Application of Acoustic Emission measurements to monitor the initiation and the propagation of single or multiple Stress Corrosion Cracks (SCC)
O. Al Haj1  2, J. Bolivar2, A. Proust111, M. Fregonese22
1Mistras Group, SAS19, Sucy-en-Brie [France]
2National Institute of Applied Science ( INSA-Lyon)- University of Lyon65, Villeurbanne [France]
Acoustic Emission (AE)
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The aim of this study is to monitor initiation and propagation of single or a colony of intergranular SCC defects using the AE. The main goal is a better understanding behavior and propagation of multiple cracks for a better evaluation of the influence of cracks interactions on failure process. For nuclear applications, the final aim is to propose new integrity criteria.
Experimental tests were performed using mill annealed and sensitized samples of alloy 600, which were immersed in a 0.01 M of potassium tetrathionate solution. The experiments were also monitored by in-situ digital image correlation (DIC) and electrochemical noise (EN) measurements.
Three stages were identified in the propagation and the growth of both a single crack and a colony. During stage I, no crack was detected and the main AE was related to intergranular corrosion (ISCC) and/or anodic dissolution.
Stage II was characterized by surface crack propagation induced by anodic dissolution. The AE signals recorded during this phase were generated by ISCC processes.
During stage III, the interactions between the cracks became more intense (coalescences) with an implication of plastic strain (macro cracking, plastic deformation,…). Such processes were identified as the source of AE activity during this last stage.
The implementation of AE technique coupled with DIC and EN measurements contributed to the understanding of mechanisms involved in short stress corrosion cracks interactions which was determinant for the modeling of the colony behavior.
MATETPRO ANR-12-RMNP-0020 (ECCOFIC). Thanks to LaMCoS, Institut of Corrosion, Andra, AREVA, P. Combrade.
 
| EWGAE 2018
Session: Corrosion | 2019-01 |
Classification of acoustic emission signals using wavelets and Random Forests: application to localized corrosion
N. Morizet, N. Godin9, J. Tang, M. Fregonese2, B. Normand
National Institute of Applied Science ( INSA-Lyon)- University of Lyon65, Villeurbanne [France]
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This work aims at proposing a novel approach to classify acoustic emission (AE) signals deriving
from corrosion damage, even if embedded into a noisy environment. Tests involving noise and crevice corrosion
are conducted, by preprocessing the waveforms including wavelet denoising and extracting a rich set of features as
input of the Random Forest algorithm. To this end, a software called RF-CAM has been developed. Results show
this approach is very efficient on ground truth data and is also very promising on real data, especially for its
reliability, performance and speed, which are serious criteria for the chemical industry.
| Journal-AE
Session: Volume 34, 2016 | 2017-10 |
