The ultrasonic phased array total focusing method (TFM) has the advantages of full-range dynamic focusing and high imaging resolution, but the problem of long imaging time limits its practically industrial applications. To reduce the imaging calculation demand of TFM, the locations of active array elements in the sparse array are optimized by combining almost different sets with the genetic algorithm (ADSGA), and corrected based on the consistency of the effective aperture with the equivalent point diffusion function. At the same time, to further increase the imaging efficiency, a sparse-TFM image with lower resolution is obtained by reducing the number of focus points and then interpolated by the new edge-directed interpolation algorithm (NEDI) to obtain a high quality sparse-TFM image. Compared with TFM, the experimental results show that the quantitative accuracy of the proposed method is only decreased by 1.09% when the number of sparse transmitting elements reaches 8 for a 32-element transducer, and the imaging speed is improved by about 16 times with the same final pixel resolution.

A method based on the one-dimensional local binary pattern (1-D LBP) algorithm to extract features of ultrasonic defect signals and perform multi-class defect classification was proposed. The ultrasonic defect echo signals were first decomposed into wavelet coefficients by the wavelet packet decomposition. The 1-D LBP algorithm was employed to extract LBP features of components at low and high frequencies, respectively. Subsequently, these LBP statistical feature sets were regarded as feature vectors of defect classification. Weld defects were then classified automatically by using the radial basis function support vector machine. Defects of slag inclusion, porosity and incomplete penetration in a steel plate butt weld were used for experiments and feature extraction and defect classification were performed. The results show that the class separability of 1-D LBP features used for defect classification is superior to that of the traditional features. Moreover, the accuracy of defect classification reached 98.3%, providing an efficient tool for ultrasonic defect classification.