Investigating the Origin of Acoustic Signals in AE Monitoring of Stainless Steel Pitting Corrosion via Combined Electrochemical Techniques

Master Thesis (2025)
Author(s)

T. Hua (TU Delft - Mechanical Engineering)

Contributor(s)

J.M.C. Mol – Graduation committee member (TU Delft - Team Arjan Mol)

Lotfollah Pahlavan – Graduation committee member (TU Delft - Ship and Offshore Structures)

A.M. Homborg – Mentor (TU Delft - Team Arjan Mol)

R. Habiyaremye – Graduation committee member (TU Delft - Ship Hydromechanics)

Faculty
Mechanical Engineering
More Info
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Publication Year
2025
Language
English
Graduation Date
30-09-2025
Awarding Institution
Delft University of Technology
Programme
['Materials Science and Engineering']
Faculty
Mechanical Engineering
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Abstract

Stainless steels are widely deployed in marine, chemical-processing, and energy infrastructures, yet their long-term integrity is threatened by chloride-induced pitting corrosion—a highly localized process that initiates stochastically and accelerates unpredictably. Conventional electrochemical methods (e.g., polarization scans, impedance) offer bulk averages and often miss the earliest, transient signatures of pit nucleation. Acoustic emission (AE) monitoring can, in principle, capture those fast microevents, but the physical origin of AE during corrosion remains debated (e.g., hydrogen bubble rupture vs. passive-film breakdown vs. pit growth), limiting interpretability and trust in early-warning use. To investigate this gap, AE was integrated with electrochemical noise (EN) in a passive, time-aligned framework to directly correlate acoustic bursts with electrochemical transients and strengthen source attribution. Using AISI 304 stainless steel exposed to 3.5 wt% NaCl at pH 2 under open-circuit conditions, we observe clear temporal coincidence between burst-type AE events and EN spikes. As corrosion evolves, AE amplitudes and durations increase, indicating a transition from pit nucleation to stable growth; microscopy confirms localized pits (~20–80 μm) with surface deposits. Sensor resonance (~150–170 kHz) shapes observed peak frequencies, highlighting the need for multi-parameter interpretation. The proposed AE+EN approach is promising in earlier, more reliable detection and mechanism discrimination for pitting, and could be extended to other alloy–environment systems.

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