Prediction of corrosion behaviour of Alloy 22 using neural network as a data mining tool

Kamrunnahar, M.; Urquidi‐Macdonald, Mirna

doi:10.1016/j.corsci.2010.11.028

Cited by 44 publications

(17 citation statements)

References 32 publications

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“…In recent years, with the development of machine-learning algorithms, many studies have used machine-learning technology to establish the corrosion model and to implement the prediction of the corrosion status [9][10][11][12][13][14][15][16][17]. For example, Kamrunnahar [9,10], Jiang [11], Shirazi [12], and Shi [13] used artificial neural networks (abbreviated as ANN) to build the corrosion behavior model [9,10] or prediction model [11][12][13] of one specific alloy material. Fang established a corrosion loss prediction model of metallic materials in an atmospheric environment based on support vector regression (abbreviated as SVR) [14].…”

Section: Introductionmentioning

confidence: 99%

“…Shi analyzed and built a prediction model of the corrosion density data using a hidden Markov chain method [17]. However, in most of the above works, the objective was just one single material [9][10][11][12][13][14]17] or had one single input variable [15,16]. When extended to the corrosion prediction of various materials in a variety of environmental conditions, there still exist some problems in the above methods, leading to poor performance on the datasets.…”

Section: Introductionmentioning

confidence: 99%

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Prediction and Knowledge Mining of Outdoor Atmospheric Corrosion Rates of Low Alloy Steels Based on the Random Forests Approach

Zhi

Zhang

et al. 2019

Metals

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The objective of this paper is to develop an approach to forecast the outdoor atmospheric corrosion rate of low alloy steels and do corrosion-knowledge mining by using a Random Forests algorithm as a mining tool. We collected the corrosion data of 17 low alloy steels under 6 atmospheric corrosion test stations in China over 16 years as the experimental datasets. Based on the datasets, a Random Forests model is established to implement the purpose of the corrosion rate prediction and data-mining. The results showed that the random forests can achieve the best generalization results compared to the commonly used machine learning methods such as the artificial neural network, support vector regression, and logistic regression. In addition, the results also showed that regarding the effect to the corrosion rate, environmental factors contributed more than chemical compositions in the low alloy steels, but as exposure time increases, the effect of the environmental factors will gradually become less. Furthermore, we give the effect changes of six environmental factors (Cl− concentration, SO2 concentration, relative humidity, temperature, rainfall, and pH) on corrosion with exposure time increasing, and the results illustrated that pH had a significant contribution to the corrosion of the entire process. The paper also dealt with the problem of the corrosion rate forecast, especially for changing environmental factors situations, and obtained the qualitative and quantitative results of influences of each environmental factor on corrosion.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction and Knowledge Mining of Outdoor Atmospheric Corrosion Rates of Low Alloy Steels Based on the Random Forests Approach

Zhi

Zhang

et al. 2019

Metals

View full text Add to dashboard Cite

show abstract

“…The model tried to find out the relationship between the composition of the alloy and the environmental conditions with the corrosion rate and the electrochemical potentials. Based on the highquality results, these authors continued their studies, and in 2011, they applied ANN models to estimate the weight loss and the crevice repassivation potential of metals (Kamrunnahar & Urquidi-Macdonald, 2011). One of the most recent application of ANNs on corrosion studies was developed by Hodhod and Ahmed (2014).…”

Section: Introductionmentioning

confidence: 99%

A two-stage model based on artificial neural networks to determine pitting corrosion status of 316L stainless steel

2016

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Motivated to reduce the costs incurred by corrosion in material science, this article presents a combined model based on artificial neural networks (ANNs) to predict pitting corrosion status of 316L austenitic stainless steel. This model offers the advantage of automatically determining the pitting corrosion status of the material. In this work, the pitting corrosion status was predicted, with the environmental conditions considered, in addition to the values of the breakdown potential estimated by the model previously, but without having to use polarization tests. The generalization ability of the model was verified by the evaluation using the experimental data obtained from the European project called "Avoiding Catastrophic Corrosion Failure of Stainless Steel". Receiver operating characteristic space, in addition to area under the curve (AUC) values, was presented to measure the prediction performance of the model. Based on the results (0.994 for AUC, 0.980 for sensitivity, and 0.956 for specificity), it can be concluded that ANNs become an efficient tool to predict pitting corrosion status of austenitic stainless steel automatically using this two-stage procedure approach.

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“…In addition, the sensitivity analysis showed that the temperature and conductivity contributed the most to the crack growth rate. Kamrunnahar et al developed an ANN model using the actual measurements of corrosion weight loss data of Alloy 22 to predict the future corrosion weight loss of this material. Results demonstrated a good agreement between the predicted and measured values under similar environmental and sampling conditions.…”

Section: Introductionmentioning

confidence: 99%

Risk assessment of failure of rock bolts in underground coal mines using support vector machines

Jiang

Craig

Crosky

et al. 2017

Appl Stoch Models Bus & Ind

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In recent years, there has been an increasing incidence of failure of rock bolts due to stress corrosion cracking and localized corrosion attack in Australian underground coal mines. Unfortunately, prediction of the risk of failure from results obtained from laboratory testing is not necessarily reliable because it is difficult to properly simulate the mine environment. An alternative way of predicting failure is to apply machine learning methods to data obtained from underground mines. In this paper, support vector machines are built to predict failure of bolts in complex mine environments. Feature transformation and feature selection methods are applied to extract useful information from the original data. A dataset, which had continuous features and spatial data, was used to test the proposed model. The results showed that principal component analysis-based feature transformation provides reliable risk prediction.

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Prediction of corrosion behaviour of Alloy 22 using neural network as a data mining tool

Cited by 44 publications

References 32 publications

Prediction and Knowledge Mining of Outdoor Atmospheric Corrosion Rates of Low Alloy Steels Based on the Random Forests Approach

Prediction and Knowledge Mining of Outdoor Atmospheric Corrosion Rates of Low Alloy Steels Based on the Random Forests Approach

A two-stage model based on artificial neural networks to determine pitting corrosion status of 316L stainless steel

Risk assessment of failure of rock bolts in underground coal mines using support vector machines

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