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.
| Originalsprache | Englisch |
|---|---|
| Titel | Proceedings of the 35th European and 10th International Conference on Acoustic Emission Testing EWGAE 35 & ICAE 10 |
| Herausgeber*innen | Roman Šturm, Tomaž Kek |
| Seitenumfang | 9 |
| Band | 28 |
| Publikationsstatus | Veröffentlicht - 2023 |
Publikationsreihe
| Name | e-Journal of Nondestructive Testing (eJNDT) 1435-4934 |
|---|
Wissenschaftszweige
- 203 Maschinenbau
- 203003 Bruchmechanik
- 203007 Festigkeitslehre
- 203012 Luftfahrttechnik
- 203015 Mechatronik
- 203022 Technische Mechanik
- 203034 Kontinuumsmechanik
- 205016 Werkstoffprüfung
- 201117 Leichtbau
- 203002 Betriebsfestigkeit
- 203004 Fahrzeugtechnik
- 203011 Leichtbau
- 205015 Verbundwerkstoffe
- 211905 Bionik
JKU-Schwerpunkte
- Sustainable Development: Responsible Technologies and Management
Projekte
- 1 Abgeschlossen
-
Artificial Intelligence-based corrosion sensing and prediction for aircraft applications
Erlinger, T. (Forscher*in), Schagerl, M. (Forscher*in) & Kralovec-Rödhammer, C. (Projektleiter*in)
01.10.2020 → 30.09.2023
Projekt: Geförderte Forschung › FFG - Österreichische Forschungsförderungsgesellschaft
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