Fatigue life prediction of laminated composites using a multi-scale M-LaF and Bayesian inference

This paper presents a probabilistic model for fatigue life estimation of composite laminates using a high fidelity multi-scale approach called M-LaF (Micromechanics based approach for Fatigue Life Failure). To this end, square and hexagonal representative unit cells are introduced to calculate constituent stresses using a bridging matrix between macro and micro stresses referred to as the stress amplification factor matrix. The M-LaF is based on the constituent level input data that makes it possible to predict fatigue life of a variety of laminates with any possible fiber volume fraction. The M-LaF model parameters are calibrated as posterior distribution using the Bayesian inference methodology. A reference test data from literature was used for parameter calibration. The calculated posterior statistics were then used to calculate probabilistic fatigue life estimates of sample laminates. The predicted S–N curves are in good agreement with the test data for a range of composite laminas as well as laminates with different fiber volume fractions and under diverse stress ratios. As an illustration, the above approach was applied to a wind turbine blade to show the effect of multi-axial loading on the fatigue life of composite laminates.