A. James Clark School of Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/1654
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item Damage Assessment Using Information Entropy of Individual Acoustic Emission Waveforms during Cyclic Fatigue Loading(MDPI, 2017-05-30) Sauerbrunn, Christine M.; Kahirdeh, Ali; Yun, Huisung; Modarres, MohammadInformation entropy measured from acoustic emission (AE) waveforms is shown to be an indicator of fatigue damage in a high-strength aluminum alloy. Three methods of measuring the AE information entropy, regarded as a direct measure of microstructural disorder, are proposed and compared with traditional damage-related AE features. Several tension–tension fatigue experiments were performed with dogbone samples of aluminum 7075-T6, a commonly used material in aerospace structures. Unlike previous studies in which fatigue damage is measured based on visible crack growth, this work investigated fatigue damage both prior to and after crack initiation through the use of instantaneous elastic modulus degradation. Results show that one of the three entropy measurement methods appears to better assess the damage than the traditional AE features, whereas the other two entropies have unique trends that can differentiate between small and large cracks.Item Measures of Entropy to Characterize Fatigue Damage in Metallic Materials(MDPI, 2019-08-17) Yu, Huisung; Modarres, MohammadThis paper presents the entropic damage indicators for metallic material fatigue processes obtained from three associated energy dissipation sources. Since its inception, reliability engineering has employed statistical and probabilistic models to assess the reliability and integrity of components and systems. To supplement the traditional techniques, an empirically-based approach, called physics of failure (PoF), has recently become popular. The prerequisite for a PoF analysis is an understanding of the mechanics of the failure process. Entropy, the measure of disorder and uncertainty, introduced from the second law of thermodynamics, has emerged as a fundamental and promising metric to characterize all mechanistic degradation phenomena and their interactions. Entropy has already been used as a fundamental and scale-independent metric to predict damage and failure. In this paper, three entropic-based metrics are examined and demonstrated for application to fatigue damage. We collected experimental data on energy dissipations associated with fatigue damage, in the forms of mechanical, thermal, and acoustic emission (AE) energies, and estimated and correlated the corresponding entropy generations with the observed fatigue damages in metallic materials. Three entropic theorems—thermodynamics, information, and statistical mechanics—support approaches used to estimate the entropic-based fatigue damage. Classical thermodynamic entropy provided a reasonably constant level of entropic endurance to fatigue failure. Jeffreys divergence in statistical mechanics and AE information entropy also correlated well with fatigue damage. Finally, an extension of the relationship between thermodynamic entropy and Jeffreys divergence from molecular-scale to macro-scale applications in fatigue failure resulted in an empirically-based pseudo-Boltzmann constant equivalent to the Boltzmann constant.Item Framework for Small Fatigue Crack Propagation and Detection Joint Modeling Using Gaussian Process Regression(2017) Smith, Reuel Calvin; Modarres, Mohammad; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Engineers have witnessed much advancement in the study of fatigue crack detection and propagation (CPD) modeling. More recently the use of certain damage precursors such as acoustic emission (AE) signals to assess the integrity of structures has been proposed for application to prognosis and health management of structures. However, due to uncertainties associated with small crack detection of damage precursors as well as crack size measurement errors of the detection technology used, applications of prognosis and health management assessments have been limited. This dissertation defines a new methodology for the assessment of CPD parameters and the minimization of uncertainties including detection and sizing errors associated with a series of known CPD models that use AE as the precursor to fatigue cracking. The first step of the procedure is defining the separate crack propagation and crack detection models that are to be used for the testing of a joint-CPD model. The two propagation models for this study are based on a Gaussian process regression model that correlates crack shaping factors (CSFs) to the propagation of the crack. One of these propagation models includes a particle filtering technique that includes several AE data. The testing of this joint-CPD model is facilitated by the Bayesian inference of the CPD likelihood where the posterior models are extracted and tested for correctness. The CSFs, the CPD data, and the AE signal data used for testing of this methodology come from a series of fatigue tests done on dog-bone Al 7075-T6 specimens. The data is first corrected for measurement error that is present based on the initial crack measurements. Then the data is used to generate the prior CPD models that is needed for the Bayesian inference procedure. With the resulting posterior CPD models, a correlation procedure that estimates the CPD model parameters of validation specimens based on the relationship that exists between the CSFs and the CPD model parameters is performed as well as a model error correction procedure. The result of this correlation provides reasonable estimates for the remaining useful life of a given validation specimen.