Laser ultrasonic spatially resolved acoustic spectroscopy for grain size study based on Improved Variational Mode Decomposition (IVMD)

Abstract

Laser Ultrasonic Spatially Resolved Acoustic Spectroscopy (SRAS) is a technique used for detecting the grain size of metal surfaces. It involves exciting Surface Acoustic Waves (SAW) using a grated light source and employs optical deflection techniques to detect ultrasound. The differences in sound velocity between different grains are utilized for grain size characterization. However, this technique is susceptible to the influence of sample surface roughness, resulting in issues like low signal-to-noise ratio and waveform distortion. To address these challenges, a study was conducted on grain size characterization using Laser Ultrasonic SRAS based on Improved Variational Mode Decomposition (IVMD). This involved optimizing Variational Mode Decomposition (VMD) by introducing Permutation Entropy (PE), Weighted Spectral Kurtosis (WSK), and Dynamic Time Warping (DTW). Adaptive decomposition and selection of simulated surface wave signals were employed to achieve signal recovery. A comparison with Empirical Mode Decomposition (EMD) demonstrated that the proposed algorithm had higher noise robustness. The Laser Ultrasonic SRAS detection system was used to characterize the grain size of Ti-6Al-4V casting alloys, and the data were processed using the proposed algorithm. The results were consistent with optical microscopy images, further confirming the reliability and effectiveness of the improved algorithm. The research findings indicate that Laser Ultrasonic Spatially Resolved Acoustic Spectroscopy based on IVMD can effectively reduce the impact of noise on detection signals, thereby enhancing the detectability of grain size measurement.