Corrosive Effects of Chlorides on Metals

Alloying metallic elements added during the making of the steel increase corrosion resistance, hardness, or strength. The metals used most commonly as alloying elements in stainless steel include chromium, nickel, and molybdenum. Due to the alloy contains different, in general, stainless steels are separated the grouped into martensitic stainless steels, ferritic stainless steels, austenitic stainless steels, duplex (ferritic-austenitic) stainless steels and precipitation-hardening stainless steels.


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stainless steels in water containing chloride, a micro-environment essentially representing hydrochloric acid may be established within the pits. The pH within the pits tends to be lowered significantly, together with an increase in chloride ion concentration, as a result of the electrochemical pitting mechanism reactions in such systems. Pitting is often found in situations where resistance against general corrosion is conferred by passive surface films. A localized pitting attack is found where these passive films have broken down. Pitting attack induced by microbial activity, such as sulfate reducing bacteria also deserves special mention. Most pitting corrosion in stainless alloys occurs in neutral-to-acid solutions with chloride or ions containing chlorine.
The detection and meaningful monitoring of pitting corrosion usually represents a major challenge. Monitoring pitting corrosion can be further complicated by a distinction between the initiation and propagation phases of pitting processes. The highly sensitive electrochemical noise technique may provide early warming of imminent damage by characteristic in the pit initiation phase. Pitting failures can occur unexpectedly, and with minimal overall metal loss.
Furthermore, the pits may be hidden under surface deposits, and/or corrosion products. A small, narrow pit with minimal overall metal loss can lead to the failure of an entire engineering system. Pitting corrosion, which, for example, is almost a common denominator of all types of localized corrosion attack, may assume different shapes. Corrosion of metals and alloys by pitting constitutes one of the very major failure mechanisms. Pits cause failure through perforation and engender stress corrosion cracks and the life cycle of stainless alloy will decrease.

Pitting corrosion phenomenon
The pits often appear to be rather small at the surface, but may have larger cross-section areas deeper inside the metal. Since the attack is small at the surface and may be covered by corrosion products, a pitting attack often remains undiscovered until it causes perforation and leakage. The experimental evaluation of the parameters of a general stochastic model for the initiation of pitting corrosion on stainless steels is described. The variation of these parameters with experimental conditions is used in the development of a microscopic model. A microscopic model which accords with the observed behavior attributes the initiation of pitting corrosion to the production and persistence of gradients of acidity and electrode potential on the scale of the surface roughness of the metal. The observed fluctuations are related to fluctuations in the hydrodynamic boundary layer thickness. A pit becomes stable when it exceeds a critical depth related to the surface roughness.
This group of alloys are non-magnetic and have an austenitic structure. The basic Stainless Steel Grades 300 alloy contains 18% chromium and 8% nickel. These alloys are subject to crevice corrosion and pitting corrosion. They have a range of incubation times in seawater ranging from essentially zero in the case of the free machining grades, such as Type 303, to 6 months to 1 year for the best alloys, such as Type 316. They have been widely used in facilities with mixed results. If used in an application where chloride levels are low or where concentration cell corrosion has been prevented through design, they are likely to perform well. When chloride levels are high and where concentration cells can occur, the performance of these alloys is often poor. They must always be selected with care for a specific application and the effect of potential non-uniform attack on system performance must be addressed.

c. Stainless Steel Grades 400 Series
This group of alloys are magnetic and have a martensitic structure. The basic alloy contains 11% chromium and 1% manganese. These Stainless Steel Grades alloys can be hardened by heat treatment but have poor resistance to corrosion. They are subject to both uniform and non-uniform attack in seawater. The incubation time for non-uniform attack in chloride containing environments is very short, often only hours or a few days. Unless protected, using these Stainless Steel Grades in sea water or other environments where they are susceptible to corrosion is not recommended.

d. Stainless Steel Grades 600 Series
This series of stainless steels grade is commonly referred to as Precipitation Hardening stainless steels. These steels can be heat treated to high strength levels. They are subject to crevice corrosion and pitting in chloride containing environments and are also subject to stress corrosion cracking and hydrogen embrittlement. The incubation time for crevice corrosion and pitting in seawater is relatively short, often only a few days. The incubation time for stress corrosion cracking can be very short, sometimes measured in hours.
The use of this Stainless Steel Grade in chloride containing environments is not normally recommended unless they are carefully selected, their heat treatment is carefully specified and controlled, and the effect of pitting and crevice corrosion is properly addressed.

