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Summary There are numerous factors that affect the corrosion of structural components. A thorough understanding of the localized corrosion envi-

References

ronment (at the corrosion location) and the material and its

The following is a list of publications that discuss the various aspects of life prediction under a variety of corrosion conditions.

microstructure is necessary to begin addressing the problem of life prediction. A qualitative prediction may be readily made using a potential-pH diagram that incorporates passivation characteristics. Such maps can delineate the conditions under which the different modes of corrosion may be operative, thus enabling the requisite changes in the environment, the material or both to extend life or prevent premature failure. Quantitative predictions are largely based on either 1) theoretical modeling that is verified by experimental data, 2) empirical models that are derived from experimental data or field data or 3) statistical distributions of failure data and an understanding of the parameters that are extracted from such distributions. Although the overall approach to life prediction and performance assurance can be generalized, the availability of experimental data, models, service data, and understanding the precise degradation conditions dictates a situationspecific approach to this important problem.

Note: A comprehensive report that details the state-of-the-art in this topic area is being developed for publication in the Fall of 1998.

[1] Life Prediction of Corrodible Structures, Vols. 1 and II, NACE, Houston, TX, 1994. [2] “Materials Considerations in Service Life Predictions,” R. P. Wei and D. G. Harlow, Applied Mech. Review, Vol. 46, may 1993. [3] “Modeling and Life prediction of Stress Corrosion Cracking in Sensitized Stainless Steel in High Temperature Water,” P. L. Andresen and F. P. Ford, Predictive Capabilities in Environmentally Assisted Cracking, ed. R. Rungta, ASME, NY, 1985, pp. 17-39. [4] “Corrosion Damage Function – Interface Between Corrosion Science and Engineering,” D. D. Macdonald and M. Urquidi-Macdonald, Corrosion, Vol. 48, No. 5, 1992, pp 354-367.

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A MPTIAC

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Dr. A.K. Kuruvilla IIT Research Institute Huntsville, Alabama

LIFE PREDICTION AND PERFORMANCE ASSURANCE OF STRUCTURAL MATERIALS IN CORROSIVE ENVIRONMENTS

Introduction Reliable estimates indicate that corrosion of metals costs the US

The following is a brief overview/discussion of the influence of

[5] “A Dominant Flaw Probability Model for Corrosion and Corrosion Fatigue,” D. G. Harlow and R. P. Wei, 12th Int. Corrosion Congress, NACE, Houston, TX, 1993.

economy almost $300 billion per year or about 4% of the GNP.

these factors on life predictive capability and assurance of trouble

Corrosion is a pervasive problem that affects almost all sectors of

free operation.

[6] “Development and Use of a Predictive Model of Crack Propagation in 304/316L, A533/A508 and Inconel 600/182 Alloys in 288 oC Water,” F. P. Ford and P. L. Andresen, Environmental Degradation of Materials in Nuclear Power Systems-Water Reactors, eds. G. J. Theus and J. R. Weeks, TMS, Warrendale, PA, 1988, pp. 789-800.

the economy including transportation (bridges, automobiles, air-

Environment: All corrosion involves reaction of the metal with

craft, ships, pipelines, etc.), energy (power plants, petrochemical

the environment and hence, a definition of the chemical environ-

industries etc.) and defense (naval applications, aging aircraft &

ment with the metal is of primary importance. Typically, the fac-

weapon systems, etc.). Although the complete elimination of cor-

tors that control reactivity include the electrochemical potential,

rosion is virtually impossible, it is believed that a third of this cost

the pH, the passivating characteristics, temperature and the flow

is avoidable based on the present status of corrosion science, the

rate of the aqueous environment. The chemical species in the

state-of-the-art in industrial practice and the continual develop-

environment clearly affect most of these factors. In defining the

ments in associated technologies. Broader application of corrosion

environment, very often, the localized chemical characteristics are

resistant materials, improvements in corrosion prevention practice,

more important than the bulk chemistry. Therefore, all possible

effective detection and repair and an investment in corrosion relat-

environmental variations must be considered when dealing with a

ed research are the necessary ingredients of any program that tar-

structural component. For example, if the component design

gets minimization of corrosion related costs.

