A Depth Fusion Characterization Method of Surface Cracks in Aluminum Plate Based on Fuzzy Relationship Matrices
Source abstract
Abstract
The inspection and characterization of surface cracks are important issues in the structural health monitoring (SHM) of structural components. Electromagnetic acoustic transducer (EMAT) is a nondestructive testing technology widely used in health monitoring. However, it is difficult to evaluate the depth of defects accurately with consideration of the ambiguous relationship between the cracks and feature values. Therefore, an accurate evaluation method for the depth of surface cracks needs to be established. In this paper, a fusion characterization method based on fuzzy relationship matrices is proposed. Firstly, the different depth estimated values of the surface crack are obtained based on the relationship modals between the crack depth and multiple different feature values. Secondly, a difference matrix is established that can describe the difference in accuracy between different depth estimated values, and a relationship matrix is constructed based on the triangular fuzzy number method for evaluating the quantization accuracy. Thirdly, the weights of different estimated values in the fusion quantification were determined, and the final defect depth was obtained. Finally, we applied our approach to experimental data and its maximum quantization relative error was 5.9%, which demonstrates its capability to characterize surface cracks compared with other methods.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Numerical methods in engineering · Non-Destructive Testing Techniques · Structural Health Monitoring Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (19)
Show source-provided references
1. Sun, H., Peng, L., Wang, S., Huang, S., and Qu, K., Development of frequency-mixed point-focusing shear horizontal guided-wave EMAT for defect inspection using deep neural network, IEEE Trans. Instrum. Meas., 2020, vol. 70, pp. 1–14. https://doi.org/10.1109/TIM.2020.303816710.1109/TIM.2020.3038167 ↗
2. Wu, Z. and Tao, N., Quantitative characterization of defect depth using pulsed thermography based on dynamic compression reconstruction peak method, Measurement, 2025, vol. 253, p. 117611. https://doi.org/10.1016/j.measurement.2025.11761110.1016/j.measurement.2025.117611 ↗
3. Gómez Muñoz, C.Q., García Márquez, F.P., Arcos Jiménez, A., Cheng, L., Kogia, M., and Papaelias, M., Calculus of the defect severity with EMATs by analyzing the attenuation curves of the guided waves, Proc. 12th Int. Conf. Cond. Monit. Mach. Failure Prev. Technol. (CM 2015/MFPT 2015) (Oxford, UK, 2015).
4. Cheng, L., Kogia, M., Mohimi, A., Kappatos, V., Selcuk, C., and Gan, T.-H., Crack characterisation using invariable feature extraction in stainless steel specimen used for absorber tubes of CSP applications via EMAT, Renewable Energy, 2017, vol. 101, pp. 771–781. https://doi.org/10.1016/j.renene.2016.09.04810.1016/j.renene.2016.09.048 ↗
5. Lin, S. and Wang, Y., Crack-depth estimation in concrete elements using ultrasonic shear-horizontal waves, J. Perform. Constr. Facil., 2020, vol. 34, no. 4, p. 04020064. https://doi.org/10.1061/(ASCE)CF.1943-5509.000145610.1061/(ASCE)CF.1943-5509.0001456 ↗
6. Edwards, R.S., Dixon, S., and Jian, X., Characterisation of defects in the railhead using ultrasonic surface waves, NDT and E Int., 2006, vol. 39, no. 6, pp. 468–475. https://doi.org/10.1016/j.ndteint.2005.12.00110.1016/j.ndteint.2005.12.001 ↗
