Featured Application: The work presented in this paper outlines three methods to analyze individual acoustic emission waves emitted within a cyclically-loaded metallic structure. Upon further development, one potential application of the proposed analysis methods would be to aid in estimating the remaining useful life of aircraft structures.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.
A parametric approach to estimating the acoustic entropy detected over the course of fatigue damage is presented. Information entropy and relative entropy is estimated through a parametric approach where trial probability density functions (PDFs) are fitted to each individual received acoustic signal as the material degrades over the cycles of loading. The PDF that produces the maximum cumulative entropy is selected to model the signals. This selection criterion is due to the fact that the PDF with higher cumulative entropy results in less bias during the selection process. The evolution trends of both information entropy and relative entropy show the stages of fatigue damage observed in the fatigue indicators such as change in hardness. The acoustic entropy has an advantage over the conventional indices of damage as it can be employed directly in the online sensor based structural health monitoring schemes as a diagnosis feature.
In this paper, AE signals collected during fatigue crack-growth of aluminum and titanium alloys (Al7075-T6 and Ti-6Al-4V) were analyzed and compared. Both the aluminum and titanium alloys used in this study are prevalent materials in aerospace structures, which prompted this current investigation. The effect of different loading conditions and loading frequencies on a proposed AE-based crack-growth model were studied. The results suggest that the linear model used to relate AE and crack growth is independent of the loading condition and loading frequency. Also, the model initially developed for the aluminum alloy proves to hold true for the titanium alloy while, as expected, the model parameters are material dependent. The model parameters and their distributions were estimated using a Bayesian regression technique. The proposed model was developed and validated based on post processing and Bayesian analysis of experimental data.
Abstract. In this paper, we propose the information entropy of acoustic emission (AE) signatures as an index for assessment of degradation in materials. This is a new method for health monitoring of materials and structures that relies on the estimation of the information entropy from the AE signals detected, for example from cyclic fatigue loading of the materials. Different ways of calculation of the information entropy from AE signals are explored and discussed in this paper. As an example of the materials degradation, this study investigates the behavior of maximum entropy ( ) at the time of the materials' failure using a series of fatigue cracking tests on titanium alloy, a material commonly used in certain aircraft structures. This paper describes the fatigue tests in which AE waves and features were measured and discusses the various approaches to estimate the information entropy corresponding to the detected AE. The results show that fatigue failure occurs at the same maximum entropy value regardless of the trajectory of the fatigue crack growth and loading conditions that caused the fatigue.
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