Principle of pitting corrosion
Pitting corrosion is an electrochemical oxidation-reduction process, which occurs within localized deeps on the surface of metals coated with a passive film.

Pitting Corrosion 142
Anodic reactions inside the pit: The electrons given up by the anode flow to the cathode where they are discharged in the cathodic reaction: As a result of these reactions the electrolyte enclosed in the pit gains positive electrical charge in contrast to the electrolyte surrounding the pit, which becomes negatively charged.
The positively charged pit attracts negative ions of chlorine Clincreasing acidity of the electrolyte according to the reaction: pH of the electrolyte inside the pit decreases from 6 to 2-3, which causes further acceleration of corrosion process.
Large ratio between the anode and cathode areas favors increase of the corrosion rate. Corrosion products (Fe(OH) 3 ) form around the pit resulting in further separation of its electrolyte.

Stages of pitting corrosion
Pitting corrosion is treated as a time-dependent stochastic damage process characterized by an exponential or logarithmic pit growth. Data from propulsion shaft for high-speed craft is used to simulate the sample functions of pit growth on metal surfaces. Perforation occurs when the deepest pit extends through the thickness of the propulsion shaft. Because the growth of the deepest pit is of stochastic nature, the time-to-perforation is modeled as a random variable that can be characterized by a suitable reliability model. It is assumed that corrosion will occur at multiple pits on both sides of the rivet deeps and will cause multiple fatigue cracks. Therefore, system failure could occur due to the linkage between any two neighboring cracks.

Pit initiation
An initial pit may form on the surface covered by a passive oxide film as a result of the following: a. Mechanical damage of the passive film was caused by scratches. Anodic reaction starts on the metal surface exposed to the electrolyte. The passivity surrounding surface is act as the cathode. b. Particles of a second phase emerging on the metal surface. These particles precipitating along the grains boundaries may function as local anodes causing localized galvanic corrosion and formation of initial pits. c. Localized stresses in form of dislocations emerging on the surface may become anodes and initiate pits. d. Non-homogeneous environment may dissolve the passive film at certain locations where initial pits form.

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Corrosive Effects of Chlorides on Metals 143

Pitting growth
In presence of chloride ions pits are growing by autocatalytic mechanism. Pitting corrosion of a stainless steel is illustrated in the Figure 1. The actual pitting corrosion phenomenon is shown on propeller shaft of high speed craft, and the pit depth was measured with dial gauge as shown in Figure 2.

Transition from pitting to fatigue crack nucleation
The third stage is the transition from pit growth to fatigue crack nucleation, where mechanical effects such as the stress intensity factor come into play. The nucleation of the corrosion crack is essentially a competition between the processes of pit growth and crack growth. Two criteria are used to describe the transition process.

Short crack growth
The short crack growth stage involves chemical and microstructural factors and their interactions. Although much research has been done in this area, it has been difficult to derive an explicit formula for short crack growth, especially in corrosive environment. For computational simplicity, a probabilistic power law model is presented here to describe the relationship between the stress intensity factor and the growth rate. In this method, an empirically based probabilistic relationship is used to model the corrosion short crack growth.

Transition from short crack growth to long crack growth
Experimental and analytical approaches have been proposed to determine the transition size from short crack growth to long crack growth.

Long crack growth
The widely used Paris Law may be used in this stage to estimate the time for long crack growth.

Crack coalescence
The linkage between any two neighboring cracks is considered to be the failure criterion at this stage.

