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平面波成像因其数据采集速度快, 系统结构简单, 在无损检测中得到广泛应用. 但由于其采用非聚焦发射方式, 声能分布分散, 成像质量较差. 为改善成像质量, 复合平面波成像方法通过多角度平面波的相干叠加来提升成像效果, 但在图像分辨率、对比度以及伪像抑制方面仍存在不足. 为了提高平面波成像质量, 本文提出一种基于延迟乘和的复合平面波成像方法(coherent plane wave compounding-delay multiply and sum, CPWC-DMAS). 首先对多个角度的平面波信号进行相干叠加, 实现多角度信息融合. 然后在图像输出中引入空间相干性来提高图像质量, 实现多角度复合平面波的高质量成像. 最后, 对钢轨与车轮构件进行实验验证, 结果表明: CPWC-DMAS算法与全聚焦和复合平面波算法相比, 在阵列性能指标方面提升了51.18%, 50%, 在对比度方面提升了50.8%, 46.52%, 在信噪比方面提升了25.14%, 21.56%, 提高了成像质量和分辨能力.Plane wave imaging has widespread applications in non-destructive testing due to its fast data acquisition speed and simple system architecture. However, traditional plane wave imaging employs an unfocused transmission scheme. This results in dispersed acoustic energy distribution, low imaging resolution, and poor image quality. Although coherent plane wave compounding (CPWC) improves imaging performance through multi-angle coherent summation, it still has shortcomings in image resolution, contrast, and artifact suppression when detecting defects far from the acoustic axis center. To break through these limitations, this paper proposes a coherent plane wave compounding with delay multiplication and sum (CPWC-DMAS) method in which multi-angle plane wave is combined with DMAS beamforming technology to enhance imaging quality and resolution capability. First, coherent summation of multi-angle plane wave signals is performed to achieve comprehensive angular information fusion, ensuring effective coverage of the detection region. Subsequently, the DMAS method is used to perform cross-multiplication and summation of signals acquired from all angles by different array elements, utilizing the spatial coherence between received signals from different array elements to effectively enhance the target echo signals, while suppressing incoherent noise and reducing artifacts. Finally, to validate the correctness and effectiveness of the proposed method, experimental verification is conducted on defects embedded in steel rail and wheel components. The results indicate that compared with the total focusing method and CPWC algorithms, the proposed CPWC-DMAS algorithm achieves significant improvements of 51.18% and 50% in array performance index, 50.8% and 46.52% in contrast ratio, and 25.14% and 21.56% in signal-to-noise ratio, respectively. In summary, the proposed CPWC-DMAS algorithm demonstrates significant advantages over traditional methods in resolution enhancement, contrast improvement, and artifact suppression, achieving high-quality imaging for multi-angle coherent plane wave compounding. This method provides a novel approach for detecting defects both near and away from the center of acoustic axis, offering new insights into defect detection in complex structures with broad engineering applications.
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Keywords:
- ultrasound imaging /
- coherent plane wave compounding /
- delay multiply and sum /
- defect detection
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图 2 平面波算法示意图
Fig. 2. Schematic diagram of the coherent plane wave compounding algorithm.
图 4 钢轨与车轮试样缺陷分布 (a) A区钢轨横向缺陷; (b) B区钢轨纵向缺陷; (c) C区车轮纵向缺陷, D区车轮底部缺陷
Fig. 4. Distribution of defects in rail and wheel specimens: (a) Transverse defects in Region A of the rail; (b) longitudinal defects in Region B of the rail; (c) longitudinal defects in Region C of the wheel, bottom defect in Region D of the wheel.
图 5 钢轨缺陷成像结果 (a), (d) TFM; (b), (e) CPWC; (c), (f) CPWC-DMAS
Fig. 5. Imaging results of rail defects: (a), (d) TFM; (b), (e) CPWC; (c), (f) CPWC-DMAS.
图 6 钢轨缺陷成像指标 (a)—(c)横向缺陷的API, CR, SNR; (d)—(f)纵向缺陷的API, CR, SNR
Fig. 6. Rail defect imaging metrics: (a)—(c) API, CR, SNR for transverse defects; (d)—(f) API, CR, SNR for longitudinal defects.
图 7 车轮缺陷成像结果 (a), (d) TFM; (b), (e) CPWC; (c), (f) CPWC-DMAS
Fig. 7. Imaging results of wheel defects: (a), (d) TFM; (b), (e) CPWC; (c), (f) CPWC-DMAS.
图 8 车轮缺陷成像指标 (a)—(c) 纵向缺陷的API, CR, SNR; (d)—(f) 底部缺陷的API, CR, SNR
Fig. 8. Wheel defect imaging metrics: (a)–(c) API, CR, SNR for longitudinal defects; (d)–(f) API, CR, SNR for bottom defects.
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