| Estimating microscopic defect size from Acoustic Emission Monitoring of Mode I, Mode II and Mixed-Mode I/II delamination propagation in GFRP laminates under quasi-static loads
Brunner, A. J., Gferrer, M., Koss, V., & Pinter, G. (2025). Estimating microscopic defect size from Acoustic Emission Monitoring of Mode I, Mode II and Mixed-Mode I/II delamination propagation in GFRP laminates under quasi-static loads. In Ž. Božić, R. Basan, G. Vukelić, S. Schmauder, L. Banks-Sills, A. Sedmak, & F. Iacoviello (Eds.), Procedia structural integrity: Vol. 68. 24th European conference on fracture 2024 (EFC24) (pp. 1266-1272). https://doi.org/10.1016/j.prostr.2025.06.197 |
| Nano-imaging mass spectrometry by means of high-energy laser desorption ionization (HELDI)
Bleiner, D. (2024). Nano-imaging mass spectrometry by means of high-energy laser desorption ionization (HELDI). Journal of Analytical Atomic Spectrometry, 39(4), 1057-1069. https://doi.org/10.1039/d3ja00399j |
| Classification of progressive wear on a multi-directional pin-on-disc tribometer simulating conditions in human joints-UHMWPE against CoCrMo using acoustic emission and machine learning
Deshpande, P., Wasmer, K., Imwinkelried, T., Heuberger, R., Dreyer, M., Weisse, B., … Pandiyan, V. (2024). Classification of progressive wear on a multi-directional pin-on-disc tribometer simulating conditions in human joints-UHMWPE against CoCrMo using acoustic emission and machine learning. Lubricants, 12(2), 47 (23 pp.). https://doi.org/10.3390/lubricants12020047 |
| AE based crack size estimates from delamination propagation in fiber reinforced thermoset composites
Gfrerrer, M., Koss, V., Pinter, G., & Brunner, A. J. (2024). AE based crack size estimates from delamination propagation in fiber reinforced thermoset composites. In e-Journal of Nondestructive Testing: Vol. 29. 36th conference of the European working group on acoustic emission. EWGAE 2024 (p. (10 pp.). https://doi.org/10.58286/30253 |
| Monitoring of Laser Powder Bed Fusion process by bridging dissimilar process maps using deep learning-based domain adaptation on acoustic emissions
Pandiyan, V., Wróbel, R., Richter, R. A., Leparoux, M., Leinenbach, C., & Shevchik, S. (2024). Monitoring of Laser Powder Bed Fusion process by bridging dissimilar process maps using deep learning-based domain adaptation on acoustic emissions. Additive Manufacturing, 80, 103974 (13 pp.). https://doi.org/10.1016/j.addma.2024.103974 |
| Qualify-as-you-go: sensor fusion of optical and acoustic signatures with contrastive deep learning for multi-material composition monitoring in laser powder bed fusion process
Pandiyan, V., Baganis, A., Axel Richter, R., Wróbel, R., & Leinenbach, C. (2024). Qualify-as-you-go: sensor fusion of optical and acoustic signatures with contrastive deep learning for multi-material composition monitoring in laser powder bed fusion process. Virtual and Physical Prototyping, 19(1), e2356080 (20 pp.). https://doi.org/10.1080/17452759.2024.2356080 |
| Numerical modeling techniques for noise emission of free railway wheels
Taenzer, L., Pachale, U., Van Damme, B., Bergamini, A., & Tallarico, D. (2024). Numerical modeling techniques for noise emission of free railway wheels. Railway Engineering Science, 32, 144-161. https://doi.org/10.1007/s40534-023-00327-z |
| Healing of keyhole porosity by means of defocused laser beam remelting: operando observation by X-ray imaging and acoustic emission-based detection
de Formanoir, C., Hamidi Nasab, M., Schlenger, L., Van Petegem, S., Masinelli, G., Marone, F., … Logé, R. E. (2024). Healing of keyhole porosity by means of defocused laser beam remelting: operando observation by X-ray imaging and acoustic emission-based detection. Additive Manufacturing, 79, 103880 (18 pp.). https://doi.org/10.1016/j.addma.2023.103880 |
| Investigation of background noise affecting AE data acquisition during tensile loading of FRPs
Gfrerrer, M., Wiener, J., Brunner, A. J., & Pinter, G. (2023). Investigation of background noise affecting AE data acquisition during tensile loading of FRPs. In E-Journal of Nondestructive Testing: Vol. 28. 35th European and 10th international conference on acoustic emission testing. EWGAE 35 & ICAE 10 (p. (8 pp.). https://doi.org/10.58286/27631 |
| Optimizing in-situ monitoring for laser powder bed fusion process: deciphering acoustic emission and sensor sensitivity with explainable machine learning
Pandiyan, V., Wróbel, R., Leinenbach, C., & Shevchik, S. (2023). Optimizing in-situ monitoring for laser powder bed fusion process: deciphering acoustic emission and sensor sensitivity with explainable machine learning. Journal of Materials Processing Technology, 321, 118144 (17 pp.). https://doi.org/10.1016/j.jmatprotec.2023.118144 |
