Failure Mechanism and Damage Tolerance Correlation Analysis of Carbon Fiber Reinforced Composites Under Compression After Impact Based on Multi‐Source Monitoring

ABSTRACT A multi‐source monitoring approach integrating acoustic emission (AE), infrared thermography (IRT), and digital image correlation (DIC) was employed to investigate the compression‐after‐impact (CAI) failure mechanisms and damage tolerance of T800 carbon fiber reinforced polymer (CFRP) composites. CAI failure modes at representative impact energies were identified through load–displacement responses, out‐of‐plane displacement fields, and fracture morphologies. Damage modes were classified using principal component analysis combined with a Gaussian mixture model based on AE time‐frequency parameters. Thermodynamic responses during failure were characterized by IRT, while full‐field strain evolution and out‐of‐plane displacement distributions under different impact energies were analyzed using DIC. Pearson correlation analysis was further conducted to evaluate the relationships between key monitoring parameters and CAI damage tolerance. The results show that the displacement at peak load exhibits a non‐monotonic trend with increasing impact energy, reflecting alternating stiffness‐ and strength‐dominated behaviors. AE results reveal progressive damage evolution from matrix cracking to delamination and fiber fracture. At high impact energies, dual‐zone thermal hotspots correspond well with partitioned out‐of‐plane displacement fields, confirming localized buckling instability. Strong correlations are observed between CAI damage tolerance and AE energy, maximum average temperature rise, and axial strain. This study, validated through laboratory CAI tests, establishes an AE‐IRT‐DIC multi‐source synergistic monitoring and feature‐parameter screening methodology. It not only provides multi‐physics experimental evidence for a deeper understanding of CAI failure in composites, but also targets future in‐service structural health monitoring by informing the selection of key features required for post‐impact risk assessment.