CC BY 4.0Weichert, DorinaDorinaWeichertHaedecke, Elena GinaElena GinaHaedeckeErnis, GunarGunarErnisHouben, SebastianSebastianHoubenKister, AlexanderAlexanderKisterWrobel, StefanStefanWrobel2025-07-142025-07-142025-04-10https://doi.org/10.24406/publica-4871https://publica.fraunhofer.de/handle/publica/48947410.1007/978-3-031-83097-6_610.24406/publica-48712-s2.0-105003696122A vital material property of metals is long life fatigue strength. It describes the maximum load that can be cyclically applied to a defined specimen for a number of cycles that is thought to represent an infinite lifetime. The experimental measurement of long life fatigue strength is costly, justifying the need to create a precise estimate with as few experiments as possible. We propose a new approach for estimating long life fatigue strength that defines a ready-to-use experimental and analysis procedure. It relies on probabilistic machine learning methods, efficiently connecting expert knowledge about the material behavior and the test setup with historical and newly generated data. A comparison to state-of-the-art standard experimental procedures shows that our approach requires fewer experiments to produce an estimate at the same precision-massively reducing experimental costs.entruebook article