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.

Keywords

• fatigue damage detection
• aluminum alloys
• ultrasonic time-series data
• deep neural network
• transfer learning
• structural health monitoring