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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