Findings on Machine Learning Reported by Investigators at BeijingUniversity of Technology (Physics-informed Machine Learning for Tribological Properties Predic tion of S32750/cfrpeek Tribopair Under Seawater Lubrication Via Pissa-cnn-lstm)
Findings on Machine Learning Reported by Investigators at BeijingUniversity of Technology (Physics-informed Machine Learning for Tribological Properties Predic tion of S32750/cfrpeek Tribopair Under Seawater Lubrication Via Pissa-cnn-lstm)
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 to newsreporting out of Beijing, People’s Re public of China, by NewsRx editors, research stated, “Tribologicalproperties an d wear mechanisms of S32750 stainless steel/carbon fiber-reinforced polyethereth erketone(CFRPEEK) tribopair in seawater under different salinities, sliding spe eds and loads were experimentallystudied. A novel Physics-Informed constraint s trategy has been originally developed and combined withsparrow search algorithm (SSA) to optimize a Convolutional Neural Network-Long Short-Term Memory(CNN-LS TM) model for predicting friction coefficient.”
Key words
Beijing/People’s Republic of China/Asi a/Cyborgs/Emerging Technologies/Machine Learning/Beijing University of Techn ology
NETL
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