Aspects of the effects of temperature and electrolyte composition on pitting corrosion of stainless steel in dairy fluids

Perrin, F.X.; Girard, Henri-Louis; Pagetti, J.; Daufin, G.

doi:10.1002/1521-4176(200108)52:8<629::aid-maco629>3.0.co;2-t

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…In this work, two different kernel functions have been presented: polynomial kernel using different polynomial grades (1, 2, 3) and radial basis function (RBF) using values of the RBF width (g) from 2 À1 to 2 10 .…”

Section: Support Vector Machines Classification Modelsmentioning

confidence: 99%

“…Many authors have developed studies about this phenomenon [6][7][8]. Some of them have been focused on analysing the influence of environmental factors on corrosion behaviour, concluding that principal environmental variables affecting corrosion behaviour are chloride ion concentration, pH and temperature [9][10][11]. With the aim to understand the complex nonlinear relationships between these variables and corrosion resistance, different methods based on artificial intelligence techniques have been proposed:…”

Section: Introductionmentioning

confidence: 99%

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A comprehensive approach based on SVM to model pitting corrosion behaviour of EN 1.4404 stainless steel

Jiménez‐Come

Turias

Ruiz-Aguilar

et al. 2013

Materials & Corrosion

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Potentiodynamic polarisation tests have been widely used to evaluate pitting corrosion behaviour of stainless steels. This technique involves two principal steps: the interpretation of the polarisation curve and the microscopic analysis of the material surface. Therefore, analysing the influence of all possible environmental conditions on pitting corrosion behaviour can be complex. To deal with this problem, automatic models based on Bayes classifier (BC) and support vector machines (SVMs) techniques are presented in this work. The principal environmental variables involved in pitting corrosion: chloride ion concentration, pH and temperature are considered. Based on the obtained results related to precision and accuracy terms, SVMs models using linear or radial basis function kernels outperform models based on Bayes theory. In addition, ROC analysis reveals that SVMs models using linear kernel is the best option providing maximum values of sensitivity and specificity. These results reflect the validity of the presented model to be applied in the automatic determination of the corrosion state of austenitic stainless steel as function of environmental conditions, not requiring the use of polarisation tests.

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Section: Support Vector Machines Classification Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A comprehensive approach based on SVM to model pitting corrosion behaviour of EN 1.4404 stainless steel

Jiménez‐Come

Turias

Ruiz-Aguilar

et al. 2013

Materials & Corrosion

View full text Add to dashboard Cite

show abstract

“…Therefore, in order to continue our studies about pitting corrosion modelling , this work presents a model with the ability to predict pitting corrosion behaviour of stainless steel as crucial step in real world experience and practical applications of this material. The influence of the principal environmental factors involved in pitting corrosion, including chloride ion concentration, pH and temperature are analysed . SVMs are presented as an efficient tool to carry on this purpose.…”

Section: Introductionmentioning

confidence: 99%

Pitting corrosion behaviour modelling of stainless steel with support vector machines

Jiménez‐Come

Turias

Ruiz-Aguilar

2014

Materials and Corrosion

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The knowledge of pitting corrosion behaviour of stainless steel is a critical factor in material science. In order to develop an automatic system to study pitting corrosion behaviour of this material, models based on support vector machines (SVMs) and k-nearest neighbour are presented in this work. The influence of the principal environmental factors involved in pitting corrosion, including chloride ion concentration, pH and temperature together with breakdown potential values obtained from polarisation tests, are analysed. Different feature selection techniques are applied (principal component analysis -PCA, linear discriminant analysis -LDA and Fisher criterion -FDR) as pre-processing stage. The results based on precision and accuracy indices prove that SVMs model using cubic polynomial kernel with LDA or FDR provides excellent classification performance. Therefore, this model becomes an effective strategy for modelling pitting corrosion and it may be considered as useful tool in the design stage of the structures.

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Einfluss höchster Reinheitsgrade auf die Lochkorrosionseigenschaften austenitischer Stähle

Hochörtler¹,

Kriszt²,

Huber³

2004

Materials & Corrosion

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Großtechnisch hergestellte Stähle der Werkstoff Nr. 1.4401, welche zur Erreichung besonderer Reinheitskriterien bis zu drei Mal im Vakuum umgeschmolzen wurden, wurden auf das Korrosionsverhalten mit elektrochemischen Prüfungen untersucht. Dazu wurden potentiodynamische Stromdichte-Potentialkurven auf Basis ASTM G 61-86 aufgenommen.Es wird gezeigt, dass der Reinheitsgrad einen klaren, günstigen Einfluss auf das Lochkorrosionsverhalten ausübt. Das Umschmelzen mit selbstverzehrender Elektrode unter Vakuum bewirkt eine Erhöhung des Reinheitsgrades, indem die nichtmetallischen Einschlüsse in die Blockoberflächenzonen verdrängt werden -es kommt zu geringsten Gasgehalten und positiver Beeinflussung von Seigerungen im Block.Stainless steel grades of the type material. no. 1.4401, produced in an industrial scale, have been vacuum arc re-melted up to three times to achieve lowest inclusion contents. These materials have been corrosion tested to get information about the influence of non-metallic inclusion content on pitting corrosion behaviour. By the use of potentiodynamic polarisation curves on basis of ASTM G61-86 a clear effect of steel cleanliness was detected. The highest degree of cleanliness and best corrosion behaviour has been achieved with vacuum arc re-melting material where non-metallic inclusions are moved to the ingot surface area and minimum gas contents and micro-segregations are gained.

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Aspects of the effects of temperature and electrolyte composition on pitting corrosion of stainless steel in dairy fluids

Cited by 5 publications

References 4 publications

A comprehensive approach based on SVM to model pitting corrosion behaviour of EN 1.4404 stainless steel

A comprehensive approach based on SVM to model pitting corrosion behaviour of EN 1.4404 stainless steel

Pitting corrosion behaviour modelling of stainless steel with support vector machines

Einfluss höchster Reinheitsgrade auf die Lochkorrosionseigenschaften austenitischer Stähle

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