PSI - Issue 52

Feifei Ren et al. / Procedia Structural Integrity 52 (2024) 730–739 Author name / Structural Integrity Procedia 00 (2023) 000–000

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(b)

Fig. 5. The fit index of the group velocity for, (a) A 0 mode; (b) S 0 mode.

5. Conclusion

This work presents a comprehensive investigation of the sensitivity analysis of group velocity to temperature varia tions. A theoretical approach is established using the SAFE model to analyze the relationship between group velocity, frequencies, and thicknesses. Experimental tests are then conducted to determine the temperature-dependent material properties and evaluate their impact on group velocity. Follow by that, the sensitivity analysis of group velocity with respect to temperature variations is performed, and a linear fit was applied to model the relationship between group velocity and temperature. The findings demonstrate that the group velocity with temperature remains consistent within specific f · d ranges, specifically, [300 kHz · mm − 840 kHz · mm] for A 0 mode and [0 kHz · mm − 500 kHz · mm for S 0 ] mode. For the A 0 mode at the frequency of 50 kHz, the temperature dependency of the group velocity is constant in the thicknesses range of 4 mm-16 mm. These findings have significant implications for the scalability analysis of temperature com pensation for group velocity in panels of di ff erent thicknesses. The results of the sensitivity analysis provide valuable insights into the behavior of group velocity in response to temperature fluctuations. In SHM, understanding the tem perature sensitivity of guided wave group velocity enables the detection of anomalies or structural changes caused by temperature variations. For further research, the investigation of guided wave amplitude changes with temperature will be explored. This would allow for the scalability analysis of temperature compensation factors for guided wave signals in composite structures with di ff erent thicknesses. Overall, the established relationships and insights obtained from this study contribute to the development of compensation techniques and enhance the accurate interpretation of guided wave signals in practical applications.

Acknowledgements

The authors would like to express gratitude to the Lee Family scholarship at Imperial College London and the Great Britain China Educational Trust for funding Feifei Ren Ph.D. research.

References

Gobbato, M., Conte, J.P., Kosmatka, J.B., 2016. Statistical performance assessment of an nde-based shm-dp methodology for the remaining fatigue life prediction of monitored structural components and systems. Proceedings of the IEEE 104, 1575–1588. Pandey, P., Rai, A., Mitra, M., 2022. Explainable 1-d convolutional neural network for damage detection using lamb wave. Mechanical Systems and Signal Processing 164, 108220.

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