Physics-constrained Gaussian process for life prediction under in-phase multiaxial cyclic loading with superposed static components

Aleksander Karolczuk, Yongming Liu, Krzysztof Kluger, Szymon Derda, Dariusz Skibicki, Łukasz Pejkowski

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Under multiaxial fatigue loading, the superposed static components are additional factors for life prediction models to be considered. The increased dimension in fatigue data imposes difficulties in pattern recognition using existing functional form models. A framework to build a Gaussian process (GP) model for lifetime prediction under multiaxial loading was developed to solve this problem. Physically consistent constraints were imposed by applying a novel technique on the GP model to control its behavior and to decrease an overfitting risk. The model consistency with the rotationally invariant principle of damage was provided by the application of the critical plane concept. The framework was demonstrated to have excellent prediction capability on S355 steel and 7075-T651 aluminum alloy. Five well-known fatigue models of functional forms were also implemented for comparison. Detailed parametric studies were presented for the training sample effect, GP kernel effect, and model predictability.

Original languageEnglish (US)
Article number107776
JournalInternational Journal of Fatigue
Volume175
DOIs
StatePublished - Oct 2023

Keywords

  • Fatigue life prediction
  • Machine learning
  • Multiaxial loading
  • Physics-constrained Gaussian process

ASJC Scopus subject areas

  • Modeling and Simulation
  • General Materials Science
  • Mechanics of Materials
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

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