Abstract
Information 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.
Highlights
Decades of research have produced guidelines for estimating ideal service life for aircraft to limit safety risks and monetary losses due to catastrophic fatigue failure
It was shown that information entropy derived from the Acoustic emission (AE) counts mirrored the evolution of counts throughout the tests [35], and the cumulative information entropy from counts may be constant at failure [36]
Because instantaneous entropy is independent between waveforms, the total disorder due to fatigue damage is assumed to be measured by cumulative instantaneous entropy from the AE signals
Summary
Decades of research have produced guidelines for estimating ideal service life for aircraft to limit safety risks and monetary losses due to catastrophic fatigue failure. It was shown that information entropy derived from the AE counts mirrored the evolution of counts throughout the tests [35], and the cumulative information entropy from counts may be constant at failure [36] For all of these previous studies that estimated information entropy from AE signals, the information entropy was calculated based on AE feature distributions formed from numerous AE signals. Instead of analyzing features from several AE hits at a time, one can estimate the information entropy of each individual AE waveform from the signals’ voltage distributions This technique theoretically utilizes more information carried in an AE signal compared to summary statistics like counts, amplitude, or average frequency and is expected to be a more representative measure of fatigue damage. AE counts and AE energy, two traditional AE signal features, in addition to the three methods of quantifying information entropy from AE signals, are correlated to modulus degradation throughout crack initiation and growth.
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