基于自适应Var-MRLA稀疏方法的频域超声成像效率优化

IF 5.6 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Xiao Li, Shijie Jin, Chengjun Di, Zhongbing Luo
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引用次数: 0

摘要

超声检测中的时域全聚焦方法(TFM)面临着计算效率低下的问题,主要是由于大规模的全矩阵捕获(FMC)数据集和复杂的延迟和(DAS)算法。本文提出了一种基于频域TFM的稀疏阵列设计方案,将单元选择与频域TFM数据集相结合,以保持稳定的成像性能,提高成像效率。通过方差分析动态选择包含关键信息的发射机子集,并根据最小冗余原则优化阵列布局。实验结果表明,该阵列对铝合金样品中不同间距和位置的侧钻孔具有较强的适应性,且光束方向性保持在与原阵列相当的水平。成像时间至少缩短45.6%,等效直径的测量误差在0.2 mm以内。最后进行了钢材料、平面缺陷、双层介质等不同条件下的模拟和实验测试。缺陷指示和定量结果进一步证实了该方法与原始阵列和传统稀疏阵列相比具有良好的成像性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficiency optimization of frequency-domain ultrasonic imaging by adaptive Var-MRLA sparse method
The time-domain total focusing method (TFM) in ultrasonic testing faces the issue of computational inefficiency, primarily due to the large-scale full matrix capture (FMC) datasets and the complex delay-and-sum (DAS) algorithm. In this paper, a new design scheme based on the frequency-domain TFM for sparse arrays is proposed by linking element selection with the FMC datasets to maintain stable imaging performance and improve imaging efficiency. A subset of transmitters containing critical information is dynamically selected through variance analysis, and the array arrangements are optimized according to the minimum redundancy principle. Experimental results demonstrate strong adaptability in detecting side-drilled holes (SDHs) with different spacings and positions in aluminum alloy specimens, and the beam directivity is maintained at a level comparable to the original array. The imaging time is reduced by 45.6 % at least, and the measurement errors of equivalent diameters are within 0.2 mm. Finally, simulation and experimental tests are conducted under different conditions, including steel material, planar defects, and double-layer media. The defect indications and quantitative results further confirm the great imaging performance of the proposed method compared to the original array and conventional sparse arrays.
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来源期刊
Measurement
Measurement 工程技术-工程:综合
CiteScore
10.20
自引率
12.50%
发文量
1589
审稿时长
12.1 months
期刊介绍: Contributions are invited on novel achievements in all fields of measurement and instrumentation science and technology. Authors are encouraged to submit novel material, whose ultimate goal is an advancement in the state of the art of: measurement and metrology fundamentals, sensors, measurement instruments, measurement and estimation techniques, measurement data processing and fusion algorithms, evaluation procedures and methodologies for plants and industrial processes, performance analysis of systems, processes and algorithms, mathematical models for measurement-oriented purposes, distributed measurement systems in a connected world.
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