Full-field damage monitoring frameworks for composite laminates — combining DIC, Acoustic Emission, and Smoothing Element Analysis.
My research develops model-free, full-field experimental methodologies that combine multiple sensing modalities to characterize and monitor damage in fiber-reinforced composite laminates. The core philosophy: extract damage indicators directly from experimental measurements, without relying on finite element models or constitutive damage laws.
Full-field surface displacement and strain mapping with sub-pixel accuracy for crack localization
Real-time detection and temporal classification of damage-related stress wave events
Noise-robust gradient evaluation enabling sharp localization index computation from DIC fields
High-precision local mechanical strain measurement for point-wise deformation tracking and load analysis
This study introduces an original dual-stage SEA-assisted DIC framework for full-field crack propagation monitoring in cracked composite laminates. The novelty lies in applying Smoothing Element Analysis at two distinct DIC post-processing stages: displacement regularization before strain reconstruction, and equivalent-strain smoothing before gradient evaluation.
This study introduces an integrated experimental framework for damage characterization and crack propagation monitoring in pre-cracked composite laminates by combining DIC, Acoustic Emission, and strain gauge measurements. Rather than relying on a single measurement output, the framework evaluates spatial, temporal, and local mechanical evidence together through a stage-wise interpretation strategy.
Full-field measurement techniques (DIC, AE, strain gauge) for characterizing deformation, damage initiation, and fracture in composite and structural materials under complex loading conditions.
Numerical methods including Smoothing Element Analysis (SEA) and finite element-based approaches for strain field regularization, gradient evaluation, and damage localization in heterogeneous materials.
Extending multi-instrument monitoring frameworks to complex loading states, fatigue damage, and environmental conditions in aerospace-grade composites.
Integrating machine learning with DIC and AE data streams for automated, real-time damage classification and remaining useful life prediction.
Bridging full-field macro-scale measurements with micro-scale fracture events using CT scanning and correlative microscopy techniques.
Developing physics-informed digital twin models that assimilate experimental sensor data to predict damage evolution in structural components.