William P. Leser

Development and Characterization of Embedded Sensory Particles Using Multi-Scale 3D Digital Image Correlation

A method for detecting fatigue cracks has been explored at NASA Langley Research Center. Microscopic NiTi shape memory alloy (sensory) particles were embedded in a 7050 aluminum alloy matrix to detect the presence of fatigue cracks. Cracks exhibit an elevated stress field near their tip inducing a martensitic phase transformation in nearby sensory particles. Detectable levels of acoustic energy are emitted upon particle phase transformation such that the existence and location of fatigue cracks can be detected. To test this concept, a fatigue crack was grown in a mode-I single-edge notch fatigue crack growth specimen containing sensory particles. As the crack approached the sensory particles, measurements of particle strain, matrix-particle debonding, and phase transformation behavior of the sensory particles were performed. Full-field deformation measurements were performed using a novel multi-scale optical 3D digital image correlation (DIC) system. This information will be used in a finite element-based study to determine optimal sensory material behavior and density.

IWSHM 2015: Probabilistic fatigue damage prognosis using surrogate models trained via three-dimensional finite element analysis

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.

Multi-mode reverse time migration damage imaging using ultrasonic guided waves

HighlightsThe aim of the present work is to develop a multi‐mode imaging technique that will allow for identification of damage size and location using ultrasonic guided waves.The proposed technique combines a reverse‐time migration (RTM) imaging algorithm with a 3D wave propagation simulator using different wave modes.This combination enables the separation of multiple modes using the wavefield filtering techniques, potentially providing more information to damage types.Without the limitation of generating single dominant mode waves, wide frequency ranges are accessible, enabling optimal frequencies for a variety of ultrasonic data acquisition systems. ABSTRACT The sensitivity of Lamb wave modes to a particular defect or instance of damage is dependent on various factors (e.g., the local strain energy density due to that wave mode). As a result, different modes will be more useful than others for damage detection and quantification, dependent on damage type and location. For example, prior work in the field has shown that out‐of‐plane modes may have a higher sensitivity than in‐plane modes to surface defects in plates. The excitability of a certain data acquisition system and the corresponding resolution for damage imaging also varies with frequency. The aim of the present work was to develop a multi‐mode damage imaging technique that enables characterization of damage type and size, general sensitivity to unknown damage types, higher resolution imaging, and detectability regardless of the data acquisition system used. A reverse‐time migration (RTM) imaging algorithm was combined with a numerical simulator—the three‐dimensional (3D) elastodynamic finite integration technique (EFIT)—to provide multi‐mode damage imaging. The approach was applied to two simulated case studies featuring damaged isotropic plates. Sensitivities of damage type to wave mode were investigated by separating the Symbol and Symbol Lamb wave modes obtained from the resultant RTM wavefields. Symbol. No Caption available. Symbol. No Caption available.

IWSHM 2017: damage-scattered wave extraction in an integral stiffened isotropic plate: a baseline-subtraction-free approach

Ultrasonic guided waves enable long-distance inspection of structures for health monitoring purposes. However, this capability is diminished when applied to complex structures where damage-scattered waves are often buried by scattering from various structural components or boundaries in the time–space domain. Here, a baseline-subtraction-free inspection concept based on the Radon transform is proposed to identify and separate these scattered waves from those scattered by damage. The received time–space domain signals can be converted into the Radon domain, in which the scattered signals from structural components are suppressed into relatively small regions such that damage-scattered signals can be identified and extracted. In this study, a piezoelectric wafer and a linear scan via laser Doppler vibrometer were used to excite and acquire the Lamb wave signals in an aluminum plate with multiple stiffeners. Linear and inverse linear Radon transform algorithms were applied to the direct measurements. Currently, this method needs baseline measurements for comparison in the Radon domain, but avoids baseline subtraction. The results demonstrate the effectiveness of the Radon transform as an extraction tool for damage-scattered waves in a stiffened aluminum plate for a damage site in the bay area between two stiffeners and also suggest the possibility of generalizing this technique for application to a wide variety of complex, large-area structures.

Probabilistic Fatigue Damage Prognosis Using a Surrogate Model Trained Via 3D Finite Element Analysis

Utilizing inverse uncertainty quantification techniques, structural health monitoring data can be integrated with damage progression models to form probabilistic predictions of a structure’s remaining useful life. 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 significantly reduced using a surrogate model trained via finite element analysis. The new approach is applied to experimental damage diagnosis data to form a probabilistic prediction of remaining useful life for a test specimen under mixed-mode conditions. doi: 10.12783/SHM2015/299