results in a crevice or an occluded cell, the local chemistry will be

[7] Application of Accelerated Tests to Service Life Prediction of Materials, ASTM STP 1194, eds. G. Cragnolino and N. Sridhar, American Society for Testing of Materials, Philadelphia, Pa, 1993. [8] “Development and Application of Corrosion Mode Diagrams,” R. W. Staehle, Fundamental Aspects of Stress corrosion Cracking, eds. S. M. Bruemmer, E. I. Meletis, R. H. Jones, W. W. Gerberich, F. P. Ford and R. W. Staehle, TMS, Warrendale, PA, 1992, pp. 457-491.

Given the ubiquitous nature of corrosion, life prediction and

quite different from (and often more aggressive) that of the bulk

performance assurance is vital to the cost-effective management of

chemistry. As a result, the corrosion characteristics will also be sig-

components and systems that encounter corrosion in service. Over

nificantly different.

- conservative design and operation has been the preferred

The environment is typically described using a potential-pH

approach to reducing failures and ensuring the designed perfor-

diagram that is more commonly known as the Pourbaix diagram.

mance in corrosive environments. However, the cost penalty asso-

These plots are ideal for displaying the potential corrosion regimes

ciated with this approach is continually directing efforts toward

given the environmental conditions. It has been pointed out in the

more effective life prediction and performance assurance.

literature that it is also important to superimpose a “passivating parameter” on these plots in order to describe the effect of partic-

A D VA N C E D M AT E R I A L S

AND

[email protected] http://rome.iitri.com/amptiac EMAIL:

PROCESSES TECHNOLOGY

A MPTIAC PHONE:

315.339.7117

FA X :

315.339.7107

AMPTIAC is a DoD Information Analysis Center Sponsored by the Defense Technical Information Center and Operated by IIT Research Institute

The Essential Elements

ular chemical species on the passivation of metal surfaces under a

Any life prediction effort related to corrosion of structural compo-

given set of conditions. Additionally, if a component experiences

nents should comprise, for a minimum, certain essential elements:

different environments at different points, the potential-pH range

1) 2) 3) 4) 5) 6) 7)

Definition of the environment Definition of the material / microstructure Definition of the stress state Understanding of the mode of corrosive attack Definition of failure Field data (if available) Results of accelerated testing.

of exposure can be well described on such plots. Note that it is equally important to document changes in the environment with time, if any, to be able to predict the nature and rate of corrosion. Material / Microstructure: The reactivity of a metal to any given environment is dictated by its chemical composition. Since reactions occur at metal surfaces, the surface composition is often

AMPTIAC is a DoD Information Analysis Center Sponsored by the Defense Technical Information Center and Operated by IIT Research Institute

Material E

A

S

A MPTIAC A D VA N C E D M AT E R I A L S

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AND

PROCESSES TECHNOLOGY

Another example of modeling crack velocity under stress corrosion cracking is the following equation:

extremely important. The effect of major alloying elements is usually pro-

Prediction involves assessing the life of the component in the environment

nounced in most alloy systems. However, minor alloying elements and

whereas assurance deals with the level of confidence with which perfor-

impurities also have a significant effect on the reactivity of the metal sur-

mance is guaranteed through the design life of the component. The latter

face. If the grain boundary composition is different from that of the bulk,

therefore involves inspection, monitoring and maintenance functions in

then the nature of corrosion will be different at the grain boundary than

addition to the initial prediction.

