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高聚物黏结炸药(PBX)是一类由炸药造型颗粒高度填充和少量黏结剂构成的复合材料, 其内部裂纹的有效检出对结构完整性评价和安全可靠性评估具有重要意义. PBX因其特殊的细观结构特征呈现出声速低、衰减强的特点, 利用传统线性超声聚焦成像时, 超声波在颗粒边界发生的多重反射会造成重构图像信噪比低, 裂纹特征淹没在背景噪声中, 难以有效识别. 为改善成像信噪比, 提出一种基带非线性合成聚焦(BB-NSF)超声相控阵成像算法, 充分利用全矩阵数据中各通道接收信号的空间相干性, 增强缺陷位置图像强度的同时抑制了背景噪声, 再结合PBX曲面构形特征对算法的延时法则进行修正, 实现了曲面构形PBX不同取向裂纹缺陷的高信噪比成像, 并对重构图像质量和成像效率进行了定量对比评价. 结果表明, BB-NSF算法可有效地抑制背景噪声, 显著地改善PBX裂纹检出能力. BB-NSF算法中的信号空间相干度 p是影响图像信噪比的关键参数. p值大于2.0时, PBX裂纹缺陷超声重构图像的信噪比相比于传统的全聚焦成像算法可以提升10 dB以上. 随着 p值增加, 重构图像的信噪比可以得到进一步提升, 而计算效率保持稳定, 体现出BB-NSF算法在强衰减复合材料内部缺陷成像方面的优势和应用前景.
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关键词:
- 高聚物粘结炸药/
- 裂纹缺陷/
- 超声相控阵/
- 基带非线性合成聚焦算法
Polymer bonded explosive (PBX) is a kind of composite material with highly filled molding explosive particles (normally more than 95%) and a small quantity of binders (less than 5%). The effective detection of internal cracks in PBX is of great significance in evaluating structural integrity and safety reliability. Ultrasonic phased array detection and imaging methods show great advantages and potential in detecting crack defects. But acoustic test results indicate that the PBX has unique characteristics with low longitudinal wave velocity (～3000 m·s –1) and strong attenuation (attenuation coefficient ～400 dB·m –1for 2.5 MHz ultrasound). When the defect is imaged by traditional ultrasonic total focusing method (TFM), the structural noises at the boundaries between particles lead to low signal-to-noise ratio (SNR) in the FMC signals and strong background noise in reconstructed image, which will disturb the detection of cracks. To realize the high SNR imaging of crack defects in PBX, an ultrasonic imaging algorithm based on baseband nonlinear synthetic focusing (BB-NSF) is proposed. By utilizing the spatial coherence of the received signals in full matrix capture (FMC) data, the pixel intensity at defect position can be enhanced while the background noise can be drastically weakened. The delay rule of the algorithm is modified according to the characteristics of PBX surface configuration. In this way, the high SNR imaging of crack defects with different orientations of PBX surface configuration is realized, and the quality of the reconstructed images is compared and evaluated quantitatively. Meanwhile, the base band transformation in calculation process optimization could significantly reduce calculation burden and increase imaging efficiency. Experimental results show that the proposed algorithm can effectively suppress background noise and significantly improve the ability to detect the PBX cracks. The effective suppression to background noise makes the defect more highlighting and distinguished easily. For the BB-NSF algorithm, spatial coherence coefficient pis a crucial parameter used for dynamically regulating the SNR of reconstructed image. When pvalue is more than 2.0, the SNR of the ultrasonic reconstructed image of PBX crack defect is improved by more than 10 dB compared with that of the traditional linear synthetic focusing imaging. With the increase of pvalue, the SNR is further improved, while the calculation efficiency for a single image is almost kept stable. Moreover, the increase of SNR to some extent will improve the far-field detect capability. Besides, with the BB-NSF algorithm, flexible transducer inhibits different imaging characteristics of for cracks with different orientations and depths in curved PBX specimens. For defects with large orientation angle and buried depth, the tip, root and shape of cracks can be completely present. For defects with small orientation angle and buried depth, part of shape and contour features will be lost. In conclusion, the BB-NSF algorithm shows the advantage of high SNR and calculation efficiency in imaging PBX cracks, and exhibits great application prospect in imaging internal defects of other strongly attenuated composites. -
Keywords:
- polymer bonded explosives/
- crack defect/
- ultrasonic phased array/
- baseband nonlinear synthesis focusing algorithm
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算法 信噪比/dB 计算效率/
(s·帧–1)缺陷A 缺陷B 缺陷C TFM[30] 9.38 7.35 5.02 0.29 指向性校正的TFM[12] 12.55 10.87 9.28 0.49 相干因子的TFM[31] 13.04 15.01 14.25 0.32 F-DMAS[25] 19.19 19.18 15.30 142.29 BB-NSF (p= 1) 9.38 7.35 5.02 0.38 BB-NSF (p= 1.5) 14.33 13.37 10.59 0.59 BB-NSF (p= 2.0) 19.15 19.19 15.64 0.41 BB-NSF (p= 3.0) 27.98 29.43 24.30 0.60 
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