A Unified Mixed Deep Neural Network for Fatigue Damage Detection in Components with Different Stress Concentrations

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.

A random forest‐based fatigue damage classification framework using global loading and displacement information

“…From a data analysis perspective, the current study can be expanded by employing more complex machine learning algorithms such as deep learning. Deep learning can open new opportunities by enabling knowledge transfer 38 or knowledge fusion 39 where the information from this study can be used for other specimen geometries or materials. Since the broader application of the proposed work across different specimen geometries and materials is not yet studied, further analysis can provide an important tool to structural engineers and reduce data requirement for new materials or manufacturing processes.…”