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.

  • Published in:
    AI to Accelerate Science and Engineering Workshop at AAAI Conference on Artificial Intelligence
  • Type:
    Inproceedings
  • Authors:
    Weichert, Dorina; Kister, Alexander; Houben, Sebastian; Ernis, Gunar; Wrobel, Stefan
  • Year:
    2023

Citation information

Weichert, Dorina; Kister, Alexander; Houben, Sebastian; Ernis, Gunar; Wrobel, Stefan: Robustness in Fatigue Strength Estimation, AI to Accelerate Science and Engineering Workshop at AAAI Conference on Artificial Intelligence, 2023, https://ai-2-ase.github.io/papers/9SubmissionAI2ASE_2023_CameraReady.pdf, Weichert.etal.2023a,

Associated Lamarr Researchers

lamarr institute person Weichert Dorina - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Dorina Weichert

Autorin to the profile
lamarr institute person Houben Sebastian - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Dr. Sebastian Houben

Author to the profile
lamarr institute person Wrobel Stefan e1663925461852 - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Prof. Dr. Stefan Wrobel

Director to the profile