Pitting corrosion behavior
Corrosion of metals and alloys by pitting constitutes one of the very major failure mechanisms. Pits cause failure through perforation and engender stress corrosion cracks. Pitting is a failure mode common to very many metals. It is generally associated with particular anions in solution, notably the chloride ion. The origin of pitting is small. Pits are nucleated at the microscopic scale and below. Detection of the earliest stages of pitting requires techniques that measure tiny events.
Stainless steels are used in countless diverse applications for their corrosion resistance. Although stainless steels have extremely good general resistance, stainless steels are nevertheless susceptible to pitting corrosion. This localized dissolution of an oxide-covered metal in specific aggressive environments is one of the most common and catastrophic causes of failure of metallic structures. The pitting process has been described as random, sporadic and stochastic and the prediction of the time and location of events remains extremely difficult. Many contested models of pitting corrosion exist, but one undisputed aspect is that manganese sulphide inclusions play a critical role.
The chromium in the steel combines with oxygen in the atmosphere to form a thin, invisible layer of chrome-containing oxide, called the passive film. The sizes of chromium atoms and their oxides are similar, so they pack neatly together on the surface of the metal, forming a stable layer only a few atoms thick. If the metal is cut or scratched and the passive film is disrupted, more oxide will quickly form and recover the exposed surface, protecting it from oxidative corrosion.
The passive film requires oxygen to self-repair, so stainless steels have poor corrosion resistance in low-oxygen and poor circulation environments. In seawater, chlorides from the salt will attack and destroy the passive film more quickly than it can be repaired in a low oxygen environment.
Some metals show preferential sites of pit nucleation with metallurgical microstructural and microcompositional features defining the susceptibility. However, this is not the phenomenological origin of pitting per se, since site specificity is characteristic only of some metals. A discussion is presented of mechanisms of nucleation; it is shown that the events are microscopically violent. The ability of a nucleated event to survive a series of stages that it must go through in order to achieve stability is discussed. Nucleated pits that do not propagate must repassivate. However, there are several states of propagation, each with a finite survival probability. Several variables contribute to this survival probability.

Electron fractography of fatigue fracture with pitting corrosion
The evolution of corrosion pits on stainless steel immersed in chloride solution occurs in three distinct stages: nucleation, metastable growth and stable growth. A microcrack generated by pitting corrosion, forms the initial origin for fatigue fracture. But in fact, fatigue failure is not certainly caused at the deepest interior pitting deep. Owing to the different shape of interior pitting deeps as shown in Figure 3, some of them are hard to start or to continue the crack propagation and play as a role of crack arrester, as shown in Figure  4. If the fracture is caused by the pitting deep as an inclusion, then continuous plastic deformation can be found around the pitting deep. The dimples are generally equalized by overloaded tension, and elongated by shear or tearing, as shown in Figure 5 and Figure 6. The features of cleavage failure can be seen by flat fractography as shown in Figure 7 and Figure 8 with 3000 and 1000 times Scanning Electron Microscopy (SEM) respectively. Cleavage failure occurs by separation along crystallographic planes. This transgranular fracture is categorized to be the brittle fracture in the fracture area. There are several features can be identified to be cleavage, namely, herringbone, tongues and river stream. Figure 9 and Figure 10 show the microscopic fractographics of river stream and tongue patterns, which have seen in the study.
At grain boundaries the fracture plane or cleavage plane changes because of the differences of crystallographic orientations. Cleavages are not only associated with transgranular fracture, but also with brittle particles as shown in Figure 11 and Figure 12 respectively. Some metals can fail in brittle manner, but do not cleave. These fractures are identified as quasi-cleavage. They are similar to cleavage but their features are usually fairly flat and smaller, as shown in Figure 13 and Figure 14.  Also the intergranular fracture can occur by a number of causes, but it is generally possible to be identified fractographically by the features of grain contours, grain boundaries and triple points. Examples of this kind in tergranular fractures are tree pattern fracture and subcrack caused by the interior pitting deeps, as shown as Figure 15 and Figure 16.

Initial pitting deep and pitting corrosion behavior
Pitting corrosion is a localized form of corrosion by which cavities or deeps are produced in the material. Pitting is considered to be more dangerous than uniform corrosion damage because it is more difficult to detect, predict and design against. Corrosion products often cover the pits. A small, narrow pit with minimal overall metal loss can lead to the failure of an entire engineering system.

Corrosion pit shapes
Pitting corrosion forms on passive metals and alloys like stainless steel when the ultra-thin passive film is chemically or mechanically damaged and does not immediately re-passivity. The resulting pits can become wide and shallow or narrow and deep which can rapidly perforate the wall thickness of a metal.