in the matrix. In addition to the composition, other microstructural factors such as yield strength, amount of deformation, the composition and distribution of second phases and surface conditions are also significant to corrosion characteristics of the metallic material. Stress: Integral to the environment, stresses assist the degradation of metals under corrosive conditions. Loads such as bending, tension and

r = B exp — Q* ± zFαE RT

,

(5) CV =

where:

Qf εct M

εfzFd

,

(7)

where:

The key to predicting life is to quantify the qualitative information as discussed in the previous section dealing with the environment, material etc. This is accomplished by developing accurate theoretical models that can be verified with real life data. Another way of predicting life is to use the accelerated test data or service data (if available) to generate empirical relationships with which the time to failure can be estimated.

r

=

rate of electrochemical reaction

B

=

electrochemical rate constant

CV

=

Q*

=

chemical activation energy in the electrochemical

Qf

=

charge density

εct

=

crack tip strain rate

reaction z

=

charge on the activated species

F

=

Faraday’s constant

crack velocity

εf

=

strain to rupture the crack tip film

z

=

valency of the solvated species

α

=

transfer coefficient

M

=

atomic weight of the metal

tributors to corrosive damage. Instead, it is the stresses such as the resid-

Theoretical Modeling

E

=

electrochemical potential

=

Faraday’s constant

ual stress and stresses resulting from the expansion of corrosion products

The development of sound theoretical models of failure is a good starting

Q* ±zFαE = electrochemical activation energy.

F d

=

density of the metal.

that cause the most damage. The stress situation at the surface of the

point for life prediction. The accuracy of the model and its range of

metal that encounters the corrosive environment must therefore be accu-

applicability are, therefore, extremely important.

The better known

It is important to note that if there is a mechanically related process

There are several expressions available in the literature to calculate the

rately defined in order to be able to predict the extent of corrosion.

dependencies of the time to failure are represented in Equations 1 - 3 as

operating in conjunction with the electrochemical process, stress would

crack tip strain rate under static, monotonically increasing or cyclic load-

follows:

also be a factor that influences the activation energy term. In such

ing conditions. Models similar to those cited above have been derived for

instances, corrosive degradation may not be adequately represented by

different modes of corrosive degradation such as stress corrosion cracking, corrosion, fatigue, etc.

torsion that can be analytically defined are often not the most critical con-

Mode of Corrosion: As the metal interacts with the environment, it results in a corrosion path. The morphology of this path is referred to as the corrosion mode. Understanding the mode of corrosion is vital to any

tf = cσ-n,

(1)

mere addition of the stress and chemical reaction terms. The complexity

corrosion life predictive capability. Examples of the modes of corrosion

tf = geQ/RT,

(2)

of service conditions often precludes theoretical models from adequately

are general corrosion, pitting corrosion, intergranular corrosion and stress

tf = a[H+]-m.

(3)

representing actual performance. However, theoretical models serve as

2.) Statistical Concepts

good starting points for analysis and life prediction.

There are two approaches to applying statistics to corrosion related failure.

corrosion which can be either transgranular or intergranular in its mode of failure. The mode of corrosion can be superimposed on the potential-pH diagrams to generate maps that predict the type of corrosion for a given set

The combined equation that represents an overall dependency for timeto-failure would then be

of environmental conditions. tf = dσ-n eQ/RT [H+]-m,

Definition of Failure: Failure can be defined in several ways by estab-

(4)

lishing the necessary criteria under the given operating conditions. Failure data are often characterized using statistical distributions such as the

where:

Weibull distribution.

example, assuming a Poisson distribution for the generation of pits (in pit-

Experimental data (either from service or from accelerated testing) are typ-

ting corrosion), the probability of the formation of a pit can be computed

ically modeled either empirically (with some basis in theoretical modeling)

as it varies with the surface area or time in the corrosive environment.

or statistically. An example of the former is the correlation of crack growth

Experimental results for pitting induced failure have been found to corre-

rate to crack tip strain rate under stress corrosion cracking, given by the

late well with theoretical predictions in some studies. In the second approach, the statistical distribution of service failure data

following equation:

Field data: If available, service data from the field can be extremely

tf

=

time to failure

valuable to performance assurance and life prediction. Ideally, failure data

σ

=

stress

could be used to generate the representative statistical distribution from

n

=

exponent of stress

which parameters indicative of the potential and probability for failure can

Q

=

thermal activation energy

be extracted.

