| 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, 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 |
| 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 |
| Acoustic emission analysis and synchrotron-based microtomography of glued shear strength samples from spruce wood
Niemz, P., Baensch, F., & Brunner, A. J. (2020). Acoustic emission analysis and synchrotron-based microtomography of glued shear strength samples from spruce wood. Bulletin of the Transilvania University of Braşov. Series II: Forestry • Wood Industry • Agricultural Food Engineering, 13(62)(1), 81-88. https://doi.org/10.31926/but.fwiafe.2020.13.62.1.7 |
| Microscopic damage size in fiber-reinforced polymer-matrix composites: quantification approach via NDT-measurements
Brunner, A. J., Potstada, P., & Sause, M. G. R. (2019). Microscopic damage size in fiber-reinforced polymer-matrix composites: quantification approach via NDT-measurements. In P. M. G. P. Moreira & P. J. S. Tavares (Eds.), Procedia Structural Integrity: Vol. 17. 3rd international conference on structural integrity, ICSI 2019 (pp. 146-153). https://doi.org/10.1016/j.prostr.2019.08.020 |
| Unsupervised pattern recognition of acoustic emission signals of adhesively bonded wood
Clerc, G., Sause, M. G. R., Brunner, A. J., Niemz, P., & Van de Kuilen, J. W. G. (2019). Unsupervised pattern recognition of acoustic emission signals of adhesively bonded wood. In X. Wang, U. H. Sauter, & R. J. Ross (Eds.), General Technical Report: Vol. FPL–GTR–272. Proceedings. 21st international nondestructive testing and evaluation of wood symposium (pp. 619-626). U.S. Department of Agriculture, Forest Service, Forest Products Laboratory. |
| Acoustic emission analysis and synchrotron-based microtomography on glued shear strength samples from spruce solid wood
Niemz, P., Baensch, F., Brunner, A. J., & Gaff, M. (2019). Acoustic emission analysis and synchrotron-based microtomography on glued shear strength samples from spruce solid wood. In X. Wang, U. H. Sauter, & R. J. Ross (Eds.), General Technical Report: Vol. FPL–GTR–272. Proceedings. 21st international nondestructive testing and evaluation of wood symposium (pp. 168-175). U.S. Department of Agriculture, Forest Service, Forest Products Laboratory. |
| 3D reconstruction of cracks propagation in mechanical workpieces analyzing non-stationary acoustic mixtures
Shevchik, S. A., Meylan, B., Violakis, G., & Wasmer, K. (2019). 3D reconstruction of cracks propagation in mechanical workpieces analyzing non-stationary acoustic mixtures. Mechanical Systems and Signal Processing, 119, 55-64. https://doi.org/10.1016/j.ymssp.2018.09.022 |
| Laser welding quality monitoring via graph support vector machine with data adaptive kernel
Shevchik, S. A., Le-Quang, T., Farahani, F. V., Faivre, N., Meylan, B., Zanoli, S., & Wasmer, K. (2019). Laser welding quality monitoring via graph support vector machine with data adaptive kernel. IEEE Access, 7, 93108-93122. https://doi.org/10.1109/ACCESS.2019.2927661 |
| In situ quality monitoring in AM using acoustic emission: a reinforcement learning approach
Wasmer, K., Le-Quang, T., Meylan, B., & Shevchik, S. A. (2019). In situ quality monitoring in AM using acoustic emission: a reinforcement learning approach. Journal of Materials Engineering and Performance, 28(2), 666-672. https://doi.org/10.1007/s11665-018-3690-2 |
| Why is in situ quality control of laser keyhole welding a real challenge?
Le-Quang, T., Shevchik, S. A., Meylan, B., Vakili-Farahani, F., Olbinado, M. P., Rack, A., & Wasmer, K. (2018). Why is in situ quality control of laser keyhole welding a real challenge? In M. Schmidt, F. Vollertsen, & G. Dearden (Eds.), Procedia CIRP: Vol. 74. 10th CIRP conference on photonic technologies [LANE 2018] (pp. 649-653). https://doi.org/10.1016/j.procir.2018.08.055 |
| Acoustic emission for in situ quality monitoring in additive manufacturing using spectral convolutional neural networks
Shevchik, S. A., Kenel, C., Leinenbach, C., & Wasmer, K. (2018). Acoustic emission for in situ quality monitoring in additive manufacturing using spectral convolutional neural networks. Additive Manufacturing, 21, 598-604. https://doi.org/10.1016/j.addma.2017.11.012 |
| <i>In situ</i> and real-time monitoring of powder-bed AM by combining acoustic emission and artificial intelligence
Wasmer, K., Kenel, C., Leinenbach, C., & Shevchik, S. A. (2018). In situ and real-time monitoring of powder-bed AM by combining acoustic emission and artificial intelligence. In M. Mebold & C. Klahn (Eds.), Industrializing additive manufacturing - proceedings of additive manufacturing in products and applications - AMPA2017 (pp. 200-209). https://doi.org/10.1007/978-3-319-66866-6_20 |
| High-speed X-ray imaging for correlating acoustic signals with quality monitoring: a machine learning approach
Wasmer, K. (2018). High-speed X-ray imaging for correlating acoustic signals with quality monitoring: a machine learning approach. In Contributed Papers from Materials Science & Technology 2018 (pp. 165-168). |
| In situ quality monitoring in am using acoustic emission: a machine learning approach
Wasmer, K., Kenel, C., Leinenbach, C., & Shevchik, S. A. (2017). In situ quality monitoring in am using acoustic emission: a machine learning approach (pp. 386-388). Presented at the Materials science and technology (MS&T17). https://doi.org/10.7449/2017/MST_2017_386_388 |
| Automatic detection of scuffing using acoustic emission
Saeidi, F., Shevchik, S. A., & Wasmer, K. (2016). Automatic detection of scuffing using acoustic emission. Tribology International, 94, 112-117. https://doi.org/10.1016/j.triboint.2015.08.021 |
| Damage evolution in wood – pattern recognition based on acoustic emission (AE) frequency spectra
Baensch, F., Sause, M. G. R., Brunner, A. J., & Niemz, P. (2015). Damage evolution in wood – pattern recognition based on acoustic emission (AE) frequency spectra. Holzforschung, 69(3), 357-365. https://doi.org/10.1515/hf-2014-0072 |
| Acoustic emission and synchrotron radiation X-ray tomographic in-situ microscopy of sub-macroscopic damage phenomena in wood
Ritschel, F., Zauner, M., Sanabria, S. J., Brunner, A. J., & Niemz, P. (2013). Acoustic emission and synchrotron radiation X-ray tomographic in-situ microscopy of sub-macroscopic damage phenomena in wood (pp. 434-441). Presented at the 18th international nondestructive testing and evaluation of wood symposium. . |
