Automated Rayleigh-Wave Nonlinear Acoustic Platform for Real-Time Fatigue Monitoring in Metallic Materials

This paper presents a fully automated platform for real-time monitoring of fatigue-induced microstructural changes in metallic materials, using Rayleigh surface waves and Laser Doppler Vibrometry (LDV). The system integrates ultrasonic excitation, non-contact optical sensing, and high-speed signal processing in a unified LabVIEW environment. Rayleigh waves are generated via a contact transducer, while LDV captures surface vibrations with sub-nanometric velocity resolution, ensuring repeatability and eliminating coupling variability. The software automates synchronization, deterministic data acquisition, filtering, FFT analysis, and extraction of nonlinear coefficients (β₂, β₃) at high execution rates without the need for post-processing. Experimental validation under cyclic loading revealed a clear sensitivity hierarchy: the Rayleigh wave velocity remained invariant, the acoustic attenuation responded gradually, while the nonlinear parameters exhibited the earliest and steepest response to fatigue damage, confirming their superiority as early-stage indicators. The system offers low-latency timing, long-term stability, and modular design, establishing a robust data-streaming foundation that can support future integration with digital twin frameworks and machine learning models. Furthermore, the acoustic findings were successfully cross-validated using Infrared Thermography, which confirmed the critical damage transition phase. This work bridges nonlinear acoustics and software automation, providing a scalable diagnostic solution for predictive maintenance within structural health monitoring systems.