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Coupling In Situ Microstructure Observation with Machine Learning Algorithms for Damage Diagnostics and Prognostics



Acoustic Emission (AE) obtained from a novel experimental setup which involves recording of AE data from inside a Scanning Electron Microscope (SEM), is leveraged in this article in a signal processing approach that combines data reduction, classification and outlier analysis to identify data trends and relate them to detection of damage and predictions of remaining useful life at the specimen level. This approach offers the possibility to increase the reliability of NDE methods used in data-driven characterization of material behavior. To demonstrate the benefits of this approach, two types of Aluminum alloys, 2024-T3 and 7075-T651 were mechanically tested inside a SEM while simultaneously recording AE. Data obtained by this approach was then used to develop and validate a data reduction and classification algorithm with the goal to identify sensing information that appears to be the most sensitive to the activation of microstructural-level damage mechanisms. By combining this approach with an outlier analysis it is demonstrated that it is possible to eliminate noise and provide data trends that are found to follow the material degradation process in both monotonic and cyclic conditions. The framework presented can be extended to a variety of materials, while the produced data trends may serve as inputs to computational models.


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