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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
Sensitivity analysis of acoustic emission detection using fiber bragg gratings with different optical fiber diameters
Violakis, G., Le-Quang, T., Shevchik, S. A., & Wasmer, K. (2020). Sensitivity analysis of acoustic emission detection using fiber bragg gratings with different optical fiber diameters. Sensors, 20(22), 6511 (11 pp.). https://doi.org/10.3390/s20226511
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
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
Analysis of time, frequency and time-frequency domain features from acoustic emissions during laser powder-bed fusion process
Pandiyan, V., Drissi-Daoudi, R., Shevchik, S., Masinelli, G., Logé, R., & Wasmer, K. (2020). Analysis of time, frequency and time-frequency domain features from acoustic emissions during laser powder-bed fusion process. In M. Schmidt, F. Vollertsen, & E. Govekar (Eds.), Procedia CIRP: Vol. 94. 11th CIRP conference on photonic technologies [LANE 2020] (pp. 392-397). https://doi.org/10.1016/j.procir.2020.09.152
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
Modelling and monitoring of abrasive finishing processes using artificial intelligence techniques: a review
Pandiyan, V., Shevchik, S., Wasmer, K., Castagne, S., & Tjahjowidodo, T. (2020). Modelling and monitoring of abrasive finishing processes using artificial intelligence techniques: a review. Journal of Manufacturing Processes, 57, 114-135. https://doi.org/10.1016/j.jmapro.2020.06.013
Adaptive laser welding control: a reinforcement learning approach
Masinelli, G., Le-Quang, T., Zanoli, S., Wasmer, K., & Shevchik, S. A. (2020). Adaptive laser welding control: a reinforcement learning approach. IEEE Access, 8, 103803-103814. https://doi.org/10.1109/ACCESS.2020.2998052
Re-solidification dynamics and microstructural analysis of laser welded aluminium
Meylan, B., Le-Quang, T., Olbinado, M. P., Rack, A., Shevchik, S. A., & Wasmer, K. (2020). Re-solidification dynamics and microstructural analysis of laser welded aluminium. International Journal of Materials Research, 111(1), 17-22. https://doi.org/10.3139/146.111838
Supervised deep learning for real-time quality monitoring of laser welding with X-ray radiographic guidance
Shevchik, S., Le-Quang, T., Meylan, B., Vakili Farahani, F., Olbinado, M. P., Rack, A., … Wasmer, K. (2020). Supervised deep learning for real-time quality monitoring of laser welding with X-ray radiographic guidance. Scientific Reports, 10, 3389 (12 pp.). https://doi.org/10.1038/s41598-020-60294-x
Deep learning for<em> in situ </em>and real-time quality monitoring in additive manufacturing using acoustic emission
Shevchik, S. A., Masinelli, G., Kenel, C., Leinenbach, C., & Wasmer, K. (2019). Deep learning for in situ and real-time quality monitoring in additive manufacturing using acoustic emission. IEEE Transactions on Industrial Informatics, 15(9), 5194-5203. https://doi.org/10.1109/TII.2019.2910524
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
Characterization of ablated bone and muscle for long-pulsed laser ablation in dry and wet conditions
Nguendon Kenhagho, H., Shevchik, S., Saeidi, F., Faivre, N., Meylan, B., Rauter, G., … Zam, A. (2019). Characterization of ablated bone and muscle for long-pulsed laser ablation in dry and wet conditions. Materials, 12(8), 1338 (16 pp.). https://doi.org/10.3390/ma12081338
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
Piezo acoustic versus opto-acoustic sensors in laser processing
Wasmer, K., Le-Quang, T., Shevchik, S. A., & Violakis, G. (2019). Piezo acoustic versus opto-acoustic sensors in laser processing. In NDT.net (p. (8 pp.).
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
Laser processing quality monitoring by combining acoustic emission and machine learning: a high-speed X-ray imaging approach
Wasmer, K., Le-Quang, T., Meylan, B., Vakili-Farahani, F., Olbinado, M. P., Rack, A., & Shevchik, S. A. (2018). Laser processing quality monitoring by combining acoustic emission and machine learning: a high-speed X-ray imaging approach. In M. Schmidt, F. Vollertsen, & G. Dearden (Eds.), Procedia CIRP: Vol. 74. 10th CIRP conference on photonic technologies [LANE 2018] (pp. 654-658). https://doi.org/10.1016/j.procir.2018.08.054
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
When AE (acoustic emission) meets AI (artificial intelligence) II
Wasmer, K., Saeidi, F., Meylan, B., Le, Q. T., & Shevchik, S. A. (2018). When AE (acoustic emission) meets AI (artificial intelligence) II. In Conference proceedings Ewgae 2018 (p. (12 pp.). CETIM.
Acoustic Emission for <i>in situ</i> monitoring of laser processing
Shevchik, S., Le, Q. T., Meylan, B., & Wasmer, K. (2018). Acoustic Emission for in situ monitoring of laser processing. In Conference proceedings Ewgae 2018 (p. (9 pp.). CETIM.