Session: 01-13-01: Congress-Wide Symposium on NDE & SHM: Computational Nondestructive Evaluation and Structural Health Monitoring Count

Paper Number: 90231

90231 - Guided Wave Damage Imaging of Composite Laminates With Least-Squares Reverse-Time Migration (LSRTM) 

A method for adapting least squares reverse time migration (LSRTM) for ultrasonic guided wave imaging of composite laminates is proposed in this paper. As composites become more widely used in fields such as the aerospace industry, there is a greater dependence for high resolution images for structural health monitoring (SHM) and nondestructive evaluation (NDE). Delamination is a common problem in composite laminates, which has led to the apprehension in the use of composite materials for load-bearing structures. There is a need to further improve imaging methods to better utilize data and the ever-growing computational power.

Originally, reverse time migration (RTM) was developed for seismology but has since been applied to SHM and NDE. Although the solver-based refocusing in RTM captures damage with a wide range of damage-scattering effects, the resulted images do not fully define the damage regions due to the limited data acquisition aperture, sensor density, limited frequencies/wavelengths, and the incompleteness of adjoint reconstruction. Image artifacts are sometimes difficult to distinguish from the damage itself, so damage geometries can become distorted, and the resolution is hindered. To reduce the amount of artifacts that can distort images, a scattering amplitude model was created to accurately reconstruct the damage. We have previously developed an LSRTM algorithm for metallic plates.        

LSRTM begins by using a forward wave modeling process. Derivations for the forward modeling operator on an isotropic plate have been shown in previous reports. Both the forward modeling operator and an adjoint operator for the anisotropic material have been derived and implemented for ultrasonic guided waves. A misfit function was determined as a function of the observed data and the forward modeling operator. Then the misfit function was minimized to optimize the models (i.e., scattering amplitude) using a conjugate gradient method. Due to advances in computational power and numerical simulation methods, LSRTM has become more probable for current applications.

In this study, numerical simulations using the proposed LSRTM method were conducted on composite laminate plates using A₀ Lamb waves. Received signals were acquired using a circular array of sensors, where some of these transducers acted also as actuators to propagate the waves. Numerical case studies were conducted using the Born Reflectivity for damage modeling with 1) multiple square damage sites that display small spacing, 2) varying damage dimensions, and 3) changing reflectivity. To represent a delamination case in the composite plate, a fourth case study created the damage region by modifying the stiffness matrix of the damaged region and evaluated the damage imaging performance of LSRTM. Finally, a damage model in the composite laminates was generated using a spatially-varying reflectivity model to simulate complex damage (e.g., those that resulted from impacts). These benchmarking studies have shown that LSRTM is more effective in reducing artifacts, improving resolution, and enhancing damage model reconstruction compared to RTM in composite laminates.

 

.

 

Presenting Author: Jiaze He University of Alabama

Presenting Author Biography: Dr. Jiaze He is an assistant professor in Aerospace Engineering and Mechanics (AEM) at the University of Alabama. His lab develops/implements novel ultrasonic imaging methods for materials/structures/medical imaging. Before he arrived at the UA, he was a postdoctoral research associate in the Theoretical & Computational Seismology Group at Princeton University and a research scholar at NASA LaRC in the Digital Twin program. Currently, he serves as a member of SAE AMS K Non-Destructive Methods and Processes Committee, Nondestructive Evaluation, Diagnosis, and Prognosis Division (NDPD) at ASME, Additive Manufacturing for Maintenance Operations Working Group, and AM Common Data Model Group.

Authors:

Jiaze He University of Alabama
Anthony Schwarberg University of Alabama