A novel experimental mechanics technique using Scanning Electron Microscopy (SEM) in conjunction with Acoustic Emission (AE) monitoring is discussed to investigate microstructure-sensitive mechanical behavior and damage of metals and to validate AE related information. Validation for the use of AE method was obtained by using aluminum alloy sharp notch specimens with different geometries tested inside the microscope and compared to results obtained outside the microscope, as well as to previously published data on similar investigations at the laboratory specimen scale. Additionally, load data were correlated with both AE information and microscopic observations of microcracks around grain boundaries as well as secondary cracks, voids, and slip bands. The reported AE results are in excellent agreement with similar findings at the mesoscale, while they are further correlated with in situ and post mortem observations of microstructural damage processes.

Utilizing inverse uncertainty quantification techniques, structural health monitoring (SHM) can be integrated with damage progression models to form a probabilistic prediction of a structure's remaining useful life (RUL). However, damage evolution in realistic structures is physically complex. Accurately representing this behavior requires high-fidelity models which are typically computationally prohibitive. In this paper, high-fidelity fatigue crack growth simulation times are reduced by three orders of magnitude using a model based on a set of surrogate models trained via three-dimensional finite element analysis. The developed crack growth modeling approach is experimentally validated using SHM-based damage diagnosis data. A probabilistic prediction of RUL is formed for a metallic, single-edge notch tension specimen with a fatigue crack growing under mixed-mode conditions.

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The concept of utilizing ferromagnetic shape memory alloys as embedded sensory particles in aluminum alloys for damage detection is discussed. When embedded in a material, a shape memory particle can undergo an acoustically detectable solid-state phase transformation when the local strain reaches a critical value. The emitted acoustic signal can be used for real-time damage detection. To study the transition behavior of the sensory particle inside a metal matrix under load, a simulation approach based on a coupled atomistic-continuum model is used. The simulation results indicate a strong dependence of the particle's pseudoelastic response on its crystallographic orientation with respect to the loading direction. These results serve as a basis for understanding the efficacy and variability in the sensory particle transformation to detect damage processes.

Large-area monitoring and accurate damage quantification are two primary goals of ultrasonic, guided wave-based structural health monitoring (SHM). Reverse-time migration (RTM) is an effective damage imaging technique for both metallic and composite plates. In geophysics, incorporating least-squares inversion into migration can generate images with higher resolution and suppressed artifacts in comparison with conventional RTM. Development of a least-squares reverse time migration (LSRTM) technique is promising for SHM since it could expand the imaging area for a given sensor array while maintaining a relatively high resolution. An LSRTM technique is introduced in this research for damage imaging in an isotropic plate using A 0 mode Lamb waves. A finite difference algorithm based on the Mindlin plate theory was used to simulate the flexural wave propagation. To form the theoretical foundations for guided wavebased LSRTM, a forward modeling operator and its adjoint are defined. The damage images from both numerical simulations and experiments show that LSRTM can enhance imaging resolution and reduce artifacts.