首页|Reports from Ajou University Describe Recent Advances in Machine Learning (Enhan cing flow stress predictions in CoCrFeNiV high entropy alloy with conventional and machine learning techniques)
Reports from Ajou University Describe Recent Advances in Machine Learning (Enhan cing flow stress predictions in CoCrFeNiV high entropy alloy with conventional and machine learning techniques)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Current study results on artificial in telligence have been published. According to newsreporting from Suwon, South Ko rea, by NewsRx journalists, research stated, “A machine learning techniquelever aging artificial intelligence (AI) has emerged as a promising tool for expeditin g the exploration anddesign of novel high entropy alloys (HEAs) while predictin g their mechanical properties at both room andelevated temperatures.” Funders for this research include National Research Foundation of Korea; Ministr y of Science, Ict AndFuture Planning; Ministry of Education.Our news journalists obtained a quote from the research from Ajou University: “I n this paper, wepredict the flow stress of hot-compressed CoCrFeNiV HEAs using conventional (qualitative and quantitativemodels) and advanced machine learning approaches across various temperature and strain rate conditions.Conventional modeling methods, including the modified Johnson-Cook (JC), modified Zerilli-Arm strong(ZA), and Arrhenius-type constitutive equations, are employed. Simultaneo usly, machine learning modelsare utilized to forecast flow stress under differe nt hot working conditions. The performance of bothconventional and machine lear ning models is evaluated using metrics such as coefficient of determination(R2) , mean abosolute error (MAE), and root mean squared error (RMSE).”
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