Sparse Optimal Design of an Ultrasonic Sensor Array for Fast TFM Based on a Discrete Slime Mold Algorithm

The total focusing method (TFM) is an ultrasonic phased array imaging algorithm used in ultrasonic nondestructive testing (NDT) that processes large amounts of data from full matrix capture (FMC). This limits its application in some industrial fields with real-time requirements. To solve this proble...

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Vydáno v:IEEE sensors journal Ročník 24; číslo 8; s. 12207 - 12216
Hlavní autoři: Zhu, Wenfa, Wei, Zhengbo, Xiang, Yanxun, Chai, Xiaodong, Liu, Sihao, Fan, Guopeng, Zhang, Haiyan, Zhang, Hui, Qi, Weiwei
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 15.04.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Acoustics
Algorithms
Antenna arrays
Arrays
Convergence
Discrete slime mold algorithm (DSMA)
Genetic algorithms
Imaging
Mold
Nondestructive testing
Optimization
Particle swarm optimization
Phased arrays
Real numbers
Sensor arrays
Sidelobe reduction
Sidelobes
Signal to noise ratio
Slime
sparse array optimization
total focusing method (TFM)
ultrasonic phased array
Ultrasonic testing
ISSN:1530-437X, 1558-1748
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Shrnutí:The total focusing method (TFM) is an ultrasonic phased array imaging algorithm used in ultrasonic nondestructive testing (NDT) that processes large amounts of data from full matrix capture (FMC). This limits its application in some industrial fields with real-time requirements. To solve this problem, a sparse array optimization method is applied to FMC-TFM that can reduce time consumption and improve imaging efficiency. However, conventional intelligent optimization methods, such as genetic algorithm (GA), use binary encoding, which require intensive computation and are easily trapped in local optima. This article proposes a discrete slime mold algorithm (DSMA), in which the slime mold position is coded in real numbers instead of binary. In the optimization process, a mapping model between the slime mold and ultrasonic array is established. A fitness function with a narrow main lobe and low sidelobe is constructed to obtain the sparse array position with the best performance. In experiments, the proposed method reduces the imaging time by more than 50% compared with conventional TFM, without affecting imaging quality. Compared with a GA and binary particle swarm optimization (BPSO), the proposed method improves array performance indicator (API) and signal-to-noise ratio (SNR) performance.
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2024.3372579