  Fatigue strength estimation is a costly manual material characterization
process in which state-of-the-art approaches follow a standardized experiment
and analysis procedure. In this paper, we examine a modular, Machine
Learning-based approach for fatigue strength estimation that is likely to
reduce the number of experiments and, thus, the overall experimental costs.
Despite its high potential, deployment of a new approach in a real-life lab
requires more than the theoretical definition and simulation. Therefore, we
study the robustness of the approach against misspecification of the prior and
discretization of the specified loads. We identify its applicability and its
advantageous behavior over the state-of-the-art methods, potentially reducing
the number of costly experiments.
