基于Var-MRLA稀疏方法的自适应全聚焦成像

doi:10.3901/JME.260270

摘要/Abstract

摘要： 为降低全聚焦成像时间并提高成像质量，构建了基于信号方差和最小冗余的线性阵列(Variance in minimum redundancy linear array, Var-MRLA)稀疏方法，并用于超声成像与缺陷表征。该方法在稀疏阵列优化时同时考虑接收信号特征和孔径扩展，可根据缺陷位置自适应调整阵列排布且保持与满阵相同的有效孔径。采用32阵元、2.25 MHz相控阵探头，从不同位置对铝合金试块内部两个直径2 mm、中心深度15 mm、间距4 mm横通孔进行了全聚焦方法(Total focusing method, TFM)试验检测。结果表明Var-MRLA稀疏方法具有稳定的成像性能与较高的成像效率，两横通孔之间的幅值差始终小于0.1 dB，成像时间较稀疏前减少75.4%以上。此外，利用符号相干因子(Sign coherence factor, SCF)对稀疏后的信号进行加权处理，可使TFM图像信噪比提高8.95 dB，大幅改善了图像质量和缺陷辨识能力。

关键词: 全聚焦方法, 稀疏阵列, 超声成像, 最小冗余阵列, 信噪比

Abstract: To reduce time cost and improve imaging quality for total focusing method(TFM), a sparse method based on variance in minimum redundancy linear array(Var-MRLA) is proposed and applied to ultrasonic imaging and defect characterization. The characteristics of received signals and aperture extension are simultaneously considered by this method during optimizing sparse array, allowing the adaptive adjustment of array arrangement according to defect position while maintaining the same effective aperture as the full array. Two transverse holes with 2 mm diameter, 15 mm center depth and 4 mm spacing in the aluminum alloy test block are detected experimentally with total focusing method(TFM) from different positions using a 32-element and 2.25 MHz phased array probe. The results indicate that the Var-MRLA sparse method shows stable imaging performance and high efficiency. The amplitude difference between the two transverse holes is always less than 0.1dB, and the imaging time is reduced by more than 75.4%. Furthermore, the signal-to-noise ratio(SNR) of TFM images is improved by 8.95 dB through the weighted processing of sparse signals utilizing the sign coherence factor(SCF), significantly enhancing image quality and defect identification capability.

Key words: total focusing method (TFM), sparse array, ultrasonic imaging, minimum redundancy linear array (MRLA), signal-to-noise ratio (SNR)

中图分类号:

TG115
TH142

李潇, 罗忠兵, 孙浩, 冯伟伦, 金士杰. 基于Var-MRLA稀疏方法的自适应全聚焦成像[J]. 机械工程学报, 2026, 62(10): 41-49.

LI Xiao, LUO Zhongbing, SUN Hao, FENG Weilun, JIN Shijie. Adaptively Total Focusing Imaging Based on Var-MRLA Sparse Method[J]. Journal of Mechanical Engineering, 2026, 62(10): 41-49.

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http://www.cjmenet.com.cn/CN/Y2026/V62/I10/41

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