Abstract
The real-time monitoring of fatigue crack length is crucial for estimating the damage tolerance and the residual lifetime of aircraft metallic structures. This paper proposed a deep learning-based approach for predicting the fatigue crack size of metallic structures using strain monitoring data. By constructing learning models for Cycle Consistent Adversarial Networks, crack sizes are classified and quantified, and the correlation between the measured strain data and finite element simulation data was established. The proposed approach was applied for monitoring the growth of fatigue crack in a central hole plate subjected to random fatigue loads. The predictions show that the proposed model can monitor the fatigue crack lengths with high accuracy, where the prediction error of the crack length is less than 1 mm. This approach provides a reliable and accurate method for predicting the fatigue crack size of metallic structures, which may have important practical applications in aviation industry.
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References
Chen, Z.M., Tang, N., Fang, H.F.: Flight load modeling method optimized for complex structures. Adv. Aeronaut. Sci. Eng. 13(2), 45–50 (2022)
Li, B., Fan, X.L., Zhou, K., Wang, T. J.: Effect of oxide growth on the stress development in double-ceramic-layer thermal barrier coatings. Ceram. Int. 43(17), 14763–14774 (2017)
Chang, Q., Yang, W.X., Zhao, H., et al.: A multi-sensor based crack propagation monitoring research. Acta Aeronaut. Astronaut. Sin. 41(2), 226–237 (2020)
Li, B., Yang, B.S., Wang, S.Y., Chen, J., Jiang, W.: Numerical study of thermomechanical delamination mechanisms in segmented high-temperature coatings. Eng. Fail. Anal. 153, 107577 (2023)
Li, B., Shu, Y.X., Li, Y.Z.: Interface cracking behavior in high-temperature coatings with non-uniformly distributed segmentation cracks. Eur. J. Mech. A-Solid, 96, 104674 (2022)
Crocombe, A.D., Ong, C.Y., Chan, C.W.M., et al.: Investigating fatigue damage evolution in adhesively bonded structures using backface strain measurement. J. Adhes. 78, 745–776 (2020)
Wang, H. W.: Research on load identification algorithm of aircraft based on neural network. ZheJiang University (2018)
Trivailo, P.M., Carn, C.L.: The inverse determination of aerodynamic loading from structural response data using neural networks. Inverse Prob. Sci. Eng. 14(4), 379–395 (2006)
Renaud, G., Liao, M., Bombardier, Y.: Demonstration of an airframe digital twin framework using a CF-188 full-scale component test. ICAF 2019—Structural Integrity in the Age of Additive Manufacturing (2019)
Acknowledgements
This work was supported by the National Natural Science Foundation of China (12072272) and the National Science and Technology Major Project (J2019-I-0016-0015).
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Li, K., Qi, X., Li, B. (2024). Fatigue Crack Quantification Model for Metallic Structures Based on Strain Monitoring. In: Li, S. (eds) Computational and Experimental Simulations in Engineering. ICCES 2023. Mechanisms and Machine Science, vol 145. Springer, Cham. https://doi.org/10.1007/978-3-031-42987-3_16
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DOI: https://doi.org/10.1007/978-3-031-42987-3_16
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