摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-研究人员详细介绍了机器学习的新数据。根据新华社天津新闻报道，“硬度和杨氏模量是设计具有理想弹性和强度性能的新型Cu-Ni-Sn-Al合金的关键指标，本研究采用高通量实验，采用CALPHAD(Computation of PHAse Diographs)方法，测定了FCC Cu-Ni-Sn-Al合金的成分相关性硬度和杨氏模量。”和机器学习(ML)模型。本研究的资助单位包括国家自然科学基金(NSFC)、河北省自然科学基金、国家金属材料磨损控制与成型联合工程研究中心。
Abstract
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 .
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