Full-matrix capture (FMC)-based ultrasonic imaging provides good sensitivity to small defects in non-destructive testing and has gradually become a mainstream research topic. Many corresponding algorithms have been developed, e.g., the total focusing method (TFM). However, the efficiency of the TFM is limited, especially in multi-layered structures. Although the appearance of wavenumber algorithms, such as extended phase-shift migration (EPSM) methods, has improved imaging efficiency, these methods cannot be applied to cases with oblique incidence. Therefore, a modified wavenumber method for full-matrix imaging of multi-layered structures with oblique array incidence is proposed. This method performs a coordinate rotation in the frequency domain to adapt it to the oblique incidence. It then utilizes wave-field extrapolation to migrate the transmitting and receiving wave field to each imaging line, and a correlation imaging condition is used to reconstruct a total focused image. The proposed method can deal with any incident angle without precision loss. Moreover, it inherits the computational efficiency advantages of the wavenumber algorithms. The simulation and experimental results show that the proposed method performs better in terms of accuracy and efficiency than the TFM. Specifically, it is nearly 60 times faster than the TFM when processing an FMC dataset with a size of 4096 × 64 × 64.

Ultrasonic TOFD imaging, as an important non-destructive testing method, has a wide range of applications in pipeline girth weld inspection and testing. Due to the limited bandwidth of ultrasonic transducers, near-surface defects in the weld are masked and cannot be recognized, resulting in poor longitudinal resolution. Affected by the inherent diffraction effect of scattered acoustic waves, defect images have noticeable trailing, resulting in poor transverse resolution of TOFD imaging and making quantitative defect detection difficult. In this paper, based on the assumption of the sparseness of ultrasonic defect distribution, by constructing a convolutional model of the ultrasonic TOFD signal, the Orthogonal Matching Pursuit (OMP) sparse deconvolution algorithm is utilized to enhance the longitudinal resolution. Based on the synthetic aperture acoustic imaging model, in the wavenumber domain, backpropagation inference is implemented through phase transfer technology to eliminate the influence of diffraction effects and enhance transverse resolution. On this basis, the time-domain sparse deconvolution and frequency-domain synthetic aperture focusing methods mentioned above are combined to enhance the resolution of ultrasonic TOFD imaging. The simulation and experimental results indicate that this technique can outline the shape of defects with fine detail and improve image resolution by about 35%.

Ultrasonic phased array total focusing method (TFM) has been widely used in the field of non-destructive testing (NDT) as an advances post-processing imaging algorithm. It shows great potential in defect detection and characterization. However, due to the small number of excitation elements and large amount of collecting data, its temporal resolution and imaging efficiency need to be improved. To overcome these disadvantages, an ultrasonic phased array total focusing method based on sparse deconvolution has been proposed in this paper. Based on the actual situation of ultrasonic testing, a regularized sparse deconvolution model has been established. The Bayesian theory is used to add low-probability prior information to the deconvolution model. At the same time, the sparse deconvolution optimization algorithm proposed in this paper is used to solve the sparse deconvolution model. Compared with ordinary phased array total focusing method, this algorithm reduced the calculation amount and improved the temporal resolution. The performance of the proposed method is proved by simulation and experimental comparison. The results verify that the proposed method has higher resolution and imaging efficiency.

Surface curvature and distances from the transducer will have an effect on the amplitude of ultrasonic wave. When using the amplitude of ultrasonic signal to quantitatively size the flaws, a correction is necessary to assure all ultrasonic signal amplitude is based on the echo signal from flat surface. Hence, a deep learning method is proposed to adjust the signal amplitude which enters arbitrary curved surface. A deep learning model will adjust the signal amplitude as the radius of surface, distance from the transducer and attenuation coefficient of material. Then, the ultrasonic signal is imaged on the basis of corrected ultrasonic data. Compared to the ultrasonic imaging based on the raw data, the quantitative evaluation of flaw size is more precise by using the deep learning method. The deep learning model is trained by the CIVA simulation data. Ultimately, the model is verified by the comparisons between prediction and experimental data. The proposed method is adaptable to various curvature, distance, and material, which avoids manufacturing multiple standard specimens to satisfy lower costs and shorter cycle.

Ultrasonic phased array total focusing method (TFM) has been widely used in the field of non-destructive testing (NDT) as an advances post-processing imaging algorithm. It shows great potential in defect detection and characterization. However, temporal resolution of TFM is limited by transducer frequency, and the large amount of collecting data leads to low imaging efficiency. To overcome these disadvantages, an ultrasonic phased array total focusing method based on sparse deconvolution has been proposed in this paper. Based on the actual situation of ultrasonic testing, a regularized sparse deconvolution model has been established. The Bayesian theory is used to add low-probability prior information into the deconvolution model. Meanwhile, the sparse deconvolution optimization algorithm proposed in this paper is used to solve the sparse deconvolution model. Compared with ordinary phased array total focusing method, this algorithm reduces the calculation amount and improves the temporal resolution. The performance of the proposed method is proved by simulation comparisons. The results verify that the proposed method has higher temporal resolution and imaging efficiency.

Ultrasonic TOFD imaging, as an important non-destructive testing method, has a wide range of applications in pipeline girths weld inspection and testing. Due to the limited bandwidth of ultrasonic transducers, near-surface defects in the weld are masked and cannot be recognized, resulting in poor longitudinal resolution. Affected by the inherent diffraction effect of scattered acoustic waves, defects image has noticeable trailing, resulting in poor transverse resolution of TOFD imaging, and making quantitative defect detection difficult. In this paper, based on the assumption of sparseness of ultrasonic defects distribution, by constructing a convolutional model of the ultrasonic TOFD signal, Orthogonal Matching Pursuit (OMP) sparse deconvolution algorithm is utilized to enhance the longitudinal resolution. Based on synthetic aperture acoustic imaging model, in wavenumber domain, back propagation inference is implemented through phase transfer technology to eliminate the influence of diffraction effects and enhance transverse resolution. On this basis, the time domain sparse deconvolution and frequency domain synthetic aperture focusing methods mentioned above are combined to enhance the resolution of ultrasound TOFD imaging. The simulation and experimental results indicate that this technology can outline the trend of defects with fine stripes and reduce the image resolution about 35%.