Crack growth optimization using eddy current testing and genetic algorithm for estimating the stress intensity factors
Author:
Funder
Direction Générale de la Recherche Scientifique et du Développement Technologique
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00707-024-03903-4.pdf
Reference58 articles.
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2. Tong, Z., Xie, S., Liu, H., Zhang, W., Pei, C., Li, Y., Chen, Z., Uchimoto, T., Takagi, T.: An efficient electromagnetic and thermal modelling of eddy current pulsed thermography for quantitative evaluation of blade fatigue cracks in heavy-duty gas turbines. Mech. Syst. Signal Process. 142, 106781 (2020). https://doi.org/10.1016/j.ymssp.2020.106781
3. Tong, Z., Xie, S., Chen, H., Qiu, J., Cai, W., Pei, C., Chen, Z., Uchimoto, T., Takagi, T.: Quantitative mapping of depth profile of fatigue cracks using eddy current pulsed thermography assisted by PCA and 2D wavelet transformation. Mech. Syst. Signal Process. 175, 109139 (2022). https://doi.org/10.1016/j.ymssp.2022.109139
4. Ju, S., Li, D., Jia, J.: Machine-learning-based methods for crack classification using acoustic emission technique. Mech. Syst. Signal Process. 178, 109253 (2022). https://doi.org/10.1016/j.ymssp.2022.109253
5. Guesmi, M., Harzallah, S., Kouzou, A.: New non-destructive testing approach based on eddy current for crack orientation detection and parameter estimation. Int. J. Appl. Electromagn. Mech.Electromagn. Mech. 67(4), 431–451 (2021). https://doi.org/10.3233/JAE-210049
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