine Learning)
Findings from Hebei University of Technology Has Provided New Data on Machine Le arning (Determination of Hardness and Young's Modulus In Fcc Cu-ni-sn-al Alloys Via High-throughput Experiments, Calphad Approach and Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Machine Learning. According to news reporting originating from Tianjin, People's Republ ic of China, by NewsRx correspondents, research stated, "Hardness and Young's mo dulus are critical indicators in the design of innovative Cu-Ni-Sn-Al alloys wit h desired elastic and strength properties. In this study, the composition-depend ent hardness and Young's modulus in the fcc Cu-Ni-Sn-Al alloys were determined u sing high-throughput experiments, the CALPHAD (CALculation of PHAse Diagrams) ap proach, and machine learning (ML) model." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Natural Science Foundation of Hebei Province, National Joint Engineering Research Center for Abrasion Control and Molding of Metal Materials .
TianjinPeople's Republic of ChinaAsi aAlloysCyborgsEmerging TechnologiesMachine LearningHebei University of Technology
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