7. Wilcox, P.D., Lowe, M.J., and Cawley, P., The excitation and detection of Lamb waves with planar coil electromagnetic acoustic transducers, IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 2005, vol. 52, no. 12, pp. 2370–2383. https://doi.org/10.1109/TUFFC.2005.156327310.1109/TUFFC.2005.1563273 ↗
8. Bernstein, J.R. and Spicer, J.B., Hybrid laser/broadband EMAT ultrasonic system for characterizing cracks in metals, J. Acoust. Soc. Am., 2002, vol. 111, no. 4, pp. 1685–1691. https://doi.org/10.1121/1.145893710.1121/1.1458937 ↗
9. Masserey, B. and Mazza, E., Ultrasonic sizing of short surface cracks, Ultrasonics, 2006, vol. 44, pp. e195–e204. https://doi.org/10.1016/j.ultras.2006.06.05910.1016/j.ultras.2006.06.059 ↗
10. Xingguo, W., Junjie, C., Yingchun, S., Shaodong, T., Man, Y., and Jiujun, X., Measurement of attenuation of ultrasonic propagating through the thin layer media with time delay spectrum, Chin. J. Mech. Eng., 2010, vol. 23, no. 2, pp. 129–134. https://doi.org/10.3901/CJME.2010.02.12910.3901/CJME.2010.02.129 ↗
11. Tanimola, F. and Hill, D., Distributed fibre optic sensors for pipeline protection, J. Natl. Gas Sci. Eng., 2009, vol. 1, no. 4-5, pp. 134–143. https://doi.org/10.1016/j.jngse.2009.08.00210.1016/j.jngse.2009.08.002 ↗
12. Trushkevych, O. and Edwards, R., Characterisation of small defects using miniaturised EMAT system, NDT and E Int., 2019, vol. 107, p. 102140. https://doi.org/10.1016/j.ndteint.2019.10214010.1016/j.ndteint.2019.102140 ↗
13. He, C., Liu, Z., Wu, B., and Zhang, Y., Estimation of surface crack depth using Rayleigh waves by electromagnetic acoustic transducers, Int. J. Acoust. Vib., 2017, vol. 22, no. 4, pp. 541–548. https://doi.org/10.20855/ijav.2017.22.444010.20855/ijav.2017.22.4440 ↗
14. Chen, T., Tian, G., Li, R., et al., Characterization of small delamination defects by multilayer flexible EMAT, IEEE Sens. J., 2024, vol. 24, no. 15, pp. 19210–19219. https://doi.org/10.1109/JSEN.2024.339269410.1109/JSEN.2024.3392694 ↗
15. Huang, S., Sun, H., Peng, L., Wang, S., Wang, Q., and Zhao, W., Defect Detection and Identification of Point-Focusing Shear-Horizontal EMAT for Plate Inspection, IEEE Trans. Instrum. Meas., 2021, vol. 70, pp. 1–9. https://doi.org/10.1109/TIM.2021.306242110.1109/TIM.2021.3062421 ↗
16. Sun, F., Wang, N., He, J., Guan, X., and Yang, J., Lamb wave damage quantification using GA-based LS-SVM, Materials, 2017, vol. 10, no. 6, p. 648. https://doi.org/10.3390/ma1006064810.3390/ma10060648 ↗
17. Feng, B., Ribeiro, A.L., and Ramos, H.G., Lamb Wave Characterization of Crack Depth in Aluminum Plates using Artificial Neural Networks, 2018 IEEE Far East NDT New Technol. and Appl. Forum (FENDT) (2018), pp. 50–54. https://doi.org/10.1109/FENDT.2018.868200810.1109/FENDT.2018.8682008 ↗
18. Baldazzi, G., Sulas, E., Urru, M., Tumbarello, R., Raffo, L., and Pani, D., Wavelet denoising as a post-processing enhancement method for non-invasive foetal electrocardiography, Comput. Methods Programs Biomed., 2020, vol. 195, p. 105558. https://doi.org/10.1016/j.cmpb.2020.10555810.1016/j.cmpb.2020.105558 ↗
19. Lei, B., Yi, P., and Xiang, J., A new defect classification approach based on the fusion matrix of multi-eigenvalue, IEEE Sens. J., 2020, vol. 20, no. 7, pp. 3398–3407. https://doi.org/10.1109/JSEN.2019.295865210.1109/JSEN.2019.2958652 ↗