T

=

absolute temperature

R

=

gas constant

Accelerated Testing: Components are generally designed for a specific

In the first, corrosion probability concepts are applied to failure. For Modeling Experimental Data: 1.) Empirical Relationships

life span. However, the assurance of economical, reliable and safe perfor-

[H+] =

hydrogen ion activity

mance for the design life and extended times require past performance

m

exponent of hydrogen ion activity

data, conservative design and results of accelerated tests. Temperature,

c, g, a, d are constants.

=

stress, ripple loading, thermal cycling, variation of the environmental com-

can be used to generate parameters that are indicative of the potential for failure. The Weibull distribution, for example, which was developed for Vt = A (εct)n,

(6)

corrosion failure data as well. The effect of environmental variables on the parameters of the distribution can be modeled to enable performance

where:

assurance of the material under varying environmental conditions. A furVt

=

average crack velocity

ther enhancement to this approach is to compute the overall probability of

εct

=

crack tip strain rate

failure of a component based on the probability of failure under different modes of corrosion that are operative simultaneously.

A, n depend on the crack tip material and environmental compositions.

position etc. are some of the variables that can be employed in accelerated

Equation 2 is the well-known Arrhenius form of the equation that rep-

tests. The key is to identify and employ those variables that yield results

resents the dependency of the rate of a chemical reaction (corrosion in this

in a short period of time that would correspond to real life conditions.

case) on temperature. In electrochemical reactions, the electrochemical

Life Prediction and Performance Assurance

potential is also included and the equation takes on the following form:

characterizing fatigue data has been found to be useful for characterizing

Material E

A

S

A MPTIAC A D VA N C E D M AT E R I A L S

E

AND

PROCESSES TECHNOLOGY

Another example of modeling crack velocity under stress corrosion cracking is the following equation:

extremely important. The effect of major alloying elements is usually pro-

Prediction involves assessing the life of the component in the environment

nounced in most alloy systems. However, minor alloying elements and

whereas assurance deals with the level of confidence with which perfor-

impurities also have a significant effect on the reactivity of the metal sur-

mance is guaranteed through the design life of the component. The latter

face. If the grain boundary composition is different from that of the bulk,

therefore involves inspection, monitoring and maintenance functions in

then the nature of corrosion will be different at the grain boundary than

addition to the initial prediction.

in the matrix. In addition to the composition, other microstructural factors such as yield strength, amount of deformation, the composition and distribution of second phases and surface conditions are also significant to corrosion characteristics of the metallic material. Stress: Integral to the environment, stresses assist the degradation of metals under corrosive conditions. Loads such as bending, tension and

r = B exp — Q* ± zFαE RT

,

(5) CV =

where:

Qf εct M

εfzFd

,

(7)

where:

The key to predicting life is to quantify the qualitative information as discussed in the previous section dealing with the environment, material etc. This is accomplished by developing accurate theoretical models that can be verified with real life data. Another way of predicting life is to use the accelerated test data or service data (if available) to generate empirical relationships with which the time to failure can be estimated.

r

=

rate of electrochemical reaction

B

=

electrochemical rate constant

CV

=

Q*

=

chemical activation energy in the electrochemical

Qf

=

charge density

εct

=

crack tip strain rate

reaction z

=

charge on the activated species

F

=

Faraday’s constant

crack velocity

εf

=

strain to rupture the crack tip film

z

=

valency of the solvated species

α

=

transfer coefficient

M

=

atomic weight of the metal

tributors to corrosive damage. Instead, it is the stresses such as the resid-

Theoretical Modeling

E

=

electrochemical potential

=

Faraday’s constant

ual stress and stresses resulting from the expansion of corrosion products

The development of sound theoretical models of failure is a good starting

Q* ±zFαE = electrochemical activation energy.

