首页|Investigators at Auburn University Report Findings in Machine Learning (A Compre hensive Study On the Effects of Surface Postprocessing On Fatigue Performance o f Additively ManufacturedAlsi10mg: an Augmented Machine Learning Perspective On …)
Investigators at Auburn University Report Findings in Machine Learning (A Compre hensive Study On the Effects of Surface Postprocessing On Fatigue Performance o f Additively ManufacturedAlsi10mg: an Augmented Machine Learning Perspective On …)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News ; New research on Machine Learning is th e subject of a report. According to newsreporting originating from Auburn, Alab ama, by NewsRx correspondents, research stated, “In this study,the efficacy of 21 distinct surface post-processing methods on the fatigue behavior of an additi vely manufactured(AM) material was investigated. Various treatments, encompassi ng single-step processes like sandblasting (SB), conventional shot peening (CSP ), severe shot peening (SSP), gradient severe shot peening(GSSP), ultrasonic sh ot peening (USP), severe vibratory peening (SVP), ultrasonic nanocrystal surfacemodification (UNSM), laser shock peening (LSP), laser polishing (LP), tumble fi nishing (TF), chemicalpolishing (CP), electrochemical polishing (ECP), and mach ining (M), as well as hybrid treatments, weresystematically investigated for th eir impact on the fatigue behavior of hourglass laser powder based fused(LB-PBF ) AlSi10Mg specimens.”
AuburnAlabamaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningAuburn Univ ersity
NETL
NSTL