| Self-Supervised Bayesian representation learning of acoustic emissions from laser powder bed Fusion process for in-situ monitoring
Pandiyan, V., Wróbel, R., Richter, R. A., Leparoux, M., Leinenbach, C., & Shevchik, S. (2023). Self-Supervised Bayesian representation learning of acoustic emissions from laser powder bed Fusion process for in-situ monitoring. Materials and Design, 235, 112458 (15 pp.). https://doi.org/10.1016/j.matdes.2023.112458 |
| Monitoring of functionally graded material during laser directed energy deposition by acoustic emission and optical emission spectroscopy using artificial intelligence
Wasmer, K., Wüst, M., Cui, D., Masinelli, G., Pandiyan, V., & Shevchik, S. (2023). Monitoring of functionally graded material during laser directed energy deposition by acoustic emission and optical emission spectroscopy using artificial intelligence. Virtual and Physical Prototyping, 18(1), e2189599 (21 pp.). https://doi.org/10.1080/17452759.2023.2189599 |
| Differentiation of materials and laser powder bed fusion processing regimes from airborne acoustic emission combined with machine learning
Drissi-Daoudi, R., Pandiyan, V., Logé, R., Shevchik, S., Masinelli, G., Ghasemi-Tabasi, H., … Wasmer, K. (2022). Differentiation of materials and laser powder bed fusion processing regimes from airborne acoustic emission combined with machine learning. Virtual and Physical Prototyping, 17(2), 181-204. https://doi.org/10.1080/17452759.2022.2028380 |
| Termination criteria for fatigue tests of continuous fiber reinforced polymers
Gfrerrer, M., Wiener, J., Schneider, C., Brunner, A. J., & Pinter, G. (2022). Termination criteria for fatigue tests of continuous fiber reinforced polymers. In A. Vassilopoulos & V. Michaud (Eds.), Vol. 3. Proceedings of the 20th European conference on composite materials. Composite meet sustainability (pp. 502-508). Ecole Polytechnique Fédérale de Lausanne (EPFL). |
| Deep learning-based monitoring of laser powder bed fusion process on variable time-scales using heterogeneous sensing and <em>operando</em> X-ray radiography guidance
Pandiyan, V., Masinelli, G., Claire, N., Le-Quang, T., Hamidi-Nasab, M., de Formanoir, C., … Wasmer, K. (2022). Deep learning-based monitoring of laser powder bed fusion process on variable time-scales using heterogeneous sensing and operando X-ray radiography guidance. Additive Manufacturing, 58, 103007 (15pp.). https://doi.org/10.1016/j.addma.2022.103007 |
| Identification of abnormal tribological regimes using a microphone and semi-supervised machine-learning algorithm
Pandiyan, V., Prost, J., Vorlaufer, G., Varga, M., & Wasmer, K. (2022). Identification of abnormal tribological regimes using a microphone and semi-supervised machine-learning algorithm. Friction, 10(4), 583-596. https://doi.org/10.1007/s40544-021-0518-0 |
| When AM (additive manufacturing) meets AE (acoustic emission) and AI (artificial intelligence)
Wasmer, K., Drissi-Daoudi, R., Masinelli, G., Quang-Le, T., Loge, R., & Shevchik, S. A. (2022). When AM (additive manufacturing) meets AE (acoustic emission) and AI (artificial intelligence). In R. Šturm & T. Kek (Eds.), Vol. 28. 35th European and 10th international conference on acoustic emission testing. EWGAE 35 & ICAE 10 (p. (14 pp.). https://doi.org/10.58286/27606 |
| Structural health and condition monitoring with acoustic emission and guided ultrasonic waves: what about long-term durability of sensors, sensor coupling and measurement chain?
Brunner, A. J. (2021). Structural health and condition monitoring with acoustic emission and guided ultrasonic waves: what about long-term durability of sensors, sensor coupling and measurement chain? Applied Sciences, 11(24), 11648 (20 pp.). https://doi.org/10.3390/app112411648 |
| Acoustic emission and machine learning based classification of wear generated using a pin-on-disc tribometer equipped with a digital holographic microscope
Deshpande, P., Pandiyan, V., Meylan, B., & Wasmer, K. (2021). Acoustic emission and machine learning based classification of wear generated using a pin-on-disc tribometer equipped with a digital holographic microscope. Wear, 476, 203622 (12 pp.). https://doi.org/10.1016/j.wear.2021.203622 |
| Artificial intelligence for monitoring and control of metal additive manufacturing
Masinelli, G., Shevchik, S. A., Pandiyan, V., Quang-Le, T., & Wasmer, K. (2021). Artificial intelligence for monitoring and control of metal additive manufacturing. In M. Meboldt & C. Klahn (Eds.), Industrializing additive manufacturing. Proceedings of AMPA2020 (pp. 205-220). https://doi.org/10.1007/978-3-030-54334-1_15 |