Pitting shape by ASTM
Pitting corrosion can produce pits with their mouth open or covered with a semi-permeable membrane of corrosion products. Pits can be either hemispherical or cup-shaped. In some cases they are flat-walled, revealing the crystal structure of the metal, or they may have a completely irregular shape. Pitting corrosion occurs when discrete areas of a material undergo rapid attack while most of the adjacent surface remains virtually unaffected.

Experiment pitting shape
Localized chemical or mechanical damage to the protective oxide film; water chemistry factors which can cause breakdown of a passive film are acidity, low dissolved oxygen concentrations and high concentrations of chloride in seawater. The actual pitting shape was investigated by Scanning Electron Microscopy shown as Figure 18 and Figure 19.

Fatigue initial point
In this section, the standard of material specimen is based on JIS G4303, 36 pieces specimen have been prepared for test and separated 4 groups, each group include 3 sets and 1 set include 3 pieces. Firstly, the acceleration pitting corrosion test has been carried out for all sampling pieces. After pitting corrosion, that be separated A, B, C and D group based on the post treatment condition. A group is without post treatment; B group is with solution treatment; C group is with preheated 1400 for metal coating and D group is preheated 200 for metal coating. The metal coating surface had been machined two sets among and other one set without machined for C and D group. The effects of the morphology and matrix structure on the fatigue process as well as fatigue life were examined through metallurgical graphic and fracture graphic observation. The metallurgical graphs of the fatigue fracture surfaces were studied by SEM and are shown in Figures 20 to Figure 23.
Under all test conditions, Figure 20 the crack initiation of Group A, was found to occur at the cavity inside the pitting deep; Figure 21, the crack initiation of Group B, the diameter of pitting deep have clearly increased from 0.1 mm to 0.5 or 1.0mm after solution treatment, and cannot find the fatigue prorogation from the fracture surface. So, the fatigue life cycles had obviously decreased to compare between Table 4 and Table 5; Figure 22 6, the crack initiation of Group C, the microstructure of initiation point have different change with Group A due to the preheated process and can be found to occur at the fracture in the boundary; Figure 23, the crack initiation of Group D, the microstructure of initiation point have cleavage and fracture phenomenon. The tests without the metal surface, cannot find the fatigue initiation point and the fracture point in the near middle of surface, and the fatigue life cycle longer than other specimen.
The observations indicated that the fatigue life is not influenced in significant way by the mechanisms of interaction between dislocations and precipitates. It has been reported that at room temperature the fatigue life is influenced by the deformation mechanism. We believe that in the present work, which has been carried out at elevated temperature and under laboratory atmosphere, the fatigue life is essentially determined by the cracks initiated at the specimen surface as a result of intercrystal line oxidation. This view is supported by the observations, which are reported in Figure

Fatigue fracture mechanism observed by SEM
From the electron fractograhpy of the test specimen, there appears many different kinds of characteristic fatigue fracture features, which encompass the fatigue lines, the fatigue beach marks, the fatigue stair line, the critical crack length and the fracture area, etc.

Fatigue lines
In the fatigue crack propagation process, the fatigue lines appear along and in front of the trajectory of macroscopic plastic deformation at different instant, as shown in Figure 24. Usually, the normal direction of these fatigue lines is referred to as the direction of fatigue crack propagation.

Fatigue beach marks
Beach marks are similar to the fatigue lines that occur before fatigue crack and along the route of plastic deformation traced, as shown in Figure 25. Usually, their normal direction approximately aims at the direction of the fatigue crack propagating. The number and the thickness of beach marks can be considered as the basis to measure fatigue endurance.

Critical crack length
Critical (fatigue) crack length means the limit length of origin crack at which the fatigue crack propagation can occur. Beyond the critical crack length of origin crack source, it represents that the fracture occurs abruptly. Such critical fatigue crack length can be seen in Figure 27.

Fracture area
In the instantaneous fracture zone or referred to as the fracture area, it can be seen some crystalline patterns like river stream and tongue as shown in Figure 28 and Figure 29.

Transgranular and intergranular fracture
In the fracture area, fractures can be categorized as transgranular and intergranular fracture based on the fracture path related to the crystalline grain structure. Transgranular fracture may be ductile or brittle depending on the amount of grain deformation as shown in Figure  30. While intergranular fracture is only pertaining to a brittle one, since the separation of grains is usually due to the presence of brittle interface, as shown in Figure 31.

