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Study of crack initiation and propagation from an electrochemically generated corrosion damage monitored by ultrasonic technique.

Communication sans acte
Author
NICARD, Cyril
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
ccRÉBILLAT, Marc
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
DEVOS, Olivier
1002421 Institut de Mécanique et d'Ingénierie de Bordeaux [I2M]
ccEL MAY, Mohamed
1002421 Institut de Mécanique et d'Ingénierie de Bordeaux [I2M]
ccMECHBAL, Nazih
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]

URI
http://hdl.handle.net/10985/26940
Date
2025-09

Abstract

Corrosion remains a critical issue in various industrial sectors, often being underestimated until significant damage occurs and heavy maintenance operation has to be carried out. Early detection and intervention can drastically extend asset life and reduce maintenance costs. Non-destructive testing (NDT) methods, widely used in the aeronautical industry [1], have proven effective in detecting structural damage, though they typically require human involvement. Recent advances in ultrasonic techniques, particularly the use of piezoelectric transducer (PZT) networks, offer promising alternatives to shift towards a condition-based maintenance approach. Building upon earlier work [2] that developed an ultrasonic Lamb wave technique for detecting electrochemically induced localized corrosion pit, this study expands the approach by integrating both electrochemical and mechanical analyses of crack initiation and propagation on Aluminum 2024 alloy. In our work, PZT discs generate and receive ultrasonic signals to monitor the evolution of a single corrosion and mechanical fatigue damage. This talk presents the results of our experiments, demonstrating how the combination of electrochemical control and ultrasonic monitoring provides a reliable method for tracking corrosion and crack growth. Embedding these sensors directly into strategic point of structures for continuous monitoring will reduce human intervention/error while generating data that could train machine learning algorithms for predictive maintenance strategies in the future. [1] S. Benavides, Corrosion in the aerospace industry, 2009. [2] C. Nicard, M. Rébillat, O. Devos, M. El May, F. Letellier, S. Dubent, M. Thomachot, M. Fournier, P. Masse, N. Mechbal, In-situ monitoring of µm-sized electrochemically generated corrosion pits using Lamb waves managed by a sparse array of piezoelectric transducers, Ultrasonics, Volume 147, 2025, 107527, ISSN 0041-624X, https://doi.org/10.1016/j.ultras.2024.107527

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