Research on defect identification of carbon fiber composite materials based on ultrasonic phased array
Source abstract
Abstract
Abstract It is more and more difficult to identify defects in carbon fiber composite materials due to the difficulty in making defect samples and the single signal analysis method. In order to better solve the problem of defect identification in carbon fiber composite materials, this study uses ultrasonic phased array equipment to quantitatively locate and detect carbon fiber composite laminates with embedded delamination defects, so as to more intuitively and effectively display the appearance of different delamination defects. The time domain analysis of the collected ultrasonic original signal and the time‐frequency domain analysis using wavelet packet are carried out. A total of 6 eigenvalues were extracted to reflect the ultrasonic signals of different delamination defects. By using genetic algorithm to optimize BP neural network, the recognition accuracy of delamination defects of different sizes is more than 95%, and the recognition accuracy of delamination defects of different depths is 100%, so as to realize the effective intelligent recognition of delamination defects of different sizes and depths of carbon fiber composites. This study is of great significance to improve the accuracy and reliability of defect identification of carbon fiber composite materials. Highlights The ultrasonic phased array equipment is used to quantitatively locate the carbon fiber composite laminates with embedded delamination defects, so that the appearance of different defects can be displayed more intuitively and effectively. Using time domain analysis and time‐frequency domain analysis based on wavelet packet, the combination of the two can more comprehensively extract the effective features of the defect signal. The BP neural network is optimized by genetic algorithm, and the results can effectively and automatically identify different layered defects, which lays a good foundation for the rapid and accurate identification of more defects in the future.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Ultrasonics and Acoustic Wave Propagation · Smart Materials for Construction · Mechanical Behavior of Composites
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical Engineering Zhejiang Sci‐Tech University Zhejiang Hangzhou China
School of Additive Manufacturing Zhejiang Institute of Mechanical & Electrical Engineering Zhejiang Hangzhou China
References (27)
Show source-provided references
1. 10.3390/app1104150810.3390/app11041508 ↗
2. A state of art review on recycling and remanufacturing of the carbon fiber from carbon fiber polymer composite
3. 10.1016/j.compstruct.2021.11503310.1016/j.compstruct.2021.115033 ↗
4. 10.1007/s10853-021-06806-010.1007/s10853-021-06806-0 ↗
5. 10.1177/1045389X1561090610.1177/1045389X15610906 ↗
6. 10.1080/00150193.2015.101245810.1080/00150193.2015.1012458 ↗
7. 10.5545/sv-jme.2010.17010.5545/sv-jme.2010.170 ↗
8. 10.1016/j.compstruct.2015.08.11910.1016/j.compstruct.2015.08.119 ↗
9. 10.1016/j.compositesb.2018.11.09110.1016/j.compositesb.2018.11.091 ↗
10. 10.1007/s11223-022-00414-210.1007/s11223-022-00414-2 ↗
11. 10.1016/j.compstruct.2022.11601610.1016/j.compstruct.2022.116016 ↗
12. 10.3390/polym1307101710.3390/polym13071017 ↗
13. 10.1784/insi.2012.54.11.61210.1784/insi.2012.54.11.612 ↗
14. 10.1016/j.ndteint.2014.01.00510.1016/j.ndteint.2014.01.005 ↗
15. Miao CJ · 2019
16. 10.3390/app1214696910.3390/app12146969 ↗
17. 10.1784/insi.2023.65.5.26210.1784/insi.2023.65.5.262 ↗
18. Digital modulation identification model using wavelet transform and statistical parameters
19. DanielJ MohanagayathriandR AbudhahirA.Characterisation of defects in magnetic flux leakage (MFL) images using wavelet transform and neural network (pp. 13–14). ICECS2014.10.1109/ECS.2014.6892788 ↗
20. Detection and classification of discontinuities using discrete wavelet transform and MFL testing
21. 10.1016/j.mtcomm.2021.10302110.1016/j.mtcomm.2021.103021 ↗
22. Chen YC · 2016
23. SaechaiS KongprawechnonW SahamitmongkolR.Test system for defect detection in construction materials with ultrasonic waves by support vector machine and neural network.Proceedings of 6th international conference on soft computing and intelligent systems(pp. 1034–1039).2012.10.1109/SCIS-ISIS.2012.6505090 ↗
24. 10.1016/j.ultras.2016.07.00810.1016/j.ultras.2016.07.008 ↗
25. Fault identification based on the kernel principal component analysis‐genetic particle swarm optimization‐support vector machine algorithm for seismic attributes in the Sihe coal mine, Qinshui Basin, China
26. Research on the application of policy tree algorithm in the detection of steel surface defects
27. 10.1007/s00170-016-9588-y10.1007/s00170-016-9588-y ↗