Pitting corroding rate
The grey systems are the systems that lack information, such as architecture, parameters, operation mechanism and system behavior, for example, for estimating the tendency of Typhoon landing in Taiwan [8]. There are a number of factors that affect the pitting corrosion rate of stainless alloy that include [a, b, c… and x] as previously mentioned.
The grey correlation analysis explains uncertain correlations between one main factor and all other factors in a given system. The grey correlation analysis method is based on the clustering approach in which the time factor is during the experiment period.

Material and experiment procedure
For this test, stainless SUS 630 was used and test specimen with a size of 50(L)*25(B)*5(t) mm. The Chemical property is shown as in Table 3.1 and the mechanical property is as shown in Table 3 The result record is shown as the Table 3 for the dynamic experiment, its unit is 1/100 mm, and the static experiment record is as shown the Table 4. If the unit was selected to mm/100/day, the Table 3 will be changed to Table 5 and Table 4 also changed to Table 6

Influence factor of corroding rate
According to measured data of pitting deep depth, utilizing the analytical method, can be estimated the distribution. Normal distribution can be regarded as the probability distribution of the corroding rate of stainless steel for pitting corrosion.
T-test is most commonly applied when the test statistic would follow a normal distribution if the value of a scaling term in the test statistic were known. The t-distribution is symmetric and bell-shaped, like the normal distribution, but has heavier tails, meaning that it is more prone to producing values that fall far from its mean. Using the Table 2, the upper and lower limit will be calculated by the t-test and t-distribution table shown as Table 7.  Table 7. Pitting deep upper and lower limits (unit mm) of depth F-test is any statistical in which the test static has an F-distribution under the null hypothesis. It is most often used when comparing statistical models that have been fit to a data set, in order to identify the model that best fits the population from which the data were sampled. Exact Ftests mainly arise when the models have been fit to the data using least squares.
The hypotheses that the means of several normally distributed populations, all having the same standard deviation, are equal. This is perhaps the best-known F-test, and plays an important role in the analysis of variance (ANOVA).
In statics, analysis of variance is a collection of statistical models, and their associated procedures, in which the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form ANOVA provides a statistical test of whether or not the means of several groups are all equal, and therefore generalizes-test to more than two groups.
For a defect-free material, pitting corrosion is caused by the chemistry that may contain aggressive chemical species such as chloride. Chloride is particularly damaging to the passive film oxide so pitting can initiate at oxide breaks. The influence factors of pitting corrosion for stainless steel were complex, for example, material defect, solution pH value and temperature etc.

The corrodes rate between pitting depth and material defect
a. Under the conditions of 95% confidence interval, its upper and lower limit value are 0.00989783 and -0.00485264 for Case 1, 0.01245305 and -0.00464923 for Case 2, 0.01151913 and -0.00521705 for Case 3, 0.0095169 and -0.00585718 for Case 4, 0.01460108 and -0.00683025 for Case 5, 0.00966449 and -0.00395963 for Case 6. Based on the abovementioned value, the intersection of extreme value and it's including 0, According to the ttest principle, there is no obvious relation between corroded rate and material defect. b. Using the ANOVA methods calculation and F value to assay, the calculated F value is less than the critical value of F value. So, there is no obvious relation between corroded rate and material defect.

The corrodes rate between pitting depth and pH value of solution
a. Under the conditions of 95% confidence interval, its upper and lower limit are 0.0056934 and -0.00468993 for Case 1 and Case 3 Group A, and 0.00999922 and -0.00974575 for Case 1 and Case 3 Group B, Based on the above-mentioned value, the intersection of extreme value and it's including 0, According to the t-test principle, there is no obvious relation between corroded rate and pH value. b. Using the ANOVA methods calculation and F value to assay, the calculated F value is less than the critical value of F value. So, there is no obvious relation between corroded rate and pH value.

