首页|Data on Machine Learning Described by Researchers at Taiyuan University of Techn ology (A brief review of machine learningassisted Mg alloy design, processing, and property predictions)

Data on Machine Learning Described by Researchers at Taiyuan University of Techn ology (A brief review of machine learningassisted Mg alloy design, processing, and property predictions)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in artificial intelligence. According to news reporting from Taiyuan, People’s Repu blic of China, by NewsRx journalists, research stated, “Owing to the hexagonal c lose-packed (HCP) crystal structure inherent in Mg alloys, strong basal texture can readily be induced through the processes of rolling or extrusion. The anisot ropy of the texture of Mg alloys impacts their stamping and forming capabilities , limiting their use in certain applications.” Our news editors obtained a quote from the research from Taiyuan University of T echnology: “Microalloying and shear deformation are currently the most common me thods of weakening the texture of Mg alloys. Many shearing processes have been e xtensively studied, and given that they require complex equipment and make it di fficult to achieve mass production, major attention has turned to studying the d esign of microalloys. Traditional trial-and-error approaches for developing micr o-alloying confront many challenges, including longer test cycles and increasing expenses. The rapid advancement of big data and artificial intelligence opens u p a new channel for the efficient advancement of metallic materials, specificall y the application of machine learning to aid in the design of Mg alloys. ML mode ling can be used to find correlations between features and attributes in data, a llowing for the development and design of high-performance Mg alloys. The articl e provides an extensive overview of machine learning applications in Mg alloys.”

Taiyuan University of TechnologyTaiyua nPeople’s Republic of ChinaAsiaAlloysCyborgsEmerging TechnologiesMac hine Learning

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Jun.6)