A stochastic fatigue damage method for composite materials based on Markov chains and infrared thermography

Stochastic Markov chain methods are applied to model the fatigue damage evolution in composite materials subjected to cyclic mechanical loadings and monitored by infrared thermography (IR-T) techniques. The IR signal from the surface of open-hole S2-glass/E733FR laminates is captured concurrently during constant amplitude fatigue loadings. The IR testing has high thermal sensitivity below 10 mK and the image integration is synchronized with the mechanical loading. A thermoelastic stress analysis (TSA) technique using thermomechanics is used to process the IR fields and relate them to surface stresses and strains. Damage metric is developed for the composite samples based on an area stress reduction threshold. The applicability and validity of the proposed TSA damage index is evaluated by comparing it to the classical overall stiffness reduction measured at select fatigue intervals using an extensometer. The IR-TSA damage index at the last fatigue cycle is used to calibrate the Markov chain models (MCMs). The damage predictions of the MCMs are then examined at different fatigue cycles. A new method is proposed to construct a stochastic S–N curve utilizing the MCMs. The proposed IR-TSA with Markov stochastics is shown to be very effective in predicting the damage evolution and allowed constructing a wide-range of stochastic S–N curves for several composite material systems including experimental results from the literature.