首页|Study Data from Hunan University Provide New Insights into Machine Learning (Uti lizing a Combination of Experimental and Machine Learning Methods To Predict and Correlate Between Accelerated and Natural Aging of Cfrp/al Adhesive Joints Unde r ...)
Study Data from Hunan University Provide New Insights into Machine Learning (Uti lizing a Combination of Experimental and Machine Learning Methods To Predict and Correlate Between Accelerated and Natural Aging of Cfrp/al Adhesive Joints Unde r ...)
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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 out of Sanya, People’s Repu blic of China, by NewsRx editors, research stated, “Thisstudy investigates how carbon fiber reinforced polymer (CFRP)-to-aluminum adhesive joints behave underaccelerated aging conditions with hygrothermal exposure and compares these findi ngs against naturallyaged samples to evaluate material reliability in challengi ng environments. The CFRP-to-aluminum adhesivejoints were manufactured and subj ected to natural aging for durations ranging from 1 to 3 years with6-month inte rvals, as well as accelerated aging (hygrothermal) for periods ranging from 100 to 1200h, with intervals of 50 h. Subsequently, the mechanical properties of th e joints were evaluated using athree-point bending test.”
SanyaPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningHunan University
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