Numerical simulation study on quantitative detection of surface defect width and depth based on laser surface acoustic waves

Aiming at the quantitative detection challenges of surface defect depth and width, this study proposes a quantitative detection method based on the reflection and diffraction theory of laser-excited surface acoustic wave (SAW), which combines reflected energy amplitude and time-of-flight of characteristic signals. Through bilateral excitation-detection and analysis of surface wave propagation, the measurement of defect width is achieved. By integrating linear fitting of reflected surface wave amplitude with the time-of-flight of oscillation waves (M wave and Q wave), a segmented depth detection model is established, effectively extending the detection range and improving accuracy. To enhance the robustness of this method in a noisy environment, the particle swarm optimization-variational mode decomposition (PSO-VMD) algorithm is introduced. The composite index constructed by permutation entropy and mutual information is used as the fitness function to achieve adaptive optimization of the decomposition parameters. Furthermore, sample entropy thresholding is applied to screen valid modal components, strengthening the ability to extract characteristic signals in noisy environments. Finite element simulation results demonstrate that this method can achieve quantitative characterization of defect width and depth, with maximum errors of 7.8% and 9.5%, respectively, providing a feasible solution for the quantitative characterization of laser ultrasonic surface defects.