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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
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
Smart closed-loop control of laser welding using reinforcement learning
Le Quang, T., Meylan, B., Masinelli, G., Saeidi, F., Shevchik, S. A., Farahani, F. V., & Wasmer, K. (2022). Smart closed-loop control of laser welding using reinforcement learning. In M. Schmidt, F. Vollertsen, & B. M. Colosimo (Eds.), Procedia CIRP: Vol. 111. 12th CIRP conference on photonic technologies [LANE 2022] (pp. 479-483). https://doi.org/10.1016/j.procir.2022.08.074
Deep transfer learning of additive manufacturing mechanisms across materials in metal-based laser powder bed fusion process
Pandiyan, V., Drissi-Daoudi, R., Shevchik, S., Masinelli, G., Le-Quang, T., Logé, R., & Wasmer, K. (2022). Deep transfer learning of additive manufacturing mechanisms across materials in metal-based laser powder bed fusion process. Journal of Materials Processing Technology, 303, 117531 (14 pp.). https://doi.org/10.1016/j.jmatprotec.2022.117531
In situ quality monitoring in direct energy deposition process using co-axial process zone imaging and deep contrastive learning
Pandiyan, V., Cui, D., Le-Quang, T., Deshpande, P., Wasmer, K., & Shevchik, S. (2022). In situ quality monitoring in direct energy deposition process using co-axial process zone imaging and deep contrastive learning. Journal of Manufacturing Processes, 81, 1064-1075. https://doi.org/10.1016/j.jmapro.2022.07.033
Monitoring of direct energy deposition process using manifold learning and co-axial melt pool imaging
Pandiyan, V., Cui, D., Parrilli, A., Deshpande, P., Masinelli, G., Shevchik, S., & Wasmer, K. (2022). Monitoring of direct energy deposition process using manifold learning and co-axial melt pool imaging. Manufacturing Letters, 33(Suppl.), 776-785. https://doi.org/10.1016/j.mfglet.2022.07.096
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
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
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