Prediction of corrosion behavior using neural network as a data mining tool

Kamrunnahar, M.; Urquidi‐Macdonald, Mirna

doi:10.1016/j.corsci.2009.10.024

Cited by 106 publications

(41 citation statements)

References 31 publications

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“…In this case, authors used an ANN model as a predictive model to establish a relationship between the Brought to you by | New York University Bobst Library Technical Services Authenticated Download Date | 10/8/15 6:33 AM RSW process of AISI 304 and the pitting potential of the RSW joint. Kamrunnahar and Urquidi-Macdonald (2010) applied a supervised neural network model to predict the corrosion behavior of metal alloys. 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.…”

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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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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“…In mining environments, water scarcity is one of the primary challenges to obtain a good quality groundwater source for mining activities, as mining areas are located in the countryside, such as in desert climates and high altitudes, where the local water reserve is limited. The utilisation of seawater or desalinated water is preferable in such conditions to continue the mining operation and development [11].…”

Section: Introductionmentioning

confidence: 99%

Stress-Induced Phase Transformation and Its Correlation with Corrosion Properties of Dual-Phase High Carbon Steel

Handoko

Pahlevani

Hossain

et al. 2019

JMMP

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It is well known that stress-induced phase transformation in dual-phase steel leads to the degradation of bulk corrosion resistance properties. Predicting this behaviour in high carbon steel is imperative for designing this grade of steel for more advanced applications. Dual-phase high carbon steel consists of a martensitic structure with metastable retained austenite which can be transformed to martensite when the required energy is attained, and its usage has increased in the past decade. In this study, insight into the influence of deformed microstructures on corrosion behaviour of dual-phase high carbon steel was investigated. The generation of strain-induced martensite formation (SIMF) by residual stress through plastic deformation, misorientation and substructure formation was comprehensively conducted by EBSD and SEM. Tafel and EIS methods were used to determine corrosion intensity and the effect of corrosion behaviour on hardness properties. As a result of the static compression load, the retained austenite transformed into martensite, which lowered its corrosion rate by 5.79% and increased the dislocation density and the length of high-angle grain boundaries. This study demonstrates that balancing the fraction of the martensite phase in structure and dislocation density, including the length of high-angle grain boundaries, will result in an increase in the corrosion rate in parallel with the applied compression load.

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“…At750°C aging process, compared with ferrous Alloy 625, γ'' 'phase precipitation and γ'' →δ phase is delayed, which have good organizational stability [8,19,20]. In the process of aging, the generation and evolution of γ'' phase, δ phase, precipitation in the grain boundary and alloy element in the grain boundary affects the performance of the alloy [10,21,22].…”

Section: Alloy 625 Structure In Aging Processmentioning

confidence: 99%

Long Term Aging Treatment Impact on Ultralow Iron Alloy 625 Intergranular Corrosion Property

Zhu¹,

Sui²,

Dai³

et al. 2017

Preprint

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This paper investigates the evolution of microstructures and precipitations of an ultra-low iron alloy 625 subjected to long term aging treatment by scanning electron microscope (SEM) and X-ray diffraction(XRD). Use ASTM G28A acid Fe3(SO4)2 erosion to represent intergranular corrosion weightlessness and corrosive morphology. The result shows that alloy at 750°C by aging treatment for 40h, precipitated γ'' phase in the grain boundary. In high density area of γ'' phase, occurs γ'' phase to δ phase degeneration transformation by aging treatment for 200h and the needle-like δ phase becomes more with time prolonged. And γ'' phase degenerated to δ phase completely until treated for 1000h. The sample which has aging treatment tends to have intergranular corrosion and mainly because alloy element spreading leads to dilution area and grain boundary precipitated phase, plus interlaced δ phase's dissolving, which makes sample grain particle fall off and this results in apparent weightlessness. The weightlessness rate(WLR) is related with precipitated volume score. With aging sensitization time change, can be described by Johnson-Mehl-Avrami equation, i.e.: = 44.32 1 − exp (− . ) + 44.62 1 − exp (− . ) + 1.267 ( / ).

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Prediction of corrosion behavior using neural network as a data mining tool

Cited by 106 publications

References 31 publications

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

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

Stress-Induced Phase Transformation and Its Correlation with Corrosion Properties of Dual-Phase High Carbon Steel

Long Term Aging Treatment Impact on Ultralow Iron Alloy 625 Intergranular Corrosion Property

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