Prediction of primary water stress corrosion crack growth rates in Alloy 600 using artificial neural networks

Shi, Jiangbo; Wang, Jihui; Macdonald, Digby D.

doi:10.1016/j.corsci.2014.12.007

Cited by 45 publications

(45 citation statements)

References 27 publications

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“…e results show that the finite element simulation of crack growth rate agrees with the theoretical calculation results under irradiation conditions; however, there are deviations in results under nonirradiation conditions [10]. Shi et al proposed an artificial neural network algorithm (ANN) for predicting the rate of SCC growth of Alloy 600. e results show that the crack propagation rate prediction results obtained by ANN are consistent with the experimental data [11]. Ritchie et al used the finite element method and the electrical analogue method to study the relationship between crack length and voltage drop of CT specimens.…”

Section: Introductionsupporting

confidence: 64%

An Approach to Estimate SCC Growing Rate in Slow Strain Rate Tensile Test Based on EPFEM

Wang

Xue

Cui

et al. 2019

Advances in Materials Science and Engineering

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The slow strain rate tensile test (SSRT) is a common means to investigate stress corrosion cracking (SCC) in key engineering structural materials of light-water reactors, and it is an important task to real-time monitor the crack growing length and rate of the specimen during the test. Because the specimen is placed in an autoclave with high-pressure and high-temperature water environment-simulated light-water reactor, the current potential drop method, which includes current potential drop (DCPD) and alternating current potential drop (ACPD), is the main means to real-time monitor crack growth rate in the SCC test. As a supplementary means to obtain the crack growth rate during the test, the SSRT process of nickel-based Alloy 600 CT specimens is investigated by using the elastic-plastic finite element method (EPFEM) in this paper. Based on the consideration that both the elastic-plastic deformation and crack length of the specimen would affect the relationship between the load and the displacement of the loading point during the SSRT test, the relationship between the loading point displacement caused by crack propagation ΔLc and plastic deformation ΔLp is separated by EPFEM. Then, the SCC crack growth rate and the real-time crack length are obtained. This proposed approach could be used to improve the test results in the SSRT test.

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Section: Introductionsupporting

confidence: 64%

An Approach to Estimate SCC Growing Rate in Slow Strain Rate Tensile Test Based on EPFEM

Wang

Xue

Cui

et al. 2019

Advances in Materials Science and Engineering

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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%

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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“…Many attempts have been made in order to imply ANN in different fields for a number of applications, like speech recognition [51] , [52], prediction of coating thickness [53], prediction of mechanical properties [54] , [55], weather prediction [56], pharmaceutical research [57], identification of cell behaviour [58], medical imaging [59], predicting corrosion behaviour [60] , [61] , [62] etc.…”

Section: Introductionmentioning

confidence: 99%

ANN prediction of corrosion behaviour of uncoated and biopolymers coated cp-Titanium substrates

et al. 2018

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The present study focuses on biopolymer surface modification of cp-Titanium with Chitosan, Gelatin and Sodium Alginate. The biopolymers were spin coated onto cp-Titanium substrate and further subjected to Electrochemical Impedance Spectroscopic (EIS) characterisation. Artificial Neural Network (ANN) was developed to predict the Open Cicuit Potential (OCP) values and Nyquist plot for bare and bioploymer coated cp-Titanium substrate. The experimental data obtained was utilised for ANN training. Two input paramters, i.e., substrate condition (coated or uncoated) and time period were considered to predict the OCP values. Back propogation Levenberg-Marquardt training algorithm was utilised in order to train ANN and to fit the model. For Nyquist plot, the network was trained to predict the imaginary impedance based on real impedance as a function of immersion periods using the Back Propagation Bayesian algorithm. The biopolymer coated cp-Titanium substrate shows the enhanced corrosion resistance compared to uncoated substrtaes. The ANN model exhibits excellent comparsion with the experimental results in both the cases indicating that the developed model is very accurate and efficiently predicts the OCP values and Nyquist plot.

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Prediction of primary water stress corrosion crack growth rates in Alloy 600 using artificial neural networks

Cited by 45 publications

References 27 publications

An Approach to Estimate SCC Growing Rate in Slow Strain Rate Tensile Test Based on EPFEM

An Approach to Estimate SCC Growing Rate in Slow Strain Rate Tensile Test Based on EPFEM

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

ANN prediction of corrosion behaviour of uncoated and biopolymers coated cp-Titanium substrates

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