Evaluation of fatigue strength of polymer-matrix composites based on thermographic data analysis

Accurate assessment of fatigue strength across different lifetimes traditionally requires conventional high-cycle fatigue (HCF) testing, a reliable but time consuming method. As an alternative, thermographic techniques (TT) offer a rapid estimation of fatigue strength, particularly for polymer matrix composites (PMCs) in the HCF regime. This study evaluates the effectiveness of two common thermographic approaches, the temperature-stress ∆T s - σ and heat dissipation rate-stress q- σ approaches for estimating fatigue strength ( σ TT ) in neat glass fiber-reinforced polymer (GFRP), GFRP with graphene nanoparticle reinforcement (GFRP-GNPs), and GFRP with hybrid nanoparticle reinforcement (GFRP-HNPs). Classical fatigue tests were conducted at 40 Hz under fully reversed loading (R = -1) and compared against thermographic predictions across multiple estimation methods: Bilinear (BL), Angular Change (AC), Minimum Curvature Radius (MCR), and Maximum Perpendicular Distance (MPD). Thermographic estimations showed strong correlation with fatigue strength σ S-N determined using S-N curve at 10 7 cycles, with the q- σ approach yielding an average relative error of only 0.6%. Among estimation techniques, the MPD method demonstrated the highest accuracy, with relative errors of -5.2% for