首页|Reports from University of Science and Technology Beijing Add New Study Findings to Research in Machine Learning (Machine Learning-Assisted Prediction of Corros ion Behavior of 7XXX Aluminum Alloys)
Reports from University of Science and Technology Beijing Add New Study Findings to Research in Machine Learning (Machine Learning-Assisted Prediction of Corros ion Behavior of 7XXX Aluminum Alloys)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News - Investigators publish new report on ar tificial intelligence. According to news reporting from Beijing, People’s Republ ic of China, by NewsRx journalists, research stated, “High-strength and lightwei ght 7XXX Al alloys are widely applied in aerospace industries.” Financial supporters for this research include The National Key R&D Program of China; The National Nature Science Foundation of China. The news reporters obtained a quote from the research from University of Science and Technology Beijing: “Stress corrosion cracking (SCC) in these alloys has be en extensively discussed, and electrochemical corrosion should be brought to the forefront when these materials are used in marine atmospheric environments. Thi s work obtained the corrosion potentials (Ecorr) and corrosion rates of 40 as-ca st 7XXX Al alloys by potentiodynamic polarization tests and immersion tests, res pectively; then, chemical compositions and physical features were used to build a machine learning model to predict these parameters. RFR was used for the predi ction model of Ecorr with the features Cu, Ti, Al, and Zn, and GPR for that of t he corrosion rate with the features of specific heat, latent heat of fusion, and proportion of p electrons. The physical meaning and reasonability were discusse d based on the analysis of corrosion morphology and precipitated composition.”
University of Science and Technology Bei jingBeijingPeople’s Republic of ChinaAsiaAlloysCyborgsEmerging Techn ologiesMachine Learning
NETL
NSTL
