Report Summarizes Machine Learning Study Findings from University of Sheffield ( Design and Selection of High Entropy Alloys for Hardmetal Matrix Applications Us ing a Coupled Machine Learning and Calculation of Phase Diagrams Methodology)

Robotics & Machine Learning Daily News2024，Issue(Apr.16) ：6-6.

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Report Summarizes Machine Learning Study Findings from University of Sheffield ( Design and Selection of High Entropy Alloys for Hardmetal Matrix Applications Us ing a Coupled Machine Learning and Calculation of Phase Diagrams Methodology)

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Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Investigators discuss new findings in Machine Learning. According to news reportingout of Sheffield, United Kingdom, by NewsRx editors, research stated, “This study aims to utilize acombined machi ne learning (ML) and CALculation of PHAse Diagrams (CALPHAD) methodology todesi gn hardmetal matrix phases for metal-forming applications that can serve as the basis for carbidereinforcement. The vast compositional space that high entropy alloys (HEAs) occupy offers a promisingavenue to satisfy the application design criteria of wear resistance and ductility.”

Key words

Sheffield/United Kingdom/Europe/Alloy s/Cyborgs/Emerging Technologies/Machine Learning/University of Sheffield

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出版年

2024

Robotics & Machine Learning Daily News

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