首页|New Machine Learning Findings Reported from Swinburne University of Technology ( Application of Machine Learning for the Prediction of Particle Velocity Distribu tion and Deposition Efficiency for Cold Spraying Titanium Powder)
New Machine Learning Findings Reported from Swinburne University of Technology ( Application of Machine Learning for the Prediction of Particle Velocity Distribu tion and Deposition Efficiency for Cold Spraying Titanium Powder)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Current study results on Machine Learn ing have been published. According tonews reporting originating in Hawthorn, Au stralia, by NewsRx journalists, research stated, “This studydemonstrates the ef ficacy of machine learning (ML) techniques, specifically Support Vector Regressi on(SVR) and Neural Network (NN) models, in predicting the spray plume character istic particle velocitydistribution during cold spraying of Titanium. Consideri ng the complexity of particle velocity distribution,models with the single part icle velocity, average particle velocity and particle count in the spray plume have been explored associated with a novel data binning mechanism.”
HawthornAustraliaAustralia and New Z ealandCyborgsEmerging TechnologiesLight MetalsMachine LearningTitaniumSwinburne University of Technology
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