首页|Research Data from Central Iron and Steel Research Institute Update Understandin g of Machine Learning (The Prediction of Flow Stress in the Hot Compression of a Ni-Cr-Mo Steel Using Machine Learning Algorithms)
Research Data from Central Iron and Steel Research Institute Update Understandin g of Machine Learning (The Prediction of Flow Stress in the Hot Compression of a Ni-Cr-Mo Steel Using Machine Learning Algorithms)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New study results on artificial intelligence have been published. According to news reporting from Beijing, People's Republic of China, by NewsRx journalists, research stated, "The constitutive model refers to the mapping relationship between the stress and deformation conditions (such as strain, strain rate, and temperature) after being loaded." Funders for this research include Basic Scientific Research Project of Education Department of Liaoning Province For Colleges And Universities. The news editors obtained a quote from the research from Central Iron and Steel Research Institute: "In this work, the hot deformation behavior of a Ni-Cr-Mo st eel was investigated by conducting isothermal compression tests using a Gleeble- 3800 thermal simulator with deformation temperatures ranging from 800 °C to 1200 °C, strain rates ranging from 0.01 s-1 to 10 s-1, and deformations of 55% . To analyze the constitutive relation of the Ni-Cr-Mo steel at high temperature s, five machine learning algorithms were employed to predict the flow stress, na mely, back-propagation artificial neural network (BP-ANN), Random Committee, Bag ging, k-nearest neighbor (k-NN), and a library for support vector machines (libS VM). A comparative study between the experimental and the predicted results was performed."
Central Iron and Steel Research Institut eBeijingPeople's Republic of ChinaAsiaAlgorithmsCyborgsEmerging Tech nologiesMachine Learning
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