A novel cross-validated nondestructive evaluation framework for damage detection using acoustic emission

Developing Structural Health Monitoring (SHM) techniques for monitoring and evaluation in civil, mechanical and aerospace applications has a direct impact on public safety, primarily because it is related to reduced downtime and life extension of critical aging components and structures. Such trends are further fueled by the observed shift in modern inspection from "time-based" to "condition-based" maintenance approaches, which promise targeted evaluations when and exactly where they are needed. In this context, the objective of this dissertation is to develop a novel cross-validated framework of using acoustics-based methods for non-destructive testing & evaluation (NDT&E) with the ultimate goal to improve infrastructure condition assessment related primarily to the aerospace industry. This framework is called cross-validated as the primary NDT method of interest, the Acoustic Emission (AE) method, is used in conjunction with several other NDT methods including Guided Ultrasonic Waves (GUW), Digital Image Correlation (DIC) and Infrared Thermography (IRT). The proposed work is built therefore upon the idea of implementing a multimodal NDE approach including both novel hardware integration and data processing techniques that can mitigate existing challenges in reliably using AE in SHM applications. The advantage of designing reliable damage detectors is realized by integrating acoustic features with heterogeneous features that can provide complementary information on the initiation and development of damage. Several demonstrations in static and dynamic conditions of the proposed framework ranging from fundamental plasticity investigations to applied structural analysis are described to demonstrate the efficacy of the novel approach.Ph.D., Mechanical Engineering and Mechanics -- Drexel University, 201

Acoustic emission signal processing framework to identify fracture in aluminum alloys

Acoustic emission (AE) is a common nondestructive evaluation tool that has been used to monitor fracture in materials and structures. The direct connection between AE events and their source, however, is difficult because of material, geometry and sensor contributions to the recorded signals. Moreover, the recorded AE activity is affected by several noise sources which further complicate the identification process. This article uses a combination of in situ experiments inside the scanning electron microscope to observe fracture in an aluminum alloy at the time and scale it occurs and a novel AE signal processing framework to identify characteristics that correlate with fracture events. Specifically, a signal processing method is designed to cluster AE activity based on the selection of a subset of features objectively identified by examining their correlation and variance. The identified clusters are then compared to both mechanical and in situ observed microstructural damage. Results from a set of nanoindentation tests as well as a carefully designed computational model are also presented to validate the conclusions drawn from signal processing

Energy dissipation via acoustic emission in ductile crack initiation

The final publication is available at Springer via http://dx.doi.org/10.1007/s10704-016-0096-8.This article presents a modeling approach to estimate the energy release due to ductile crack initiation in conjunction to the energy dissipation associated with the formation and propagation of transient stress waves typically referred to as acoustic emission. To achieve this goal, a ductile fracture problem is investigated computationally using the finite element method based on a compact tension geometry under Mode I loading conditions. To quantify the energy dissipation associated with acoustic emission, a crack increment is produced given a pre-determined notch size in a 3D cohesive-based extended finite element model. The computational modeling methodology consists of defining a damage initiation state from static simulations and linking such state to a dynamic formulation used to evaluate wave propagation and related energy redistribution effects. The model relies on a custom traction separation law constructed using full field deformation measurements obtained experimentally using the digital image correlation method. The amount of energy release due to the investigated first crack increment is evaluated through three different approaches both for verification purposes and to produce an estimate of the portion of the energy that radiates away from the crack source in the form of transient waves. The results presented herein propose an upper bound for the energy dissipation associated to acoustic emission, which could assist the interpretation and implementation of relevant nondestructive evaluation methods and the further enrichment of the understanding of effects associated with fracture

Part Qualification Methodology for Composite Aircraft Components Using Acoustic Emission Monitoring

The research presented in this article aims to demonstrate how acoustic emission (AE) monitoring can be implemented in an industrial setting to assist with part qualification, as mandated by related industry standards. The combined structural and nondestructive evaluation method presented departs from the traditional pass/fail criteria used for part qualification, and contributes toward a multi-dimensional assessment by taking advantage of AE data recorded during structural testing. To demonstrate the application of this method, 16 composite fixed-wing-aircraft spars were tested using a structural loading sequence designed around a manufacturer-specified design limit load (DLL). Increasing mechanical loads, expressed as a function of DLL were applied in a load-unload-reload pattern so that AE activity trends could be evaluated. In particular, the widely used Felicity ratio (FR) was calculated in conjunction with specific AE data post-processing, which allowed for spar test classification in terms of apparent damage behavior. To support such analysis and to identify damage critical regions in the spars, AE activity location analysis was also employed. Furthermore, recorded AE data were used to perform statistical analysis to demonstrate how AE datasets collected during part qualification could augment testing conclusions by providing additional information as compared to traditional strength testing frequently employed e.g., in the aerospace industry. In this context, AE data post-processing is presented in conjunction with ultimate strength information, and it is generally shown that the incorporation of AE monitoring is justified in such critical part qualification testing procedures

The technique of digital image correlation to identify defects in glass structures

SUMMARY The article describes an experiment using the technique of digital image correlation (DIC) to identify and analyse defects in glass structures. The investigation involved three sets of glass structures, that is, float glass sheets, with or without any defects and subjected to bearing tests. The DIC technique perfectly identified not only the position and size of the defect but also the magnitude of deformations around the defect itself. Low stress values also indicate that strains concentrate around the defects. This enables the DIC method to be used to investigate the quality of glass elements, to detect the presence of defects and to monitor the most critical structural elements. The results obtained with the DIC technique were confirmed by comparing them with data gathered from the analysis of the fracture surface. Copyright © 2014 John Wiley & Sons, Ltd