首页|Researchers from University of New Brunswick Detail New Studies and Findings in the Area of Machine Learning (Constitutive Modeling of High-temperature Deformat ion Behavior of Nonoriented Electrical Steels As Compared To Machine Learning)
Researchers from University of New Brunswick Detail New Studies and Findings in the Area of Machine Learning (Constitutive Modeling of High-temperature Deformat ion Behavior of Nonoriented Electrical Steels As Compared To 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 Machine Learn ing have been published. According to news reporting out of Fredericton, Canada, by NewsRx editors, research stated, “Hot rolling is a critical thermomechanical processing step for nonoriented electrical steel (NOES) to achieve optimal mech anical and magnetic properties. Depending on the silicon and carbon contents, th e electrical steel may or may not undergo austenite-ferrite phase transformation during hot rolling, which requires different process controls as the austenite and ferrite show different flow stresses at high temperatures.” Funders for this research include Natural Resources Canada through the Office fo r Energy Research and Development (OERD), Transport Canada’s Clean Transportatio n System - Research and Development Program (CTS-RD), Natural Sciences and Engin eering Research Council of Canada (NSERC), Canada Foundation for Innovation, Atl antic Canada Opportunities Agency (ACOA), New Brunswick Innovation Foundation (N BIF), Natural Resources Canada library.
FrederictonCanadaNorth and Central A mericaCyborgsEmerging TechnologiesMachine LearningUniversity of New Brun swick
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