Abstract
In the aircraft industry, where highly optimized aluminum structures and most stringent safety requirements meet, corrosion of aluminum is an important issue to be controlled. To this end, several structural health monitoring (SHM) methods have already been demonstrated, including the acoustic emission (AE) method, which has shown potential for corrosion monitoring. Typically, immersion-like setups are used for demonstration. However, recent results at the authors’ research group also show the potential of the AE method to monitor atmospheric corrosion of aluminum aircraft structures. This contribution presents a SHM concept for the identification, i.e., detection, localization, quantification, and typification, of corrosion of thin-walled aluminum structures by AE. The proposed monitoring concept combines time and frequency domain features of corrosion triggered AE signals and AE source localization-based imaging with a-priori knowledge of structural configuration and loading by the utilization of machine learning methods to quantify and typify corrosion. Successful detection of atmospheric corrosion of aluminum by AE is briefly presented. Furthermore, the concept is theoretically discussed for quantification and typification of corrosion forms typical for atmospheric corrosion conditions.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 35th European and 10th International Conference on Acoustic Emission Testing EWGAE 35 & ICAE 10 |
| Editors | Roman Šturm, Tomaž Kek |
| Number of pages | 9 |
| Volume | 28 |
| Publication status | Published - 2023 |
Publication series
| Name | e-Journal of Nondestructive Testing (eJNDT) 1435-4934 |
|---|
Fields of science
- 203 Mechanical Engineering
- 203003 Fracture mechanics
- 203007 Strength of materials
- 203012 Aerospace engineering
- 203015 Mechatronics
- 203022 Technical mechanics
- 203034 Continuum mechanics
- 205016 Materials testing
- 201117 Lightweight design
- 203002 Endurance strength
- 203004 Automotive technology
- 203011 Lightweight design
- 205015 Composites
- 211905 Bionics
JKU Focus areas
- Sustainable Development: Responsible Technologies and Management
Projects
- 1 Finished
-
Artificial Intelligence-based corrosion sensing and prediction for aircraft applications
Erlinger, T. (Researcher), Schagerl, M. (Researcher) & Kralovec-Rödhammer, C. (PI)
01.10.2020 → 30.09.2023
Project: Funded research › FFG - Austrian Research Promotion Agency
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