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- W4317933327 abstract "The article presents a mixed deep neural network (DNN) approach for detecting micron-scale fatigue damage in high-strength polycrystalline aluminum alloys. Fatigue testing is conducted using a custom-designed apparatus integrated with a confocal microscope and a moving stage to accurately pinpoint the instance of micron-scale crack emergence. The specimens are monitored throughout the duration of the experiment using a pair of high-frequency ultrasonic transducers. The mixed DNN is trained with ultrasonic time-series data that are obtained from two sets of specimens categorized by different stress concentration factors. To understand the effects of mixing the data from both types of specimens, a parametric analysis is performed by varying the amount of training data from each specimen to develop a series of mixed DNNs. The mixed DNN, when tested on unseen data from both specimens, exhibits an accuracy of over 95%. This article, therefore, demonstrates a successful alternative to customized DNNs for new types, geometries, or stress concentration factors in the materials under consideration." @default.
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- W4317933327 date "2023-01-25" @default.
- W4317933327 modified "2023-10-16" @default.
- W4317933327 title "A Unified Mixed Deep Neural Network for Fatigue Damage Detection in Components with Different Stress Concentrations" @default.
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- W4317933327 doi "https://doi.org/10.3390/app13031542" @default.
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