F d

=

density of the metal.

that cause the most damage. The stress situation at the surface of the

point for life prediction. The accuracy of the model and its range of

metal that encounters the corrosive environment must therefore be accu-

applicability are, therefore, extremely important.

The better known

It is important to note that if there is a mechanically related process

There are several expressions available in the literature to calculate the

rately defined in order to be able to predict the extent of corrosion.

dependencies of the time to failure are represented in Equations 1 - 3 as

operating in conjunction with the electrochemical process, stress would

crack tip strain rate under static, monotonically increasing or cyclic load-

follows:

also be a factor that influences the activation energy term. In such

ing conditions. Models similar to those cited above have been derived for

instances, corrosive degradation may not be adequately represented by

different modes of corrosive degradation such as stress corrosion cracking, corrosion, fatigue, etc.

torsion that can be analytically defined are often not the most critical con-

Mode of Corrosion: As the metal interacts with the environment, it results in a corrosion path. The morphology of this path is referred to as the corrosion mode. Understanding the mode of corrosion is vital to any

tf = cσ-n,

(1)

mere addition of the stress and chemical reaction terms. The complexity

corrosion life predictive capability. Examples of the modes of corrosion

tf = geQ/RT,

(2)

of service conditions often precludes theoretical models from adequately

are general corrosion, pitting corrosion, intergranular corrosion and stress

tf = a[H+]-m.

(3)

representing actual performance. However, theoretical models serve as

2.) Statistical Concepts

good starting points for analysis and life prediction.

There are two approaches to applying statistics to corrosion related failure.

corrosion which can be either transgranular or intergranular in its mode of failure. The mode of corrosion can be superimposed on the potential-pH diagrams to generate maps that predict the type of corrosion for a given set

The combined equation that represents an overall dependency for timeto-failure would then be

of environmental conditions. tf = dσ-n eQ/RT [H+]-m,

Definition of Failure: Failure can be defined in several ways by estab-

(4)

lishing the necessary criteria under the given operating conditions. Failure data are often characterized using statistical distributions such as the

where:

Weibull distribution.

example, assuming a Poisson distribution for the generation of pits (in pit-

Experimental data (either from service or from accelerated testing) are typ-

ting corrosion), the probability of the formation of a pit can be computed

ically modeled either empirically (with some basis in theoretical modeling)

as it varies with the surface area or time in the corrosive environment.

or statistically. An example of the former is the correlation of crack growth

Experimental results for pitting induced failure have been found to corre-

rate to crack tip strain rate under stress corrosion cracking, given by the

late well with theoretical predictions in some studies. In the second approach, the statistical distribution of service failure data

following equation:

Field data: If available, service data from the field can be extremely

tf

=

time to failure

valuable to performance assurance and life prediction. Ideally, failure data

σ

=

stress

could be used to generate the representative statistical distribution from

n

=

exponent of stress

which parameters indicative of the potential and probability for failure can

Q

=

thermal activation energy

be extracted.

T

=

absolute temperature

R

=

gas constant

Accelerated Testing: Components are generally designed for a specific

In the first, corrosion probability concepts are applied to failure. For Modeling Experimental Data: 1.) Empirical Relationships

life span. However, the assurance of economical, reliable and safe perfor-

[H+] =

hydrogen ion activity

mance for the design life and extended times require past performance

m

exponent of hydrogen ion activity

data, conservative design and results of accelerated tests. Temperature,

c, g, a, d are constants.