The corrodes rate between pitting depth and solution temperature
a. Under the conditions of 95% confidence interval, its upper and lower limit are 0.01341501 and -0.00468993 for Case 5 and Case 6 Group A, and 0.01476674 and -0.00350632 for Case 5 and Case 6 Group B, Based on the above-mentioned value, the intersection of extreme value and it's including 0, According to the t-test principle, there is no obvious relation between corroded rate and temperature. b. Using the ANOVA methods calculation and F value to assay, the calculated F value is less than the critical value of F value. So, there is not an obvious relation between corroded rate and temperature.

Corroding rate
Theoretically, a local cell that leads to the initiation of a pit can be caused by an abnormal anodic site surrounded by normal surface, which acts as a cathode, or by the presence of an abnormal catholic site surrounded by a normal surface in which a pit will have disappeared due to corrosion. The corroding rate was calculated by the grey theory method and regression method and the result was shown as Figure 33 to Figure 38 using the environmental test data.
The precision of the prediction is around 96.44% and error rate is about 3.56% on average with the grey predication for dynamic test; the precision of the prediction is around 96.97% and error rate is about 3.03% on average with the grey predication for static test. Therefore, the grey model GM (1,1) is an acceptable method for the prediction of the Pitting corrosion depth and rate.

Pitting corrosion fatigue life
As the condition of most pitting corrosion occurring on the surface of stainless steel shafts, when they are operating in fluid of neutral-to-acid solution, which especially containing chloride or chloride ions, there constitute multiple points of stress concentration on the shaft surface . The mechanism of multiorigin fatigue fracture may be more complicated than that of the above mentioned single point stress concentration condition.

Fatigue
In material science fatigue is the progressive and localized structural damage that occurs when a material is subjected to cyclic loading. The nominal maximum stress values are less than the ultimate tensile stress limit, and may be below the yield stress limit of the material.
A fatigue crack often starts at some point of stress concentration. This point of origin of the failure can be seen on the failed material as a smooth, flat, semicircular or elliptical region, often referred to as the nucleus. Surrounding the nucleus is a burnished zone with ribbed markings. This smooth zone is produced by the crack propagation relatively slowly through the material and the resulting fractured surfaces rubbing together during the alternating stressing of the component [1]. When the component has become so weakened by the crack that it is no longer able to carry the load, the final, abrupt fracture occurs, which shows a typically crystalline appearance.
Fatigue occurs when a material is subjected to be repeated loading and unloading. If the loads are above a certain threshold, microscopic cracks will begin to form at the surface. Eventually a crack will reach a critical size, and the structure will suddenly fracture. The shape of the structure will significantly affect the fatigue life; square deeps or sharp corners will lead to elevated local stresses where fatigue cracks can initiate. Round deeps and smooth transitions or fillets are therefore important to increase the fatigue strength of the structure.
According to the observation ways of a metal fracture, the fracture can be categorized into macroscopic and microscopic features. Marcroscopic fracture can be observed or inspected by either naked eyes or a magnifying glass on the surface of the metal or the fracture surface. While the microscopic fracture can be observed only by a optical microscope or a scanning electron microscope (SEM). Owing to the enlarging area and depth is not so extensive by a optical microscope, therefore a SEM is used in this study. On the contrary, SEM can observe a broad range and deeper depth on the fracture surface. Through microscopic fracture surface observation, the crack propagation process and material intrinsic crystalline structure can be identified.

Fatigue life
The fatigue life time depends on several factors, where the most important one sere the manufacturing, the material properties, and the loading conditions, which are all more or less random. Both material properties and dynamical load process are important for fatigue evaluation, and should in more realistic cases be modeled as random phenomena.
Since the well-known work of Wöhler in Germany starting in the 1850's, engineers have employed curves of stress versus cycles to fatigue failure, which are often called S-N curves (stress-number of cycles) or Wöhler's curve.The basis of the stress-life method is the Wöhler S-N curve, that is a plot of alternating stress, S, versus cycles to failure, N. The data which results from these tests can be plotted on a curve of stress versus number of cycles to failure. This curve shows the scatter of the data taken for this simplest of fatigue tests.
ASTM defines fatigue life ( N), as the number of stress cycles of a specified character that a specimen sustains before failure of a specified nature occurs. Fatigue life is the number of loading cycles of a specified character that a given specimen sustains before failure of a specified nature occurs. When analyzing the fatigue life for the structures, the level crossings have been used for a long time. However, better life predictions are obtained when using a cycle counting method, which is a rule for pairing local minima and maxima to equivalent load cycles. Fatigue damage is computed by damage accumulation hypothesis which is illustrated as Palmgren-Miner rule. This rule is used to obtain an estimate of the structural fatigue life.
In high-cycle fatigue situations, materials performance is commonly characterized by an S-N curve, also known as a wdeep curve. This is a graph of the magnitude of a cyclic stress (S) against the logarithmic scale of cycles to failure (N).
S-N curves are derived from tests on samples of the material to be characterized where a regular sinusoidal stress is applied by a testing machine which also counts the number of cycles to failure. This process is sometimes known as coupon testing. Each coupon test generates a point on the plot though in some cases there is a run out where the time to failure exceeds that available for the test. Analysis of fatigue data requires techniques from statistic, especially survival analysis and linear regression.

