首页|Reports on Machine Learning Findings from China Iron and Steel Research Institute Group Provide New Insights (Machine-learning Assisted Design of As-cast Nicofecralti Multi-principal Element Alloys With Tensile Yield Strength Over 1.35 Gpa)
Reports on Machine Learning Findings from China Iron and Steel Research Institute Group Provide New Insights (Machine-learning Assisted Design of As-cast Nicofecralti Multi-principal Element Alloys With Tensile Yield Strength Over 1.35 Gpa)
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NETL
NSTL
Elsevier
A new study on Machine Learning is now available. According to news reporting from Beijing, People's Republic of China, by NewsRx journalists, research stated, "As-cast alloys have the advantage of short forming processes, but there is currently a lack of research on systematic design alloys with better mechanical properties. Herein, combining a machine-learning with random forest model algorithm, a high-throughput alloy design framework under multidimensional constraints was used to discover new NiCoFeCrAlTi multi-principal element alloys (MPEAs) for superior tensile properties." Funders for this research include National Natural Science Foundation of China (NSFC), GIMRT Program of the Institute for Materials Research, Tohoku University, Fundamental Research Funds for the Central Universities. The news correspondents obtained a quote from the research from China Iron and Steel Research Institute Group, "The as-cast dual-phase Ni28Fe32Cr25Al10Ti5 alloy with 1386 MPa of tensile yield strength and 1.8% uniform elongation was designed, which is much higher than the best value in the original training dataset. This apparent high strength can be attributed to the phase interfacial strengthening, in which the soft face-centered cubic (FCC) phase precipitated extensively aside the grain boundaries of hard bodycentered cubic (BCC) matrix. The BCC matrix provides high strength and FCC precipitates play role in ductility."
BeijingPeople's Republic of ChinaAsiaAlloysCyborgsEmerging TechnologiesMachine LearningChina Iron and Steel Research Institute Group
NETL
NSTL
Elsevier
