Assessment of Transfer Learning Capabilities for Fatigue Damage Classification and Detection in Aluminum Specimens with Different Notch Geometries

Abstract

Fatigue damage detection and its classification in metallic materials are persistently challenging the structural health monitoring community. The mechanics of fatigue damage is difficult to analyze and is further complicated because of the presence of notches of different geometries. These notches act as possible crack-nucleation sites resulting in failure mechanisms that are drastically different from one another. Often, sensor-based tools are used to monitor and detect fatigue damage in critical metallic materials such as aluminum alloys. Through deep neural networks (DNNs), such a sensor-based approach can be ubiquitously extended for a variety of geometries as appropriate for different applications. To that end, this paper presents a DNN-based transfer learning framework that can be used to classify and detect fatigue damage across candidate notch geometries. The DNNs are built upon ultrasonic time-series data obtained during fatigue testing of Al7075-T6 specimens with two types of notch geometries, namely, a U-notch and a V-notch. The baseline U-notch DNN is shown to achieve an accuracy of 96.1% while the baseline V-notch DNN has an accuracy of 95.8%. Both baseline DNNs are, thereafter, subjected to a transfer learning process by keeping a certain number of layers frozen and retraining only the remaining layers with a small volume of data obtained from the other notch geometry. When a layer of the baseline U-notch DNN is retrained with just 10% of the total V-notch data, an accuracy above 90% is observed for fatigue damage detection of V-notch specimens. Similar results are also obtained when the baseline V-notch DNN is retrained and interrogated to detect damage for U-notch specimens. These results, in summary, demonstrate the data-thrifty quality of combining the concepts of transfer learning and DNN for fatigue damage detection in different geometries of specimens made of high-performance aluminum alloys.

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.…”

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

“…The individual performance of the baseline DNNs (DNN Test:20K t1 Train:80K t1 and DNN Test:20K t2 Train:80K t2 ) for both K t s is shown in Figure 12a,b [27]. Both networks show a balanced performance for healthy and cracked classes with over 95% sensitivity and specificity.…”

“…In addition to ultrasonic sensors, the specimens are also monitored through a confocal microscope that is focused on the inside surface of the high-stress concentration region where fatigue cracks are likely to originate [34]. Reprinted from [27] with permission under the terms and conditions of the Creative Commons Attribution (CC BY) license.…”

“…The accuracies for each K t serve as a benchmark to compare against all subsequent modifications to the DNNs. The performance of the transductive analysis (by switching testing data between K t s) is shown in Figure 12c,d [27]. Incredibly, the analysis shows a significant loss in performance with an overall accuracy of 60.9% and 56.99%, respectively, for K t1 and K t2 .…”

“…In trying to find a solution to these challenges, if a DNN trained in some other material systems or geometries aids in any way, it would result in huge cost savings. Although there are transfer learning approaches to answer such problems [27], they often result in individual models for each specimen geometry and, therefore, may lead to an intractable number of trained models. While these questions are rather firsts-of-their-kinds in the applied mechanics field, they are not new in other domains, such as natural language processing [28].…”