Hygrothermal-vibration coupled aging prediction of CFRP laminates via multiscale modeling and deep learning

Abstract

Carbon fiber reinforced polymer (CFRP) laminates applied in aircraft engine nacelles suffer complex coupled aging effects of hygrothermal and vibration loads, while existing studies rarely consider such multi-physics coupling conditions. This study developed a novel experimental platform capable of simultaneously applying hygrothermal and vibration loads to CFRP laminates, combined with multiscale numerical simulations and deep learning approaches for accurate aging prediction. Experimental tests yield bending performance data under limited aging durations due to time and cost constraints, resulting in coarse fitting curves and insufficient accuracy for performance prediction at arbitrary aging times. Concurrently, continuous monitoring of electrical resistance during aging revealed significant conductivity degradation, providing complementary insight into progressive damage potentially linked to fiber integrity, an aspect often overshadowed by matrix degradation studies. To address these data limitations and capture internal mechanisms, a multiscale finite element method (FEM) model incorporating temperature and humidity-dependent material properties is established to obtain internal stress–strain states and dynamic microscopic damage processes inaccessible through direct experiments. Time-series Generative Adversarial Networks (TimeGAN) augment the experimental dataset, while an improved Gated Recurrent Unit (GRU) -enhanced graph neural network (GNN) captures the temporal evolutions of internal stress–strain and environmental parameters, enabling precise predictions of residual mechanical properties. Validation experiments confirm superior prediction accuracy compared to traditional fitting methods, providing robust guidance for aerospace composite reliability assessment.