A Bayesian framework for calibration of multiaxial fatigue curves

A Bayesian framework is proposed to re-formulate a multiaxial fatigue model and produce probabilistic stress -life fatigue curves from experimental data. The proposed framework identifies the experimentally-driven parameters governing the multiaxial fatigue model, in the form of probability distributions. Classical and hierarchical Bayesian inference strategies are presented, accompanied by rigorous analytical expressions for calculating the joint posterior distributions necessary for in-field implementation. An example illustrates the application of the proposed hierarchical Bayesian inference framework and how it compares to a deterministic approach. This probabilistic treatment makes the existing fatigue models suitable for exercising uncertainty propagation for reliability analysis and design purposes.

Automated summary

A Bayesian framework is proposed to generate probabilistic stress-life fatigue curves and identify parameters of a multiaxial fatigue model using experimental data. Classical and hierarchical Bayesian inference strategies are presented, along with analytical expressions for calculating the joint posterior distributions. An example demonstrates the application of the proposed hierarchical Bayesian inference framework and its comparison to a deterministic approach. This probabilistic treatment enables uncertainty propagation for reliability analysis and design using existing fatigue models.