首页|Studies from Opole University of Technology Reveal New Findings on Machine Learn ing (Exploring the Impact of Phase-shifted Loading Conditions On Fatigue Life of S355j2 Mild Steel With Different Machine Learning Approaches)
Studies from Opole University of Technology Reveal New Findings on Machine Learn ing (Exploring the Impact of Phase-shifted Loading Conditions On Fatigue Life of S355j2 Mild Steel With Different Machine Learning Approaches)
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 – Investigators discuss new findings in Machine Learning. According to news reportingoriginating from Opole, Poland, by NewsRx correspondents, research stated, “Predicting a component’sfatigue life requires information on not only the number of stress cycles the component will undergo but alsothe kind and frequency of those stress cycles, as well as infor mation about the surrounding environmentand the intended purpose of the compone nt. Models that can forecast lifespan by utilizing availableexperimental data a re preferred since fatigue investigations are costly and time-consuming.”
OpolePolandEuropeCyborgsEmerging TechnologiesMachine LearningOpole University of Technology
NETL
NSTL
