Acoustic Emission Monitoring of Fatigue Damage in Steel Materials for Marine Applications

Master thesis (2024)

Authors

A. Gautam Mechanical Engineering

Contributors

Lotfollah Pahlavan Ship and Offshore Structures - Mechanical, Maritime and Materials Engineering (mentor)
C. Saccone Ship and Offshore Structures - Mechanical, Maritime and Materials Engineering (mentor)
Henk den Besten Ship and Offshore Structures - Mechanical, Maritime and Materials Engineering (graduation committee member)
Faculty
Mechanical Engineering
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Published Date

05-09-2024

Language

English

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Faculty

Mechanical Engineering

Abstract

This thesis investigates the feasibility and effectiveness of Acoustic Emission (AE) methods for monitoring fatigue crack growth in metallic materials, with the aim of enhancing predictive capabilities and understanding of crack propagation under cyclic loading. The research specifically examines the correlation between various AE parameters—such as amplitude, count rate, energy rate, and entropy—and fatigue crack growth rates, using a multi-parametric approach.

Experiments were conducted on multiple specimens under different loading conditions, and both time-domain and frequency-domain AE parameters were analyzed. The study found that parameters like energy rate and rise angle were particularly effective in detecting specific stages of fatigue crack growth, while count rate and amplitude provided consistent indicators of crack initiation and progression. However, the study also highlighted limitations in the use of filtering techniques, such as SNR and amplitude filters, which can inadvertently remove crucial AE signals.

The findings suggest that while AE methods have potential for accurately monitoring fatigue crack growth, their effectiveness is influenced by the choice of AE parameters and the management of noise. To improve accuracy, the study recommends further research that includes a broader range of specimens, explores additional AE parameters, integrates complementary techniques such as Digital Image Correlation (DIC), and applies advanced analytical methods like machine learning. Future research should also consider the impact of environmental factors, such as corrosion fatigue, particularly in marine environments where realistic AE data is critical.

Overall, this study contributes to the broader understanding of AE monitoring for fatigue damage, laying a foundation for future research and practical applications, while acknowledging the need for further refinement and validation of AE techniques across diverse materials and conditions.

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