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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
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
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. 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
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
Investigation of background noise affecting AE data acquisition during tensile loading of FRPs
Gfrerrer, M., Wiener, J., Brunner, A. J., & Pinter, G. (2022). Investigation of background noise affecting AE data acquisition during tensile loading of FRPs. In R. Šturm & T. Kek (Eds.), 35th European and 10th international conference on acoustic emission testing. EWGAE 35 & ICAE 10 (pp. 364-371). University of Ljubljana.
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
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
Acoustic emission and machine learning for in situ monitoring of a gold-copper ore weakening by electric pulse
Meylan, B., Shevchik, S. A., Parvaz, D., Mosaddeghi, A., Simov, V., & Wasmer, K. (2021). Acoustic emission and machine learning for in situ monitoring of a gold-copper ore weakening by electric pulse. Journal of Cleaner Production, 280, 124348 (12 pp.). https://doi.org/10.1016/j.jclepro.2020.124348
Semi-supervised monitoring of laser powder bed fusion process based on acoustic emissions
Pandiyan, V., Drissi-Daoudi, R., Shevchik, S., Masinelli, G., Le-Quang, T., Logé, R., & Wasmer, K. (2021). Semi-supervised monitoring of laser powder bed fusion process based on acoustic emissions. Virtual and Physical Prototyping, 16(4), 481-497. https://doi.org/10.1080/17452759.2021.1966166
Machine learning monitoring for laser osteotomy
Shevchik, S., Nguendon Kenhagho, H., Le-Quang, T., Faivre, N., Meylan, B., Guzman, R., … Wasmer, K. (2021). Machine learning monitoring for laser osteotomy. Journal of Biophotonics, 14(4), e202000352 (11 pp.). https://doi.org/10.1002/jbio.202000352
Monitoring of friction-related failures using diffusion maps of acoustic time series
Shevchik, S. A., Zanoli, S., Saeidi, F., Meylan, B., Flück, G., & Wasmer, K. (2021). Monitoring of friction-related failures using diffusion maps of acoustic time series. Mechanical Systems and Signal Processing, 148, 107172 (14 pp.). https://doi.org/10.1016/j.ymssp.2020.107172