PSI - Issue 64

Hendrik Holzmann et al. / Procedia Structural Integrity 64 (2024) 1303–1310 Hendrik Holzmann / Structural Integrity Procedia 00 (2019) 000 – 000

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application using experimental data instead of the simulation approach. The datasets consisting of a few hundred samples furthermore indicate that a realistic implementation is not cheap but possible. Further work will cover the experimental validation of different fault models as well as the experimental creation of a database with subsequent testing of the workflow. Eventually, these results can be used to optimize the production process leading to fewer customer complaints, replacements and therefore savings in carbon emissions. Acknowledgements This work has been funded by the technology transfer program “TTP Leichtbau” of the German Federal Ministry for Economic Affairs and Climate Action (Reference number: 03LB3029C). References Akiba T, Sano S, Yanase T, Ohta T, Koyama M (2019) Optuna: A Next-generation Hyperparameter Optimization Framework. In: Teredesai A, Kumar V, Li Y, Rosales R, Terzi E, Karypis G (eds) Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, New York, NY, USA, pp 2623 – 2631 Chen R-C, Dewi C, Huang S-W, Caraka RE (2020) Selecting critical features for data classification based on machine learning methods. J Big Data 7. https://doi.org/10.1186/s40537-020-00327-4 Fan W, Qiao P (2011) Vibration-based Damage Identification Methods: A Review and Comparative Study. Structural Health Monitoring 10:83 – 111. https://doi.org/10.1177/1475921710365419 Farrar CR, Worden K (2013) Structural health monitoring: A machine learning perspective. Wiley, Chichester West Sussex U.K., Hoboken N.J. Feng Y, Sheikh AH, Ng C-T, Smith ST (2023) An efficient modelling technique for simulation of guided waves in delaminated composite and sandwich structures. Journal of Sound and Vibration 566:117929. https://doi.org/10.1016/j.jsv.2023.117929 Haugwitz C, Hahn-Jose T, Allevato G, Hinrichs J, Dörsam JH, Kühn A, Steineck S, Lange J, Kupnik M (2023) Detection of Air-Voids in Foam filled Sandwich Panels using Air-Coupled Lamb Waves. In: 2023 IEEE International Ultrasonics Symposium (IUS). IEEE, pp 1 – 4 Lange J, Berner K (2020) Sandwichelemente im Hochbau. In: Kuhlmann U (ed) Neue Normung im Hochbau, Leichtbau. Ernst & Sohn, Berlin, pp 905 – 971 Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, Köpf A, Yang E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai J, Chintala S (2019) PyTorch: An Imperative Style, High-Performance Deep Learning Library Pozorski Z (2016) Sandwich panels in civil engineering - theory, testing and design. Rozprawy / Politechnika Poznańska, nr 531. Wydawnictwo Politechniki Poznańskiej, Poznań Seventekidis P, Giagopoulos D, Arailopoulos A, Markogiannaki O (2020) Structural Health Monitoring using deep learning with optimal finite element model generated data. Mechanical Systems and Signal Processing 145:106972. https://doi.org/10.1016/j.ymssp.2020.106972 Worden K, Farrar CR, Manson G, Park G (2007) The fundamental axioms of structural health monitoring. In: Proc. R. Soc. A. vol 463, pp 1639 – 1664 Yaacoubi S, El Mountassir M, Ferrari M, Dahmene F (2019) Measurement investigations in tubular structures health monitoring via ultrasonic guided waves: A case of study. Measurement 147:106800. https://doi.org/10.1016/j.measurement.2019.07.028 Zaparoli Cunha B, Droz C, Zine A-M, Foulard S, Ichchou M (2023) A review of machine learning methods applied to structural dynamics and vibroacoustic. Mechanical Systems and Signal Processing 200:110535. https://doi.org/10.1016/j.ymssp.2023.110535