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In this work, acoustic emission (AE) monitoring was correlated with in-situ optical microscopy observation and electron backscatter diffraction (EBSD) measurements to investigate the evolution of a single stress corrosion crack in SUS420J2 stainless steel subjected to chloride droplet corrosion. A single dominant crack evolution was observed to transition from a slow initiation of active path corrosion-dominant cracking to a rapid propagation of hydrogen-assisted cracking. The initiation-to-propagation was concomitant with a significant increase in the number of AE events. In addition, a cluster analysis of the AE features including traditional waveform parameters and fast Fourier transform (FFT)-derived frequency components was performed using k-means algorithms. Two AE clusters with different frequency levels were extracted. Correlated with the EBSD-derived kernel average misorientation (KAM) map of crack path, low-frequency AE cluster was found to correspond with the location of plastic deformation in the propagation region. High-frequency AE cluster is supposed to be from the cracking process. The correlation between AE feature and SCC progression is expected to provide an AE signals-based in-situ insight into the SCC monitoring.

In this study, a method for the prediction of cyclic stress–strain properties of ferrite-pearlite steels was proposed. At first, synthetic microstructures were generated based on an anisotropic tessellation from the results of electron backscatter diffraction (EBSD) analyses. Low-cycle fatigue experiments under strain-controlled conditions were conducted in order to calibrate material property parameters for both an anisotropic crystal plasticity and an isotropic J2 model. Numerical finite element simulations were conducted using these synthetic microstructures and material properties based on experimental results, and cyclic stress-strain properties were calculated. Then, two-point correlations of synthetic microstructures were calculated to quantify the microstructures. The microstructure-property dataset was obtained by associating a two-point correlation and calculated cyclic stress-strain property. Machine learning, such as a linear regression model and neural network, was conducted using the dataset. Finally, cyclic stress-strain properties were predicted from the result of EBSD analysis using the obtained machine learning model and were compared with the results of the low-cycle fatigue experiments.

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