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A hybrid prognosis model for predicting fatigue crack propagation under biaxial in-phase and out-of-phase loading

Author(s):



Medium: journal article
Language(s): English
Published in: Structural Health Monitoring, , n. 4, v. 17
Page(s): 888-901
DOI: 10.1177/1475921717725019
Abstract:

A hybrid prognosis model has been developed to predict the crack propagation in aluminum alloys subject to biaxial in-phase and out-of-phase fatigue loading conditions. The novel methodology combines physics-based modeling with machine learning techniques to predict crack growth in aluminum alloys. Understanding the failure mechanisms under these complex loading conditions is critical to developing reliable prognostic models. Therefore, extensive fatigue tests were conducted to study the failure modes of carefully designed cruciform specimens. Energy release rate was used as the physics-based parameter and Gaussian process was used to model the complex nonlinear relationships in the prognosis framework. The methodology was used to predict crack propagation in Al7075-T651 under a range of loading conditions. The predictions from the prognosis model were validated using the data obtained from the biaxial tests. The results indicate that the algorithm is able to accurately predict the crack propagation under proportional, non-proportional, in-phase, and out-of-phase loading conditions.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1177/1475921717725019.
  • About this
    data sheet
  • Reference-ID
    10562093
  • Published on:
    11/02/2021
  • Last updated on:
    19/02/2021
 
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