In-situ monitoring of µm-sized electrochemically generated corrosion pits using Lamb Waves managed by a sparse array of piezoelectric transducers

Abstract

al components, roughening the outer surface, loosening fasteners, hastening cracking, and facilitating the entry of water into electronic fixtures. In 2016, the combined commercial aircraft fleet operated by European airlines was around 7900 airplanes. The annual corrosion cost for this number of aircraft was estimated by the US National Association of Corrosion Engineers to 2.2 B$, which includes corrosion maintenance at 1.7 B$ and downtime due to corrosion to 0.3 B$. Anticipating corrosive conditions ahead of time can lead to significant cost savings and less aircraft downtime. It is estimated that savings between 15% and 35% of the cost of corrosion could be realized [1]. In order to limit corrosion issues, a typical aeronautic structure is made of qualified steel or aluminium alloys eventually protected from the environment by treating the surface with adequate coatings. It is generally expected that the protective surface is perfectly flawless over the whole structure. But on a large structure undergoing everyday service this cannot be the case. Impact damage during service life is a common occurrence of coating failure. Damage during maintenance (tools and split fluids) can also occur as well as paint cracking at high stress points around joints. From these initial premises, corrosion pits can start and threaten the structural integrity at a global level, thus motivating the need for in-situ pitting corrosion monitoring technologies. Industrially speaking, an ideal corrosion monitoring technology should allow to monitor large-scale structures, to automate measurements, to detect corrosion pits from their premises, and to exhibit a high correlation between sensors measurements and the size and locations of corroded areas. Although various effective non-destructive testing (NDT) methods have been developed to monitor corrosion [2], they remain limited in their ability to detect and assess corrosion premises and to reliably size a given corrosion damage. The first reason for that is technological. Eddy currents allow local monitoring (one side), are slow, difficult to calibrate, and remain a small-scale approach. Standard ultrasonic methods (A-scan and C-scan) are again small scale, rely on coupling fluid & human intervention and are thus rather slow. Optical methods (visual inspection, liquid penetrant) are large scale but can cope only with surface features that can be visually inspected and remain difficult to interpret. Radiography (X-rays, tomography) is small scale and relies on extensive hardware that cannot be used on the field. The second reason is methodological. Actual monitoring procedures are referred as planned maintenance and imply regular parts inspection by means of visual inspection and non-destructive testing through human highly qualified operators. Many of these inspections are (fortunately) unnecessary and as they rely on human intervention, their reliability is then subject to confidence intervals that establish a certain probability of detection. Moving from planned maintenance to condition based maintenance (CBM), i.e. maintenance only when necessary, seems mandatory to solve these issues. Monitoring in real time and autonomously the health state of structures is thus of high interest in this context. Such a process is referred to as Structural Health Monitoring (SHM) [3, 4]. To achieve this goal, structures become “smart” in the sense that they are equipped with sensors, actuators, and algorithms that allow them to state autonomously regarding their own health. One can compare smart structures with the human body which, thanks to its various senses and nerves, is able to assess if it has been hurt, where it has been hurt, and to estimate how severe it is. Following this analogy, the SHM process is classically decomposed into four steps: damage detection, localization, classification, and quantification [5]. There is thus a real need for a large scale, automated, sensitive, low cost and embedded technology to develop corrosion monitoring SHM methodologies of aeronautic parts. Ultrasonic technologies relying on Lamb waves have been shown to be extremely sensitive to corrosion damage [6, 7] and can be easily automated thanks to permanently embedded piezoelectric elements (PZT) networks [8]. In the present work, the focus is thus put on corrosion monitoring methods based on ultrasonics waves applied experimentally to plate-like structures. A literature on that topic survey has shown that ultrasonic inspection method based on spare PZT transducers arrays are the most suitable from a practical point of view but that they have never been validated on small corrosion pits realistic of actual corrosion (from 10 µm to 100 µm) but rather on large generalized corrosion areas (in cm). The reason explaining the fact that it was yet not possible to generate a single corrosion pit in a controlled manner and to simultaneously and in-situ record ultrasonic Lamb waves interacting with such a damage. The objective of this contribution is thus first to introduce an experimental setup allowing to control electrochemically the growth of a µm-sized corrosion pit and to simultaneously monitor it in situ by means of ultrasonic Lamb Waves emitted and received using a sparse array of PZT transducers. It will secondly be demonstrated that such measurements can be post-processed to compute damage indexes (DIs) that correlate very well with actual corrosion pit radius as estimated electrochemically and that using a linear model relating DI values to corrosion pit radius, corrosion pit size from 10 µm to 150 µm can be reliably detected, located, and their upcoming size extrapolated. [1] S. Benavides, Corrosion control in the aerospace industry, Elsevier, 2009. [2] S. J. Harris, M. Mishon and M. Hebbron, "Corrosion sensors to reduce aircraft maintenance," in Workshop on enhanced aircraft platform availability through advanced maintenance concepts and technologies. , 2006. [3] D. Balageas, C.-P. Fritzen et A. Güemes, Structural health monitoring, vol. 493, Wiley Online Library, 2006. [4] C. R. Farrar et K. Worden, «An introduction to structural health monitoring,» Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol. 365, pp. 303-315, 2007. [5] K. Worden, C. R. Farrar, G. Manson et G. Park, «The fundamental axioms of structural health monitoring,» Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol. 463, pp. 1639-1664, 2007. [6] C. Jirarungsatian and A. Prateepasen, "Pitting and uniform corrosion source recognition using acoustic emission parameters," Corrosion Science, vol. 52, pp. 187-197, 2010. [7] P. Kudela, M. Radzienski, W. Ostachowicz and Z. Yang, "Structural Health Monitoring system based on a concept of Lamb wave focusing by the piezoelectric array," Mechanical Systems and Signal Processing, vol. 108, pp. 21-32, 2018. [8] S. Grondel, C. Delebarre, J. Assaad, J.-P. Dupuis and L. 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