Optimization of selection matrix capture for micro defects laser ultrasound imaging using multi-parameter genetic algorithm

Abstract

Ultrasonic testing plays a crucial role in detecting early structural damage and identifying micro-defects, particularly in processes like additive manufacturing and welding. The full matrix capture (FMC) method, leveraging laser ultrasound technology, excels in imaging sub-millimeter micro defects. However, its extensive data acquisition time hinders real-time imaging. To address this, a selection matrix capture approach is adopted to reduce data collection and enhance detection speed. Specifically, a multi-parameter genetic algorithm (MPGA) is proposed to optimize sparse array layouts. This optimization is based on theoretical detection sensitivity means and standard deviations, evaluating array layout quality. The imaging method combined multi-scale principal component analysis with phase weighting techniques. Experiments on sub-millimeter defects, including side drilling holes (SDH), blind holes (BH), and spherical holes (SH), were conducted. Results showed that, compared to random and uniform sparsity, the genetic algorithm optimized sparse array provided superior imaging as sparsity decreased. Effective defect detection was achieved with only 5 %–20 % of full matrix data.