Micromechanical modelling fatigue and tribological behaviour of fibre-reinforced composites

This study presents a novel, comprehensive analysis of reinforced composite structures using analytical, experimental, and statistical methods. A multi-scale prediction platform for fibre-reinforced composite materials is investigated, incorporating micromechanics, fatigue life prediction, and tribological behaviour analysis. It encompasses optimized Halpin-Tsai equations with calibrated shape factors, progressive damage modelling, S-N curve fatigue analysis with R-ratio effects, and abrasive/adhesive wear mechanisms to enable precise property predictions from constituent material to laminate performance. The micromechanics module uses sophisticated shape factors (ζ=1.0 for transverse modulus, ζ=0.5 for shear modulus) which eliminate systematic overestimation errors from traditional formulations. Fatigue analysis covers S-N curve modelling with high sensitivity to mean stress, environmental adjustments for temperature and moisture, and progressive damage accumulation. Wear module simulates both adhesion and abrasion mechanisms with material hardness sensitivity and environmental sensitivities. Full Monte Carlo uncertainty analysis yields 95% confidence intervals for all predictions. Experimental validation against literature data demonstrates excellent accuracy with R2 > 0.90 for all the models: fatigue (R2= 0.914), wear (R2=0.998), and micromechanics properties (R2 > 0.99). The integrated system possesses 3.7 % mean prediction error, realistic fatigue life predictions with up to 3× enhancement for fully reversed loading, and convenient wear life predictions in engineering time scales. The validated framework enables rapid composite design cycles with fewer experimental test requirements and high-fidelity predictions that are relevant to aerospace, automotive, and renewable energy applications where durability is an issue.