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The role of an individual lack-of-fusion defect in the fatigue performance of additive manufactured Ti-6Al-4V part
Li, Z., Gut, A., Burda, I., Michel, S., Romancuk, D., & Affolter, C. (2022). The role of an individual lack-of-fusion defect in the fatigue performance of additive manufactured Ti-6Al-4V part. In Proceedings of 2022 international additive manufacturing conference (IAM2022) (pp. IAM2022-94120 (6 pp.). https://doi.org/10.1115/IAM2022-94120
Testing of additively manufactured small scale RC specimens for statistical validation of structural models in earthquake engineering
Giudice, L. D., Wrobel, R., Leinenbach, C., & Vassiliou, M. F. (2020). Testing of additively manufactured small scale RC specimens for statistical validation of structural models in earthquake engineering. In M. Papadrakakis, M. Fragiadakis, & C. Papadimitriou (Eds.), Vol. 2. Proceedings of the XI international conference on structural dynamics (EURODYN 2020) (pp. 3313-3323). https://doi.org/10.47964/1120.9271.19825
Static testing of additevly manufactured microreinforced concrete specimens for statistical structural model validation at a small scale
Giudice, L. D., Wrobel, R., Leinenbach, C., & Vassiliou, M. F. (2020). Static testing of additevly manufactured microreinforced concrete specimens for statistical structural model validation at a small scale. In 8AESE abstract book. https://doi.org/10.3929/ethz-b-000397086
Soft carbon-based multi-sensory resistive receptors
Georgopoulou, A., Eckey, L. M., & Clemens, F. (2022). Soft carbon-based multi-sensory resistive receptors. In A. P. Vassilopoulos & V. Michaud (Eds.), Life cycle assessment: Vol. 6. Proceedings of the 20th European conference on composite materials. Composite meet sustainability (pp. 247-254). Ecole Polytechnique Fédérale de Lausanne (EPFL).
Physical modelling of rc structures through digitally manufactured small-scale specimens for centrifuge testing
Giudice, L. D., Wrobel, R., Leinenbach, C., & Vassiliou, M. F. (2021). Physical modelling of rc structures through digitally manufactured small-scale specimens for centrifuge testing. In E. Júlio, J. Valença, & S. S. Louro (Eds.), fib symposium proceedings. Concrete structures: new trends for eco-efficiency and performance. Proceedings of the fib symposium 2021 (pp. 2317-2326). fib.
Microstructure formation in micron-scale thin-walled Hastelloy X samples fabricated with laser powder bed fusion
Wróbel, R., Ghanbari, P. G., Maeder, X., Hosseini, E., & Leinenbach, C. (2023). Microstructure formation in micron-scale thin-walled Hastelloy X samples fabricated with laser powder bed fusion. Progress in Additive Manufacturing. https://doi.org/10.1007/s40964-023-00458-z
Memory-steel for smart steel structures: a review on recent developments and applications
Wang, S., Mohri, M., Li, L., Izadi, M., Jafarabadi, A., Pichler, N., & Ghafoori, E. (2023). Memory-steel for smart steel structures: a review on recent developments and applications. In M. Veljkovic, T. Tankova, F. Kavoura, A. Taras, V. Silvestru, & V. Vigneri (Eds.), ce/papers - proceedings in civil engineering: Vol. 6. EUROSTEEL 2023 (pp. 949-958). https://doi.org/10.1002/cepa.2756
Influence of pre-curing stage in additive manufacturing of advanced thermosetting composites
Taddei, F., Barbezat, M., Troiani, E., & Struzziero, G. (2022). Influence of pre-curing stage in additive manufacturing of advanced thermosetting composites. In A. Vassilopoulos & V. Michaud (Eds.), Manufacturing: Vol. 2. Proceedings of the 20th European conference on composite materials. Composite meet sustainability (pp. 385-392). https://doi.org/10.5075/epfl-298799_978-2-9701614-0-0
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
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).
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
<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