首页|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)

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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 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.”

BeijingPeople’s Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningBeijing University of Techn ology

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Nov.25)