A Bayesian framework for fatigue life prediction of composite laminates under co-existing matrix cracks and delamination

This paper proposes a particle filter-based Bayesian framework for damage prognosis of composite laminates exhibiting concurrent matrix cracks and delamination. Literature shows a number of applications of particle filtering for real-time prognosis of metallic structures and, recently, matrix crack density evolution in composites. The work presented here enhances the methodology proposed in previous papers by extending the Bayesian framework to multiple damage mechanisms, and validates the approach using damage progression data from notched cross-ply CFRP coupons subject to tension-tension fatigue. A multiple damage-mode model for the estimation of the strain energy release rate and the remaining stiffness of damaged laminates constitutes the core of the particle filtering algorithm, thus allowing the prognostic framework to extend for monitoring of simultaneous, coexisting damages. Also, the damage state can be evolved into the future enabling simulation of damage progression and prediction of remaining useful life of the composite material. The proposed prognostic unit successfully predicts damage growth and fatigue life of the laminate, and the results are critically discussed with respect to filtered estimation of damage progression and remaining life prediction.