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Automated Rayleigh-Wave Nonlinear Acoustic Platform for Real-Time Fatigue Monitoring in Metallic Materials.
Theodoti Z Kordatou1, Spyridoula G Farmaki1, Dimitrios A Exarchos1
1Mechanics, Smart Sensors & Nondestructive Evaluation (MSS-NDE) Laboratory, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece.
Sensors (Basel, Switzerland)
|May 27, 2026
Summary
This study introduces an automated system for real-time monitoring of material fatigue using surface waves and optical sensing. Nonlinear acoustic parameters proved most effective for early detection of microstructural damage.
Area of Science:
- Materials Science
- Nonlinear Acoustics
- Structural Health Monitoring
Background:
- Fatigue-induced microstructural changes in metallic materials are critical for structural integrity.
- Early detection of fatigue damage is essential for predictive maintenance and preventing catastrophic failures.
- Current monitoring methods often lack the sensitivity or real-time capabilities for early-stage damage assessment.
Purpose of the Study:
- To develop a fully automated platform for real-time monitoring of fatigue-induced microstructural changes.
- To investigate the efficacy of Rayleigh surface waves and Laser Doppler Vibrometry (LDV) for detecting early fatigue damage.
- To establish a scalable diagnostic solution for predictive maintenance in structural health monitoring.
Main Methods:
- Integration of ultrasonic excitation, non-contact Laser Doppler Vibrometry (LDV), and high-speed signal processing in a LabVIEW environment.
- Automated generation of Rayleigh surface waves and capture of surface vibrations with sub-nanometric resolution.
- Real-time extraction of nonlinear acoustic parameters (β2, β3) and analysis of acoustic attenuation and velocity.
Main Results:
- Nonlinear acoustic parameters (β2, β3) demonstrated the earliest and most significant response to fatigue damage, outperforming Rayleigh wave velocity and acoustic attenuation.
- The automated system provided low-latency, stable, and repeatable measurements without post-processing.
- Cross-validation with Infrared Thermography confirmed the critical damage transition phase identified by acoustic methods.
Conclusions:
- The developed automated platform effectively monitors fatigue-induced microstructural changes in real-time.
- Nonlinear acoustic parameters are superior early indicators of fatigue damage compared to traditional acoustic measurements.
- This work provides a scalable diagnostic solution for predictive maintenance, with potential for integration into digital twin and machine learning frameworks.
