Zitieren Sie bitte immer diesen URN: urn:nbn:de:kobv:b43-651747
Simulation-assisted multimodal deep learning (Sim-MDL) fusion models for the evaluation of thermal barrier coatings using infrared thermography and Terahertz imaging
- Thermal Barrier Coatings (TBCs) are critical for high-temperature applications, such as gas turbines and aerospace engines, protecting metallic substrates from extreme thermal stress and degradation. Accurate evaluation of TBCs is essential to improve operational efficiency, optimize predictive maintenance strategies, and extend component life. Conventional non-destructive evaluation (NDE) techniques such as infrared thermography (IRT) and terahertz (THz) imaging have been widely used for TBC inspection with limitations when used independently, including sensitivity to surface conditions, limited penetration depth mainly in multi-layer coatings. This study proposes a novel framework called simulation-assisted multimodal deep learning (Sim-MDL) that combines IRT and THz data for a comprehensive evaluation of TBCs. To generalize the study to varying thermophysical properties of TBCs, the study uses simulation-generated data along with experimental data for training deep learning models.Thermal Barrier Coatings (TBCs) are critical for high-temperature applications, such as gas turbines and aerospace engines, protecting metallic substrates from extreme thermal stress and degradation. Accurate evaluation of TBCs is essential to improve operational efficiency, optimize predictive maintenance strategies, and extend component life. Conventional non-destructive evaluation (NDE) techniques such as infrared thermography (IRT) and terahertz (THz) imaging have been widely used for TBC inspection with limitations when used independently, including sensitivity to surface conditions, limited penetration depth mainly in multi-layer coatings. This study proposes a novel framework called simulation-assisted multimodal deep learning (Sim-MDL) that combines IRT and THz data for a comprehensive evaluation of TBCs. To generalize the study to varying thermophysical properties of TBCs, the study uses simulation-generated data along with experimental data for training deep learning models. Two deep learning frameworks based on a 1D convolutional neural networks (CNN) and a long short-term memory (LSTM) with attention were developed for the multimodal feature fusion. The IR-THz fused frameworks enable simultaneous prediction of key TBC topcoat properties including thermal conductivity, heat capacity, topcoat thickness and refractive index. Experiments were conducted on four newly coated samples topcoat thicknesses ranging from 24 to 120 μm. An attention-based LSTM model trained on both simulation and experimental data shows high prediction accuracy with MAPE values ranging from 2.06% to 4.43% for thermal conductivity, 2.05% to 3.57% for heat capacity, 11.53% to 1.75% for topcoat thickness, and 0.27% to 1.05% for refractive index, respectively, for the topcoat layers of four samples. The proposed Sim-MDL framework outperformed single-modality and conventional parameter estimation methods in accuracy and robustness, highlighting the potential of multimodal data for automated analysis of TBC in industrial settings.…


| Autor*innen: | Sruthi Krishna Kunji Purayil, S. Aroliveetil, A. Chaturvedi, K. Balasubramaniam |
|---|---|
| Dokumenttyp: | Zeitschriftenartikel |
| Veröffentlichungsform: | Verlagsliteratur |
| Sprache: | Englisch |
| Titel des übergeordneten Werkes (Englisch): | Scientific Reports |
| Jahr der Erstveröffentlichung: | 2025 |
| Organisationseinheit der BAM: | 8 Zerstörungsfreie Prüfung |
| 8 Zerstörungsfreie Prüfung / 8.3 Thermografische Verfahren | |
| Veröffentlichende Institution: | Bundesanstalt für Materialforschung und -prüfung (BAM) |
| Verlag: | Springer Science and Business Media LLC |
| Jahrgang/Band: | 16 |
| Aufsatznummer: | 1540 |
| Erste Seite: | 1 |
| Letzte Seite: | 33 |
| DDC-Klassifikation: | Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurbau |
| Freie Schlagwörter: | Deep Learning; Infrared Thermography; Multimodal fusion; Non-destructive Evaluation (NDE); Terahertz; Thermal Barrier Coatings (TBC) |
| Themenfelder/Aktivitätsfelder der BAM: | Infrastruktur |
| Infrastruktur / Leichtbau | |
| DOI: | 10.1038/s41598-025-31783-8 |
| URN: | urn:nbn:de:kobv:b43-651747 |
| ISSN: | 2045-2322 |
| Verfügbarkeit des Dokuments: | Datei für die Öffentlichkeit verfügbar ("Open Access") |
| Lizenz (Deutsch): | Creative Commons - CC BY - Namensnennung 4.0 International |
| Datum der Freischaltung: | 18.12.2025 |
| Referierte Publikation: | Ja |
| Datum der Eintragung als referierte Publikation: | 18.12.2025 |
| Schriftenreihen ohne Nummerierung: | Wissenschaftliche Artikel der BAM |


