首页|Data on Machine Learning Detailed by Researchers at Northeastern University (Mac hine Learning-assisted Design of Low Elastic Modulus B-type Medical Titanium All oys and Experimental Validation)
Data on Machine Learning Detailed by Researchers at Northeastern University (Mac hine Learning-assisted Design of Low Elastic Modulus B-type Medical Titanium All oys and Experimental Validation)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Current study results on Machine Learning have be en published. According to news reporting from Shenyang, People’s Republic of Ch ina, by NewsRx journalists, research stated, “In this study, a method combining physical metallurgical models with machine learning was used to design beta-type medical titanium alloys with low modulus of elasticity in Ti -Mo -Nb -Zr -Sn sy stem alloys. The prediction model used the Extreme Gradient Boosting (XGBoost) a lgorithm to predict the elastic modulus of the alloys, and the Mo equivalent (Mo eq value) and valence electron concentration ratio (e/a), which characterize the elastic modulus, were modeled as feature parameters that can improved the gener alization ability of the model and reduced overfitting.”
ShenyangPeople’s Republic of ChinaAs iaAlloysCyborgsEmerging TechnologiesLight MetalsMachine LearningMini ng and MineralsTitaniumNortheastern University
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