Characterization of pitting corrosion of stainless steel using artificial neural networks

Jiménez‐Come, María Jesús; Turias, Ignacio J.; Ruiz-Aguilar, Juan Jesús; Vilches, Francisco Javier Trujillo

doi:10.1002/maco.201408173

Cited by 16 publications

(6 citation statements)

References 35 publications

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“…Artificial neural networks (ANNs) have been widely applied in different fields of science and technology due to their efficiency in representing non-linear processes and its accuracy predictions. Many types of ANN´s models have been proposed and evaluated in different applications, such as the prediction of mechanical properties, corrosive resistance, particle sizes, and even applied in Large Hadron Collider (LHC) [32]- [38]. In this work the proposed ANN consist of one hidden layer and 12 neurons.…”

Section: Methodsmentioning

confidence: 99%

Prediction of the stacking fault energy in austenitic stainless steels using an artificial neural network

Román¹,

Campillo²,

Sarmiento³

et al. 2019

IJETR

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Stacking fault energy (SFE) is an important parameter to be considered in the design of austenitic stainless steels (SS) due to its influence on magnetic susceptibility, atomic order changes and intergranular corrosion resistance. An extensive review of specialized literature was examined in order to understand the different methods that have been developed for the calculation of SFE. Characterization by transmission electron microscopy (TEM), linear expressions from data processing and first-principles quantum mechanics approximations are some techniques that have been used for this objective. In the present work a feed forward backpropagation artificial neural network (ANN) was developed to predict the SFE within given specific ranges of chemical compositions of austenitic SS. The experimental data were extracted and analyzed from a research work reported by Yonezawa et al [1], for three different heat treatment conditions. The model predicts SFE values with a correlation coefficient of 0.99, which reduce the error when is compared with other works in the literature. Index Terms-Neural network, Stacking fault energy, Austenitic stainless steel.

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Section: Methodsmentioning

confidence: 99%

Prediction of the stacking fault energy in austenitic stainless steels using an artificial neural network

Román¹,

Campillo²,

Sarmiento³

et al. 2019

IJETR

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“…Therefore, how to accurately quantify such underlying relationships is still a challenge. (2) With the development of artificial intelligence, some machine learning models, including artificial neural networks (ANNs) and support vector machines (SVMs), have been widely used in corrosion science [16][17][18][19]. However, these models are not suitable for visually measuring the relationships between key factors and atmospheric corrosion because of their incapable interpretability.…”

Section: Papers Conditionmentioning

confidence: 99%

Data Mining to Atmospheric Corrosion Process Based on Evidence Fusion

Meng

Zhang²,

Wang³

et al. 2021

Materials

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An electrical resistance sensor-based atmospheric corrosion monitor was employed to study the carbon steel corrosion in outdoor atmospheric environments by recording dynamic corrosion data in real-time. Data mining of collected data contributes to uncovering the underlying mechanism of atmospheric corrosion. In this study, it was found that most statistical correlation coefficients do not adapt to outdoor coupled corrosion data. In order to deal with online coupled data, a new machine learning model is proposed from the viewpoint of information fusion. It aims to quantify the contribution of different environmental factors to atmospheric corrosion in different exposure periods. Compared to the commonly used machine learning models of artificial neural networks and support vector machines in the corrosion research field, the experimental results demonstrated the efficiency and superiority of the proposed model on online corrosion data in terms of measuring the importance of atmospheric factors and corrosion prediction accuracy.

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“…It was determined that only ANN provided precise predictions with a low mean relative error compared to statistical approaches. Some interesting results to predict the pitting corrosion for AISI 316 using ANN, for example in 29 corrosion modeling was developed by considering various environmental variables and in 30 with different classification models. The pitting corrosion of SS 304 by seawater containing Cl for construction purposes was discussed in another work 31 .…”

Section: Introductionmentioning

confidence: 99%

Neural network based corrosion modeling of Stainless Steel 316L elbow using electric field mapping data

Memon

Imran

Alhems

2023

Preprint

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Stainless steel (SS) is used in many industrial applications that demand superior corrosion resistance. Modeling its corrosion behavior in common structural and various operational scenarios is beneficial to provide wall-thickness (WT) information, thus leading to a predictive asset integrity regime. In this spirit, modeling of SS 316L corrosion behavior using artificial neural networks (ANNs) is presented, whereby saline water at different concentrations is flown through an elbow structure at different flow rates and salt concentrations. Voltage, current, and temperature data are recorded hourly using electric field mapping (EFM) pins installed on the elbow surface and used for training the model. The performance of corrosion modeling is verified by comparing the predicted WT with actual measurements obtained from experimental tests. Moreover, a concise account of the observed scale formation is also reported.‏

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Characterization of pitting corrosion of stainless steel using artificial neural networks

Cited by 16 publications

References 35 publications

Prediction of the stacking fault energy in austenitic stainless steels using an artificial neural network

Prediction of the stacking fault energy in austenitic stainless steels using an artificial neural network

Data Mining to Atmospheric Corrosion Process Based on Evidence Fusion

Neural network based corrosion modeling of Stainless Steel 316L elbow using electric field mapping data

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