Ultrasonic phased array sparse-TFM imaging based on deep learning and genetic algorithm

As ultrasonic phased array total focus method (TFM) imaging technology can achieve full range of dynamic focusing with clear imaging and strong ability to characterize defects, TFM imaging algorithm has become the gold standard for testing other post-processing algorithms. However, due to the large amount of data and time-consuming calculation, the TFM imaging has limited the application in some industrial fields. With the reduction of the number of phased array transmitting elements, the imaging quality gets worse. To improve the imaging efficiency of the TFM algorithm and ensure the imaging quality, this paper proposes a method combining Siamese Convolutional Neural Network (SCNN) and Genetic Algorithm (GA) to obtain an optimal sparse array layout, to approximate the imaging effect of the full array with limited effective elements. After selection of an appropriate sparsity ratio, GA is used to optimize the sparse array layout. The sparse array elements emit ultrasonic waves, and the full array elements receive echo signals for imaging. SCNN is trained by a self-built industrial defect dataset, to output the similarity between the sparse-TFM image and the full-array TFM image. The similarity is used as an objective evaluation index to evaluate the imaging effect. The optimal sparse array layout is proposed by combining subjective evaluation with objective evaluation.