journal article·2025-07-03

Sparse TFM imaging of different-scale phased array based on the DWSO algorithm

Zhengbo Wei, Wenfa Zhu, Hui Zhang, Xiaodong Chai, Weiwei Qi, Guopeng Fan, Haiyan Zhang

Publisher page

Abstract

Using sparse matrix for total focusing method (TFM) imaging can improve computational efficiency in non-destructive testing (NDT). In sparse matrix design, binary particle swarm optimisation (BPSO) and genetic algorithm (GA) often fall into local optimal solution, and the computational efficiency decreases significantly with the expansion of array scale. In this paper, a discrete war strategy optimisation (DWSO) is proposed to realise sparse array ultrasonic imaging. This method uses equidistant scatter mapping to real-number encode the array position and limit the search range. Then, the fitness function is constructed with low side lobe peak and narrow main lobe width to obtain the optimal array element distribution. Experiments on standard test block demonstrate the DWSO algorithm has a narrower main lobe width and lower side lobe peaks than BPSO and GA. The imaging time of the sparse array constructed by this algorithm is reduced by more than 65% compared with the full array. As array scale grows, the running time variation of the proposed method is reduced by 73.2% and 77.3% compared with GA and BPSO, which has good adaptability and provides theoretical support for real-time defect detection.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Ultrasonics and Acoustic Wave Propagation · Non-Destructive Testing Techniques · Structural Health Monitoring Techniques

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

Affiliations

Shanghai University of Engineering Science

East China University of Science and Technology

Shanghai University

Funding

National Key Research and Development Program of China · National Natural Science Foundation of China

References (36)

Show source-provided references
1. 10.1016/j.matpr.2022.12.05510.1016/j.matpr.2022.12.055 ↗
2. 10.3390/jcs512031910.3390/jcs5120319 ↗
3. 10.1016/j.ndteint.2005.04.00210.1016/j.ndteint.2005.04.002 ↗
4. 10.1016/j.ultras.2019.02.01110.1016/j.ultras.2019.02.011 ↗
5. 10.1177/1475921723115241310.1177/14759217231152413 ↗
6. 10.1080/10589759.2015.109362610.1080/10589759.2015.1093626 ↗
7. 10.1016/j.ymssp.2022.10911410.1016/j.ymssp.2022.109114 ↗
8. 10.1016/j.ymssp.2020.10714910.1016/j.ymssp.2020.107149 ↗
9. 10.1016/j.ndteint.2023.10282710.1016/j.ndteint.2023.102827 ↗
10. 10.1016/j.image.2018.07.00710.1016/j.image.2018.07.007 ↗
11. 10.1016/j.phpro.2015.03.01310.1016/j.phpro.2015.03.013 ↗
12. 10.1080/0278089880896213610.1080/02780898808962136 ↗
13. 10.1109/TBME.2022.315095210.1109/TBME.2022.3150952 ↗
14. 10.1016/j.ndteint.2017.05.00210.1016/j.ndteint.2017.05.002 ↗
15. 10.1016/j.measurement.2022.11169010.1016/j.measurement.2022.111690 ↗
16. 10.1016/j.apacoust.2024.10987810.1016/j.apacoust.2024.109878 ↗
17. 10.3390/rs1116186710.3390/rs11161867 ↗
18. 10.1109/LAWP.2018.279090710.1109/LAWP.2018.2790907 ↗
19. 10.1016/j.ultras.2006.05.13110.1016/j.ultras.2006.05.131 ↗
20. 10.1109/JSEN.2021.310061510.1109/JSEN.2021.3100615 ↗
21. 10.1016/j.sna.2021.11310210.1016/j.sna.2021.113102 ↗
22. 10.1080/10589759.2023.227400110.1080/10589759.2023.2274001 ↗
23. 10.1134/S106183092111004810.1134/S1061830921110048 ↗
24. 10.1109/ACCESS.2020.303024610.1109/ACCESS.2020.3030246 ↗
25. 10.1109/TBME.2023.332565710.1109/TBME.2023.3325657 ↗
26. 10.1109/ACCESS.2020.300194710.1109/ACCESS.2020.3001947 ↗
27. 10.1109/ACCESS.2022.315349310.1109/ACCESS.2022.3153493 ↗
28. 10.1016/j.ndteint.2020.10232410.1016/j.ndteint.2020.102324 ↗
29. 10.1016/j.apacoust.2019.07.01110.1016/j.apacoust.2019.07.011 ↗
30. 10.1080/1058975050020963810.1080/10589750500209638 ↗
31. 10.1109/TAP.2023.324059710.1109/TAP.2023.3240597 ↗
32. 10.1109/TUFFC.2018.287425610.1109/TUFFC.2018.2874256 ↗
33. 10.1109/JSEN.2008.200457410.1109/JSEN.2008.2004574 ↗
34. 10.1109/TUFFC.2020.297341910.1109/TUFFC.2020.2973419 ↗
35. 10.1016/j.ymssp.2023.11012110.1016/j.ymssp.2023.110121 ↗
36. 10.3390/met905050310.3390/met9050503 ↗