Fatigue rotating bending test
For this rotating bending testing method followed JIS Z 2247-1978, the material is stainless SUS 630 and the test pieces standard is symbol 1-10 of class 1, and the speed of loading repetition is 2400 rpm. The value obtained by diving the bending moment at a cross section generating the maximum stress by modulus of section shall be used as the nominal stress. The S-N curve will be drawn by plotting the values of stress amplitude as ordinate and the number of stress cycles. Configurations of the test machine and the specimen used in the study are shown as Figure 39 and Figure 40 respectively.

Fatigue life estimation
In this test, the number of cyclic stress reversals with constant amplitudes of 350 N/mm 2 , 400 N/mm 2 , 450 N/mm 2 , 500 N/mm 2 , 550 N/mm 2 and 600 N/mm 2 , respectively, induced in the specimens is counted. In addition, the test for that on uncorroded specimens is also carried out. Hanging equivalent weight on the test specimen and rotating under the revolution speed of 2400 rpm attain these specified stress amplitudes.
Due to the yield stress around 725 N/mm 2 and maximum stress amplitude 600 N/mm 2 in this fatigue test, therefore, the fatigue life should be estimated with statistic method for over 600 N/mm 2 stress. According to the eempirical rule, the estimated theory will be selected the Grey Model GM (1,1) and Curvilinear Regression same as Section 4.
Using the experimentation data as input, a computer program was used to obtain the results in Tables     The precision of the prediction is around 98.31875 with the grey predication;, the precision of the prediction is around 97.54732 with the regress analysis. Therefore, the grey model GM (1,1) is an acceptable method for the prediction of the S-N curve.
The grey system theory has been used to deal with partially known and unknown data for stainless alloy. Traditional prediction usually employs the statistical method for large samples of data. According to the above-mentioned results, the grey system theory is also suitable for use to predict the S-N curve the scarcity of available data.

Ferric Chloride Test (FCT)
In order to accelerate the generation of SUS 630 shaft specimen with various extents pitting corrosion to facilitate the fatigue crack propagation study, the ferric chloride test (FCT) in accordance with the standard of JIS  Table 9. Measured record and average pitting depth of test specimen

Fatigue life of pitting corrosion
The fatigue test was carried out with rotating bending test equipment under the number of cyclic stress reversals with constant amplitudes of 350 N/mm 2 , 400 N/mm 2 and 450 N/mm 2 , respectively, induced in the specimens is counted.
The fatigue life (N f ) can be divided into the number of cycles, (N i ) necessary to initiate a stage I micro crack, which has a size of the order of the grain size, and the number of cycles, (N p ) for crack propagation. Then a crack growth law relating the propagation rate of each microcrack to the applied plastic strain and crack length is derived metallographic observations. The third stage of fatigue damage, which leads to final fracture, is modeled as the coalescence of this population of microcracks which are nucleated and which propagate continuously. The measurement data for the fatigue test belong to the N p in Table 10 to Table 13.
www.intechopen.com  Table 13. Measurement data of the experiment (with metal coating treatment)

Fatigue behavior of pitting corrosion
The test results of the number of cyclic stress reversals till fatigue fracture of the specimens are listed in Table 14. In Table 14, the fatigue lifetime ratio of pitting corrosion condition of the specimen is only about 12 -27% of the fatigue time of uncorroded condition for the case of the working stress variation range 450 N/mm 2 . the range of fatigue lifetime ratios even lower to 5.