=

stress, ripple loading, thermal cycling, variation of the environmental com-

can be used to generate parameters that are indicative of the potential for failure. The Weibull distribution, for example, which was developed for Vt = A (εct)n,

(6)

corrosion failure data as well. The effect of environmental variables on the parameters of the distribution can be modeled to enable performance

where:

assurance of the material under varying environmental conditions. A furVt

=

average crack velocity

ther enhancement to this approach is to compute the overall probability of

εct

=

crack tip strain rate

failure of a component based on the probability of failure under different modes of corrosion that are operative simultaneously.

A, n depend on the crack tip material and environmental compositions.

position etc. are some of the variables that can be employed in accelerated

Equation 2 is the well-known Arrhenius form of the equation that rep-

tests. The key is to identify and employ those variables that yield results

resents the dependency of the rate of a chemical reaction (corrosion in this

in a short period of time that would correspond to real life conditions.

case) on temperature. In electrochemical reactions, the electrochemical

Life Prediction and Performance Assurance

potential is also included and the equation takes on the following form:

characterizing fatigue data has been found to be useful for characterizing

Material

Summary There are numerous factors that affect the corrosion of structural components. A thorough understanding of the localized corrosion envi-

References

ronment (at the corrosion location) and the material and its

The following is a list of publications that discuss the various aspects of life prediction under a variety of corrosion conditions.

microstructure is necessary to begin addressing the problem of life prediction. A qualitative prediction may be readily made using a potential-pH diagram that incorporates passivation characteristics. Such maps can delineate the conditions under which the different modes of corrosion may be operative, thus enabling the requisite changes in the environment, the material or both to extend life or prevent premature failure. Quantitative predictions are largely based on either 1) theoretical modeling that is verified by experimental data, 2) empirical models that are derived from experimental data or field data or 3) statistical distributions of failure data and an understanding of the parameters that are extracted from such distributions. Although the overall approach to life prediction and performance assurance can be generalized, the availability of experimental data, models, service data, and understanding the precise degradation conditions dictates a situationspecific approach to this important problem.

Note: A comprehensive report that details the state-of-the-art in this topic area is being developed for publication in the Fall of 1998.

[1] Life Prediction of Corrodible Structures, Vols. 1 and II, NACE, Houston, TX, 1994. [2] “Materials Considerations in Service Life Predictions,” R. P. Wei and D. G. Harlow, Applied Mech. Review, Vol. 46, may 1993. [3] “Modeling and Life prediction of Stress Corrosion Cracking in Sensitized Stainless Steel in High Temperature Water,” P. L. Andresen and F. P. Ford, Predictive Capabilities in Environmentally Assisted Cracking, ed. R. Rungta, ASME, NY, 1985, pp. 17-39. [4] “Corrosion Damage Function – Interface Between Corrosion Science and Engineering,” D. D. Macdonald and M. Urquidi-Macdonald, Corrosion, Vol. 48, No. 5, 1992, pp 354-367.

E

A

S

4

A MPTIAC

E

Dr. A.K. Kuruvilla IIT Research Institute Huntsville, Alabama

LIFE PREDICTION AND PERFORMANCE ASSURANCE OF STRUCTURAL MATERIALS IN CORROSIVE ENVIRONMENTS

Introduction Reliable estimates indicate that corrosion of metals costs the US

The following is a brief overview/discussion of the influence of

[5] “A Dominant Flaw Probability Model for Corrosion and Corrosion Fatigue,” D. G. Harlow and R. P. Wei, 12th Int. Corrosion Congress, NACE, Houston, TX, 1993.

economy almost $300 billion per year or about 4% of the GNP.

these factors on life predictive capability and assurance of trouble

Corrosion is a pervasive problem that affects almost all sectors of

free operation.

