journal article·2025-08-20

Equivalent modulus prediction model of composite laminate plates with local crack damage using lamb wave velocity

Li Qiubo, Fu Hailong, Hu Sen, Tian Xuanxin, Li Shiyu, Ai Shigang

Polymer Composites· Volume 46· Issue 12
Publisher page

Abstract

Abstract Nondestructive testing of composite structures is essential and challenging work. This paper establishes a correlation model between Lamb wave velocity and the local modulus of carbon fiber‐reinforced plastic (CFRP) composite laminates by measuring and calculating the Lamb wave velocity of CFRP sheets under different damage conditions. The physical significance of each parameter in the model is analyzed, and the experimental method for determining seven parameters is presented. The model accounts for the influence of excitation frequency and laminate thickness, enhancing its flexibility and application range. The accuracy of the model is validated through verification experiments. The verification results show that the minimum error in the equivalent modulus prediction is only −0.09% (theoretical value: 56.55 GPa; predicted value: 56.50 GPa), while the maximum error reaches −6.28% (theoretical value: 60.18 GPa; predicted value: 56.40 GPa). The mean error is 2.52%. Highlights Investigated the impact of crack length on Lamb wave velocity in CFRP laminates. Analyzed how excitation frequency and plate thickness affect Lamb wave velocity in cracked CFRP laminates. Developed and validated a modulus prediction model for cracked CFRP laminates.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Ultrasonics and Acoustic Wave Propagation · Mechanical Behavior of Composites · Structural Health Monitoring Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Institute of Advanced Structure Technology Beijing Institute of Technology Beijing People's Republic of China

School of Mechanical Science and Engineering Northeast Petroleum University Daqing People's Republic of China

Funding

National Natural Science Foundation of China

References (55)

Show source-provided references
1. 10.1063/1.130617210.1063/1.1306172 ↗
2. 10.1016/j.ndteint.2008.09.01210.1016/j.ndteint.2008.09.012 ↗
3. 10.1016/j.ndteint.2019.05.00610.1016/j.ndteint.2019.05.006 ↗
4. 10.1016/j.ndteint.2019.03.00110.1016/j.ndteint.2019.03.001 ↗
5. 10.1080/15376494.2016.119679810.1080/15376494.2016.1196798 ↗
6. 10.1061/(ASCE)AS.1943‐5525.000106010.1061/(ASCE)AS.1943‐5525.0001060 ↗
7. 10.1016/S0963‐8695(00)00013‐X10.1016/S0963‐8695(00)00013‐X ↗
8. 10.1016/j.ndteint.2020.10236410.1016/j.ndteint.2020.102364 ↗
9. LiuL MeiH GuoC TuY.Remote optical thermography detection method and system for silicone polymer insulating materials used in power industry.2020;69(8):5782‐5790.10.1109/TIM.2019.2959855 ↗
10. 10.1016/j.compositesb.2023.11059910.1016/j.compositesb.2023.110599 ↗
11. 10.1063/1.471634710.1063/1.4716347 ↗
12. 10.1109/JSEN.2022.314482710.1109/JSEN.2022.3144827 ↗
13. 10.1177/147592171246999410.1177/1475921712469994 ↗
14. 10.1177/1475921719878210.1177/14759217198782 ↗
15. 10.1016/j.ndteint.2005.07.01210.1016/j.ndteint.2005.07.012 ↗
16. 10.1016/j.ndteint.2016.09.00310.1016/j.ndteint.2016.09.003 ↗
17. 10.1016/j.ndteint.2017.08.00410.1016/j.ndteint.2017.08.004 ↗
18. 10.1016/j.ndteint.2023.10283110.1016/j.ndteint.2023.102831 ↗
19. 10.1016/j.ndteint.2011.05.00910.1016/j.ndteint.2011.05.009 ↗
20. 10.1016/j.ndteint.2019.10214310.1016/j.ndteint.2019.102143 ↗
21. 10.1016/j.ndteint.2020.10227710.1016/j.ndteint.2020.102277 ↗
22. 10.1016/j.ndteint.2022.10273110.1016/j.ndteint.2022.102731 ↗
23. 10.1016/j.compscitech.2005.07.01410.1016/j.compscitech.2005.07.014 ↗
24. 10.1016/j.compscitech.2006.08.01910.1016/j.compscitech.2006.08.019 ↗
25. 10.3389/fphy.2022.107320610.3389/fphy.2022.1073206 ↗
26. 10.1088/1361‐6501/ac907510.1088/1361‐6501/ac9075 ↗
27. 10.1016/S0266‐3538(03)00163‐510.1016/S0266‐3538(03)00163‐5 ↗
28. 10.1016/j.compscitech.2004.05.01010.1016/j.compscitech.2004.05.010 ↗
29. 10.1016/j.compstruct.2022.11626310.1016/j.compstruct.2022.116263 ↗
30. 10.1016/j.compstruct.2022.11657110.1016/j.compstruct.2022.116571 ↗
31. 10.1016/j.compstruct.2022.11664210.1016/j.compstruct.2022.116642 ↗
32. 10.1016/j.compscitech.2005.07.04310.1016/j.compscitech.2005.07.043 ↗
33. 10.1016/j.compscitech.2017.03.01310.1016/j.compscitech.2017.03.013 ↗
34. 10.1016/j.compscitech.2020.10807310.1016/j.compscitech.2020.108073 ↗
35. 10.3390/s2011303510.3390/s20113035 ↗
36. 10.1016/j.compstruct.2022.11557810.1016/j.compstruct.2022.115578 ↗
37. 10.3390/electronics1205114810.3390/electronics12051148 ↗
38. 10.1121/1.40815110.1121/1.408151 ↗
39. 10.1121/1.42682710.1121/1.426827 ↗
40. 10.1121/1.145107110.1121/1.1451071 ↗
41. 10.1016/j.compstruct.2015.02.02910.1016/j.compstruct.2015.02.029 ↗
42. 10.1016/j.compstruct.2020.11256910.1016/j.compstruct.2020.112569 ↗
43. 10.1023/A:102262050189910.1023/A:1022620501899 ↗
44. 10.1016/S1359‐8368(97)00007‐310.1016/S1359‐8368(97)00007‐3 ↗
45. 10.1016/S0041‐624X(99)00025‐610.1016/S0041‐624X(99)00025‐6 ↗
46. 10.1088/0964-1726/16/5/01710.1088/0964-1726/16/5/017 ↗
47. 10.1016/j.pmatsci.2018.04.00110.1016/j.pmatsci.2018.04.001 ↗
48. 10.1016/j.compstruct.2009.03.00110.1016/j.compstruct.2009.03.001 ↗
49. 10.1016/j.compstruct.2019.11096910.1016/j.compstruct.2019.110969 ↗
50. 10.1016/j.compscitech.2020.10821310.1016/j.compscitech.2020.108213 ↗
51. 10.3390/s2119667510.3390/s21196675 ↗
52. 10.1016/j.compstruct.2023.11723010.1016/j.compstruct.2023.117230 ↗
53. 10.1007/s00419‐019‐01552‐x10.1007/s00419‐019‐01552‐x ↗
54. HakodaC LissendenC RoseJL.Weak form implementation of the semi‐analytical finite element (SAFE) method for a variety of elastodynamic waveguides.2018;1949:230001. doi:10.1063/1.503164810.1063/1.5031648 ↗
55. 10.1016/j.compstruct.2022.11577910.1016/j.compstruct.2022.115779 ↗