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

Master Thesis (2024)
Author(s)

A. Gautam (TU Delft - Mechanical Engineering)

Contributor(s)

L Pahlavan – Mentor (TU Delft - Ship and Offshore Structures)

C. Saccone – Mentor (TU Delft - Ship and Offshore Structures)

JH den Besten – Graduation committee member (TU Delft - Ship and Offshore Structures)

Faculty
Mechanical Engineering
More Info
expand_more
Publication Year
2024
Language
English
Graduation Date
05-09-2024
Awarding Institution
Delft University of Technology
Programme
Offshore and Dredging Engineering
Faculty
Mechanical Engineering
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

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.

Files

MSc_Thesis_Animesh.pdf
(pdf | 98.7 Mb)
License info not available