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02 December 2022
Paper (Preprint, Research Paper, Review Paper, White Paper, etc.)
Titel

Robustness in Fatigue Strength Estimation

Titel Supplements
Published on arXiv
Abstract
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.
Author(s)
Weichert, Dorina
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Kister, Alexander
Houben, Sebastian
Ernis, Gunar
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Wrobel, Stefan
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Project(s)
ML2R
Funder
Bundesministerium für Bildung und Forschung -BMBF-
Konferenz
Workshop on AI to Accelerate Science and Engineering 2023
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DOI
10.48550/arXiv.2212.01136
Externer Link
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Language
English
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Tags
  • Machine Learning

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