Determination of SS321 pitting stage in FeCl3 solution based on electrochemical noise measurement data using artificial neural network

Abstract

Artificial neural network (ANN) was used successfully to determine the pitting stage of stainless steel 321 (SS321) based on electrochemical noise (ECN) data. The ANN inputs were the parameters derived from the analysis of ECN data. The target values were the pitting stage of SS321 in FeCl3 solution, which were determined using galvanostatic electrochemical impedance spectroscopy (GEIS) tests. The ANN was validated by using a large number of available experimental data. Four approaches have been used for ANN development. The approaches differ in the number and type of inputs. The results showed that the best performance of ANN was achieved when all ECN analysis parameters were used as ANN inputs. These ECN analysis parameters were derived from the time domain, the frequency domain and the time-frequency domain analysis methods. Based on the results of this study, ANN has a very good performance in determining the pitting stage based on ECN analysis results.