Paris' Law Parameters Estimation for Fatigue Crack Prediction of An Aluminum Alloy Plate Under Cyclic Loading

• Author(s) / Creator(s)

  • Baral, Talaz
  • Afshari, Sajad Saraygord
  • Liang, Xihui

• Fatigue-induced crack initiation and growth are common in cyclically loaded structures. Crack reduces the structures' strength and increases the risk of failure. Engineers have been using degradation models like Paris' law to relate crack length to the magnitude of the applied load to schedule repair and maintenance of such structures. The presented study developed a methodology to estimate the Paris' law parameters that can be used to predict the fatigue lifetime of 2024-T3 aluminum alloy plate under cyclic loading. An optimization method is also developed to optimize Paris' law parameters and their standard deviation, making the model more reliable. At the same time, the case of unavailable magnitude of the applied load is considered in this research. The optimized parameters are further updated using Bayesian updating with the help of condition monitoring data to increase the accuracy of crack length estimation. Virkler crack propagation data for an aluminum alloy plate is used to develop and validate the proposed method. The experimental samples' validation results show that our model's average error or structure's lifetime prediction based on crack length is 1.5% when the crack on validation samples reaches 71% of the failure threshold.

  Part of the Proceedings of the Canadian Society for Mechanical Engineering International Congress 2022.

• Date created

  2022-06-01

• Subjects / Keywords

  • Paris' law
  • Probabilistic fatigue
  • crack prediction
  • Genetic Algorithm
  • Bayesian Updating

• Type of Item

  Article (Published)

• DOI

  https://doi.org/10.7939/r3-wy54-f333

• License

  Attribution-NonCommercial 4.0 International