[6] “Development and Use of a Predictive Model of Crack Propagation in 304/316L, A533/A508 and Inconel 600/182 Alloys in 288 oC Water,” F. P. Ford and P. L. Andresen, Environmental Degradation of Materials in Nuclear Power Systems-Water Reactors, eds. G. J. Theus and J. R. Weeks, TMS, Warrendale, PA, 1988, pp. 789-800.

the economy including transportation (bridges, automobiles, air-

Environment: All corrosion involves reaction of the metal with

craft, ships, pipelines, etc.), energy (power plants, petrochemical

the environment and hence, a definition of the chemical environ-

industries etc.) and defense (naval applications, aging aircraft &

ment with the metal is of primary importance. Typically, the fac-

weapon systems, etc.). Although the complete elimination of cor-

tors that control reactivity include the electrochemical potential,

rosion is virtually impossible, it is believed that a third of this cost

the pH, the passivating characteristics, temperature and the flow

is avoidable based on the present status of corrosion science, the

rate of the aqueous environment. The chemical species in the

state-of-the-art in industrial practice and the continual develop-

environment clearly affect most of these factors. In defining the

ments in associated technologies. Broader application of corrosion

environment, very often, the localized chemical characteristics are

resistant materials, improvements in corrosion prevention practice,

more important than the bulk chemistry. Therefore, all possible

effective detection and repair and an investment in corrosion relat-

environmental variations must be considered when dealing with a

ed research are the necessary ingredients of any program that tar-

structural component. For example, if the component design

gets minimization of corrosion related costs.

results in a crevice or an occluded cell, the local chemistry will be

[7] Application of Accelerated Tests to Service Life Prediction of Materials, ASTM STP 1194, eds. G. Cragnolino and N. Sridhar, American Society for Testing of Materials, Philadelphia, Pa, 1993. [8] “Development and Application of Corrosion Mode Diagrams,” R. W. Staehle, Fundamental Aspects of Stress corrosion Cracking, eds. S. M. Bruemmer, E. I. Meletis, R. H. Jones, W. W. Gerberich, F. P. Ford and R. W. Staehle, TMS, Warrendale, PA, 1992, pp. 457-491.

Given the ubiquitous nature of corrosion, life prediction and

quite different from (and often more aggressive) that of the bulk

performance assurance is vital to the cost-effective management of

chemistry. As a result, the corrosion characteristics will also be sig-

components and systems that encounter corrosion in service. Over

nificantly different.

- conservative design and operation has been the preferred

The environment is typically described using a potential-pH

approach to reducing failures and ensuring the designed perfor-

diagram that is more commonly known as the Pourbaix diagram.

mance in corrosive environments. However, the cost penalty asso-

These plots are ideal for displaying the potential corrosion regimes

ciated with this approach is continually directing efforts toward

given the environmental conditions. It has been pointed out in the

more effective life prediction and performance assurance.

literature that it is also important to superimpose a “passivating parameter” on these plots in order to describe the effect of partic-

A D VA N C E D M AT E R I A L S

AND

[email protected] http://rome.iitri.com/amptiac EMAIL:

PROCESSES TECHNOLOGY

A MPTIAC PHONE:

315.339.7117

FA X :

315.339.7107

AMPTIAC is a DoD Information Analysis Center Sponsored by the Defense Technical Information Center and Operated by IIT Research Institute

The Essential Elements

ular chemical species on the passivation of metal surfaces under a

Any life prediction effort related to corrosion of structural compo-

given set of conditions. Additionally, if a component experiences

nents should comprise, for a minimum, certain essential elements:

different environments at different points, the potential-pH range

1) 2) 3) 4) 5) 6) 7)

Definition of the environment Definition of the material / microstructure Definition of the stress state Understanding of the mode of corrosive attack Definition of failure Field data (if available) Results of accelerated testing.

of exposure can be well described on such plots. Note that it is equally important to document changes in the environment with time, if any, to be able to predict the nature and rate of corrosion. Material / Microstructure: The reactivity of a metal to any given environment is dictated by its chemical composition. Since reactions occur at metal surfaces, the surface composition is often

AMPTIAC is a DoD Information Analysis Center Sponsored by the Defense Technical Information Center and Operated by IIT Research Institute