首页|New Machine Learning Study Findings Have Been Reported from Beihang University (Obtaining Strength and Ductility Synergy for Directed Energy Deposited Ti17 Alloys By Machine Learning)
New Machine Learning Study Findings Have Been Reported from Beihang University (Obtaining Strength and Ductility Synergy for Directed Energy Deposited Ti17 Alloys By Machine Learning)
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NETL
NSTL
Elsevier
2024 FEB 02 (NewsRx) – 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 orig- inating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “Recently, obtaining a balance of tensile strength and ductility in direct energy deposited (DED) titanium alloy com- ponents has been a major concern, which obstructs their further application. Herein, machine learning (ML) methods were applied to find the optimal process window of DEDed titanium alloy parts from a wide range of possible depositing process variables.” Financial supporters for this research include National Major Science and Tech- nology Projects of China, National Natural Science Foundation of China (NSFC). Our news editors obtained a quote from the research from Beihang University, “Four algorithms were used for ML model training and tensile property prediction of DEDed titanium alloy. The results showed that the prediction ability of the XGBoost model was the best.”
BeijingPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesLight MetalsMachine LearningTitaniumBeihang University
NETL
NSTL
Elsevier
