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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
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
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
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
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
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
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
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
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
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
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.
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
AM/LW process monitoring combining high-speed X-ray imaging, acoustic & optical sensors and artificial intelligence
Wasmer, K., Le, T. Q., Meylan, B., Vakili-Farahani, F., Leinenbach, C., Olbinado, M. P., … Shevchik, S. A. (2018). AM/LW process monitoring combining high-speed X-ray imaging, acoustic & optical sensors and artificial intelligence. Presented at the ESRF user meeting 2018. Grenoble, France.
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