TY  - INPR
U1  - Preprint
A1  - Weichert, Dorina
A1  - Kister, Alexander
A1  - Houben, Sebastian
A1  - Ernis, Gunar
A1  - Wrobel, Stefan
T1  - Robustness in Fatigue Strength Estimation
N2  - 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.
Y1  - 2022
U6  - https://doi.org/10.48550/arXiv.2212.01136
DO  - https://doi.org/10.48550/arXiv.2212.01136
AX  - 2212.01136
PB  - arXiv
ER  -