A study on metastability phenomena of passive films for corrosion resistant alloys

Contreras, G.; Fassina, P.; Fumagalli, G.; Goidanich, Sara; Lazzari, Luciano; Mazzola, Enrico

doi:10.1016/j.electacta.2006.12.037

Cited by 9 publications

(4 citation statements)

References 21 publications

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“…More precisely, T p0 represents the maximum lifetime of an isolated metastable cell: laboratory tests proved that a metastable state has a fairly short life (less than a minute on average, although metastability cases lasting up to 30 min were observed, as in previous studies by Bolzoni et al , 2006a , b , andContreras et al , 2007 ); consequently, given the hypothesis that each simulation step (iteration) lasts 10 ° s, T p0 was set to 180. The parameter d represents the maximum cathodic protection distance: it depends on the electrolyte conductivity, which was considered to be constant, thus allowing defi nition of d as 10.…”

Section: Resultsmentioning

confidence: 99%

“…Localized corrosion of stainless steels, with particular reference to pitting corrosion, is a widely investigated phenomenon, and several different theories and models have been proposed to describe its mechanism and to foresee its initiation (breakdown time of passive fi lm) as a function of environmental factors and metal characteristics, as proposed in an early study on pitting mechanism by Finley (1967) ; Szklarska-Smialowska (1986) and Baroux (1988) , several studies by Shibata (1990Shibata ( , 1996Shibata ( , 2000 , Shibata & Takeyama (1977) , and more recent research (Bolzoni , Fassina, Fumagalli, & Goidanich, 2006a, Bolzoni , Fassina, Fumagalli, Lazzari, & Mazzola, 2006bContreras et al , 2007 ;Staehle , 2000 ).…”

Section: Introductionmentioning

confidence: 99%

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Representing localized corrosion processes through cellular automata

Contreras¹,

Goidanich

Maggi

et al. 2011

Corrosion Reviews

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This research illustrates a spatially distributed cellular automaton aimed at simulating the pitting corrosion process in stainless steels. The cellular automaton model is based on the metal surface discretization into square cells; in any instant, each cell is found to be in one of the following states: (i) passivity (the passive fi lm on the cell surface is stable and protective), (ii) metastability (the passive fi lm is unstable and can either break, causing pitting initiation, or repassivate), and (iii) pitting (the cell has turned into a stable attack area, with no repassivation chance). At the very beginning, all cells are in passivity state. Subsequently, a stochastic process drives the changes in the state of cells, following rules drawn from the kinetics of pitting localized corrosion. In particular, the transition probabilities of each single cell from one state to another are infl uenced by the adjacent cells. The comparison between simulations run with the proposed model and laboratory experimental results demonstrates that the model is suitable for describing pitting corrosion processes.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Representing localized corrosion processes through cellular automata

Contreras¹,

Goidanich

Maggi

et al. 2011

Corrosion Reviews

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“…Nevertheless, it can be used to predict corrosion initiation, considered by the authors of major importance in engineering applications where, in most of the cases, it is practically the design life of the component. The use of a Markov chain model was anticipated in a previous paper, describing the metastability of the passive film in chloride‐containing environment . A metastable condition can be defined as a stage in which pitting initiation is followed by rapid repassivation.…”

Section: Introductionmentioning

confidence: 99%

Predicting the risk of pitting corrosion initiation of stainless steels using a Markov chain model

Brenna

Bolzoni

Lazzari

et al. 2017

Materials & Corrosion

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A probabilistic approach based on Markov chains for the assessment of pitting and crevice corrosion initiation is proposed. A Markov chain is a stochastic process that undergoes transitions from one state to another through a finite number of possible states, until a so‐called “absorbing state” from which the system has no tendency to evolve is attained. The proposed model calculates the probability to have pitting or to maintain a stable passive condition involving a large number of operating parameters related to both metal and environment: steel chemical composition (namely, the PREN index), chlorides content ([Cl−]), chloride critical threshold ([Cl−]cr), temperature, pH, fluid velocity, electrochemical steel potential, E, presence of crevices. Model and algorithms are mainly based on corrosion data collected from literature. Although the proposed model needs experimental confirmation, the simulations here presented seem acceptable, considering the engineering application of stainless steels.

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“…This value depends principally on materials properties (composition, deformation degree, heat treatment, surface finishing) and environment properties (temperature, pH, presence of oxidizing environment, flow regime). The behavior of stainless steels with respect to pitting or crevice corrosion has been studied extensively by many authors over the last 50 years on the DOI basis of different laboratory approaches [1][2][3][4][5][6][7]. Welding of stainless steels is carried out at high temperature and it affects their corrosion resistance by microsegregation, precipitation of secondary phases, formation of unmixed zones, recrystallization and grain growth in the weld heat-affected zone (HAZ), residual welding flux, microfissures, beginning or end of weld passes creating crevices, presence of "heat tinted oxides" formed on the surface.…”

Section: Introductionmentioning

confidence: 99%

Surface State Effect of Welded Stainless Steels on Corrosion Behavior

Liptáková¹,

Lovíšek²,

Alaskari³

et al. 2016

Acta Metall Slovaca

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Welding joints of stainless steels are in practice very sensitive places attacked by local corrosion, such as pitting and crevice corrosion, mostly in chloride containing oxidizing environments. It is caused by different oxidation products created on the surface by welding. Corrosion resistance of stainless steels is affected by quality of passive layer (Cr2O3) which created at normal conditions on air. After welding the steels are heated and on the surface are originated different types of oxides which do not have the same protective properties. Resistance to intergranular and pitting corrosion of the welded stainless steels with different surface finishing was tested. Experimental materials are the austenitic stainless steel AISI 316L (welded by the TIG method in inert argon atmosphere with filler) and the ferritic stainless steel AISI CA6-NM (welded by the WPS method in inert argon atmosphere with filler and after welding heat treated). Character of the surface after welding and after finishing by grinding and pickling was evaluated by SEM microscopy, EDX analysis. Corrosion resistance to local forms of corrosion was investigated by electrochemical potentiodynamic test and by exposure tests in chloride solutions. The steel AISI CA6-NM was tested in fluvial water to simulate real operation environment. The evaluation is supported by microscopic analysis. Susceptibility to intergranular corrosion was tested too and results detect the dangerous localities for corrosion attack and show increasing of corrosion resistance by surface treatment.

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A study on metastability phenomena of passive films for corrosion resistant alloys

Cited by 9 publications

References 21 publications

Representing localized corrosion processes through cellular automata

Representing localized corrosion processes through cellular automata

Predicting the risk of pitting corrosion initiation of stainless steels using a Markov chain model

Surface State Effect of Welded Stainless Steels on Corrosion Behavior

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