首页|New Findings from Northeastern University Describe Advances in Machine Learning (Machine learning-assisted composition design of W-free Co-based superalloys wit h high g'-solvus temperature and low density)
New Findings from Northeastern University Describe Advances in Machine Learning (Machine learning-assisted composition design of W-free Co-based superalloys wit h high g'-solvus temperature and low density)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on artificial intelligen ce have been presented.According to news originating from Shenyang,People's Re public of China,by NewsRx editors,the research stated,"Developing materials w ith multiple desired characteristics is a tremendous challenge,particularly in an elaborate material system." Financial supporters for this research include National Natural Science Foundati on of China.Our news journalists obtained a quote from the research from Northeastern Univer sity:"Herein,a machine learning assisted material design strategy was applied to simultaneously optimize dual target attributes by considering g' solvus tempe rature and alloy density of multi-component Co-based superalloys.To verify the soundness of our strategy,four alloys were selected and experimentally synthesi zed from > 510,000 candidates,each of them possessing g' solvus temperature exceeding 1200 °C and alloy density below 8.3 g/cm3.Of thos e,Co-35Ni-12Al-5Ti-3V-3Cr-2Ta-2Mo (at.%) possesses the highest g' solvus temperature of 1250 °C and lower density of 8.2 g/cm3."
Northeastern UniversityShenyangPeopl e's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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