首页|Researchers at University of Pittsburgh Publish New Study Findings on Machine Le arning (Aging heat treatment design for Haynes 282 made by wire-feed additive ma nufacturing using high-throughput experiments and interpretable machine learning )
Researchers at University of Pittsburgh Publish New Study Findings on Machine Le arning (Aging heat treatment design for Haynes 282 made by wire-feed additive ma nufacturing using high-throughput experiments and interpretable machine learning )
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on artificial in telligence have been published. According to news originating from Pittsburgh, P ennsylvania, by NewsRx editors, the research stated, “ABSTRACTWire-feed additive manufacturing (WFAM) produces superalloys with complex thermal cycles and uniqu e microstructures, often requiring optimized heat treatments.” Funders for this research include National Energy Technology Laboratory. Our news correspondents obtained a quote from the research from University of Pi ttsburgh: “To address this challenge, we present a hybrid approach that combines high-throughput experiments, precipitation simulation, and machine learning to design effective aging conditions for the WFAM Haynes 282 superalloy. Our result s demonstrate that the g’ radius is the critical microstructural feature for str engthening Haynes 282 during post-heat treatment compared with the matrix compos ition and g’ volume fraction. New aging conditions at 770°C for 50 hours and 730 °C for 200 hours were discovered based on the machine learning model and were ap plied to enhance yield strength, bringing it on par with the wrought counterpart .”
University of Pittsburgh, Pittsburgh, Pe nnsylvania, United States, North and Central America, Cyborgs, Emerging Technolo gies, Machine Learning
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