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Robustness in Fatigue Strength Estimation

  • 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.

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Metadaten
Document Type:Preprint
Language:English
Author:Dorina Weichert, Alexander Kister, Sebastian Houben, Gunar Ernis, Stefan Wrobel
DOI:https://doi.org/10.48550/arXiv.2212.01136
ArXiv Id:http://arxiv.org/abs/2212.01136
Publisher:arXiv
Date of first publication:2022/12/02
Departments, institutes and facilities:Fachbereich Informatik
Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE)
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2022/12/14