This research aims to enhance the industrial viability of laser-ultrasonic full-matrix capture, which is often hindered by large data requirements and inefficient data acquisition. We present a novel framework for intelligent optimization imaging based on a multi-mode sparse array, with its configuration intelligently designed through Dynamic Multi-Swarm Particle Swarm Optimization (DMS-PSO). This method innovatively introduces the four ultrasonic propagation modes systematically into the sparse array optimization process, constructing a physics-driven multi-mode collaborative optimization model. The designed DMS-PSO algorithm synchronously optimizes the selection of arrays and the allocation of their operating modes, aiming to achieve an optimal balance between high sensitivity in the imaging region and uniform spatial coverage. Experimental results indicate that at a 20% sparsity rate, the multi-modal fusion method achieves a mean sensitivity of 4.08 with a standard deviation as low as 1.60, significantly outperforming single-mode optimization. Imaging of different types of submillimeter defects demonstrates that this method maintains excellent imaging performance across a sparsity range of 5% to 30%. Specifically, side drilling holes (SDH) defects exhibit imaging quality comparable to full-matrix results at 20% sparsity, while blind holes (BH) defects can also be effectively identified. Even at an extremely low sparsity rate of 5%, critical defect features remain identifiable. This study demonstrates the potential of the proposed multi-mode sparse array optimization method in enhancing the efficiency of laser ultrasonic testing. The results provide a promising technical basis for achieving online and rapid detection of micro-defects, although further validation on a broader range of materials and defect configurations is required to establish generalizability.
原文链接
分享
引用
