Fatigue Crack Closure Analysis Using Digital Image Correlation

Fatigue crack closure during crack growth testing is analyzed in order to evaluate the critieria of ASTM Standard E647 for measurement of fatigue crack growth rates. Of specific concern is remote closure, which occurs away from the crack tip and is a product of the load history during crack-driving-force-reduction fatigue crack growth testing. Crack closure behavior is characterized using relative displacements determined from a series of high-magnification digital images acquired as the crack is loaded. Changes in the relative displacements of features on opposite sides of the crack are used to generate crack closure data as a function of crack wake position. For the results presented in this paper, remote closure did not affect fatigue crack growth rate measurements when ASTM Standard E647 was strictly followed and only became a problem when testing parameters (e.g., load shed rate, initial crack driving force, etc.) greatly exceeded the guidelines of the accepted standard

A Computationally-Efficient Probabilistic Approach to Model-Based Damage Diagnosis

This work presents a computationally-efficient, probabilistic approach to model-based damage diagnosis. Given measurement data, probability distributions of unknown damage parameters are estimated using Bayesian inference and Markov chain Monte Carlo (MCMC) sampling. Substantial computational speedup is obtained by replacing a three-dimensional finite element (FE) model with an efficient surrogate model. While the formulation is general for arbitrary component geometry, damage type, and sensor data, it is applied to the problem of strain-based crack characterization and experimentally validated using full-field strain data from digital image correlation (DIC). Access to full-field DIC data facilitates the study of the effectiveness of strain-based diagnosis as the distance between the location of damage and strain measurements is varied. The ability of the framework to accurately estimate the crack parameters and effectively capture the uncertainty due to measurement proximity and experimental error is demonstrated. Furthermore, surrogate modeling is shown to enable diagnoses on the order of seconds and minutes rather than several days required with the FE model

Probabilistic Prognosis of Non-Planar Fatigue Crack Growth

Quantifying the uncertainty in model parameters for the purpose of damage prognosis can be accomplished utilizing Bayesian inference and damage diagnosis data from sources such as non-destructive evaluation or structural health monitoring. The number of samples required to solve the Bayesian inverse problem through common sampling techniques (e.g., Markov chain Monte Carlo) renders high-fidelity finite element-based damage growth models unusable due to prohibitive computation times. However, these types of models are often the only option when attempting to model complex damage growth in real-world structures. Here, a recently developed high-fidelity crack growth model is used which, when compared to finite element-based modeling, has demonstrated reductions in computation times of three orders of magnitude through the use of surrogate models and machine learning. The model is flexible in that only the expensive computation of the crack driving forces is replaced by the surrogate models, leaving the remaining parameters accessible for uncertainty quantification. A probabilistic prognosis framework incorporating this model is developed and demonstrated for non-planar crack growth in a modified, edge-notched, aluminum tensile specimen. Predictions of remaining useful life are made over time for five updates of the damage diagnosis data, and prognostic metrics are utilized to evaluate the performance of the prognostic framework. Challenges specific to the probabilistic prognosis of non-planar fatigue crack growth are highlighted and discussed in the context of the experimental results

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

Utilizing inverse uncertainty quantification techniques, structural health monitoring 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 the present work, a high-fidelity finite element model is represented by a surrogate model, reducing computation times. The new approach is used with damage diagnosis data to form a probabilistic prediction of remaining useful life for a test specimen under mixed-mode conditions

Mitigation of Crack Damage in Metallic Materials

A system designed to mitigate or heal crack damage in metallic materials has been developed where the protected material or component is coated with a low-melting temperature film. After a crack is formed, the material is heated, melting the film which then infiltrates the crack opening through capillary action. Upon solidification, the healing material inhibits further crack damage in two ways. While the crack healing material is intact, it acts like an adhesive that bonds or bridges the crack faces together. After fatigue loading damages, the healing material in the crack mouth inhibits further crack growth by creating artificially-high crack closure levels. Mechanical test data show that this method sucessfully arrests or retards crack growth in laboratory specimens