Real-time monitoring of the corrosion behaviour of the 304SS in HCl solution using BPNN with joint image recognition and electrochemical noise

Electrochemical noise has an important reference role in the study of early corrosion; however, a direct correlation between the microscopic morphology and the electrochemical noise of the corrosion process is lacking. This study uses a back propagation artificial neural network (BPNN) optimized by...

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Vydané v:Corrosion science Ročník 228; s. 111779
Hlavní autori: Zhang, Haofeng, Wu, Zhiqin, Chen, Yang, Feng, Kaixuan, Yan, Hong, Song, Honggun, Luo, Chao, Hu, Zhi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.03.2024
Predmet:
304SS
Artificial Neural Networks
Electrochemical noise
In-situ observation
Intelligent optimization algorithm
Localized corrosion
Electrochemical noise
Localized corrosion
304SS
Artificial Neural Networks
In-situ observation
Intelligent optimization algorithm
ISSN:0010-938X
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Shrnutí:Electrochemical noise has an important reference role in the study of early corrosion; however, a direct correlation between the microscopic morphology and the electrochemical noise of the corrosion process is lacking. This study uses a back propagation artificial neural network (BPNN) optimized by three intelligent optimization algorithms to real-time monitor the local corrosion of 304 stainless steel in HCl solution with joint recognition and feature extraction of corrosion images and electrochemical noise. The results show that the corrosion monitoring model established by optimized BPNN with genetic algorithms exhibits the highest convergence speed and accuracy. •Corrosion area of 304SS in HCl solution is identified by real-time image recognition.•EN trend is related to the type of corrosion and the diffusion rate of corroded area.•A clear correlation between image features and electrochemical noise is observed.•The description of corrosion process of 304SS is established by feature extraction.•The optimised BPNN with joint image feature and EN is useful in monitoring corrosion.
ISSN:0010-938X
DOI:10.1016/j.corsci.2023.111779