Material constant
Failures occurring under condition of dynamic loading are called fatigue failures, presumably because up to the present it has been believed that these failures occur only after a considerable period of service. Fatigue is said to account for at least 90 percent of all service failure due to a mechanical cause.
Fatigue results in a brittle-appearing fracture, with no gross deformation at the fracture. On a macroscopic the fracture surface is usually in line with the direction of the principal tensile stress.
A fatigue failure can usually be recognized from the appearance of the fracture surface, which shows a smooth region, due to the rubbing action caused by the crack propagation through the section; and a rough region, due to the member failing in a ductile manner caused by the cross section being no longer able to carry the load.

Fatigue crack propagation
Considerable research has gone into determining the laws of fatigue crack propagation for stage II growth. The crack propagation rate da/dN is found to follow an equation: Since the stress intensity factor is undefined in compressions, K min is taken as zero if σ min is compression.
Region II represents an essentially linear relationship between log da/aN and log ΔK ( ) where:

Estimation the Fatigue crack propagation
If let the fatigue crack propagation for each cycle revolution is μ, then, where: a crack depth N cycle revolution First, measure the pitting deep depth and assume the depth is the crack beginning depth (a 0 ), then, measure the depth of the smooth area and assume the depth is the fatigue propagation depth (a p ), next, get the beginning depth plus the fatigue propagation depth and assume the depth is the critical depth (a c ). Finally, use the equation (6)  The crack propagation ratio is show in the formula (7) when the structure has been loaded with a stable alternating stress. where: The Paris formula can be deduced from the relations between the ratio of fatigue propagation and the crack depth as follows: where: C, n material constant; Y geometry factor; Δσ the difference between maximum stress (σ max ) and minimum stress (σ min ); a crack depth with the giving structure and the constant alternate load (Δσ), the equation (9) can be rewritten as follows: where: The constant C 0 and n can be decided with the following method, if, both sides of the equation (11) (13) and act as a go-between to all or division. The captioned formula can be derived as 0 C and 2 n , and will be able to comply with equation (12), then p N can be derived from equation (12).

Methodology
The Ferric Chloride Test (FCT) has been carried out same as the above 5.3.1 for the pitting corrosion and the fatigue tests are carried out for the sampling pieces; then, the metallurgical graphics are taken through the Scanning Electron Microscope (SEM) for the fatigue fracture surface.
The fatigue test result data and measuring the initial crack and critical crack depth from the SEM graphic are shown in the Table 15, Table 16 and Table 17.  Figure 43 show the metallurgical graphics of the fatigue fracture surface with the SEM. The graphics allow to measure the initial crack depth (pitting deep depth) and to calculate the critical crack depth. The result is shown as Table 15 to The fatigue crack propagation can be estimated using the data of Table 15 to Table 17 and the results that can be obtained as shown in Table 18. Table 18 gives the ranges of constant n (2.7 to 3.0), constant (C 3.47E-15 to 1.72E-14) and da/dN (8.39E-7 to 1.47E-6). The tendency curves of the constant n, C and da/dN are shown in Figure 44 to Figure 46.

Summery
Present knowledge about the relations between the microstructure changes and durability is still limited. This is the complex problem and it is necessary to deal with it as the separate question in the low-cycle fatigue durability estimation methodology. Material properties change in the low-cycle fatigue process, because most of the structural materials as steel for example are metallurgical unstable when they are cyclically deformed particularly at high temperature. Their dislocation structure changes determine the crack initiation in steel and their macroscopic behavior.
The fatigue lifetime of SUS 630 shaft under various extent of pitting corrosion condition is found to be in a range of only 2.5~27% of that of the uncorroded condition. These results are obtained by the ferric chloride test and rotating bending fatigue test on the SUS 630 shaft specimen. Apart from the feature of common fatigue fracture starting from a single point of stress concentration, i.e., forming initial stress nucleus, propagating ribbed markings and complete fracture zone, there existed much more sophisticated features on the fatigue fracture surfaces generated by multi points of stress concentration around the pitting deeps. Such as the fatigue lines, the fatigue beach marks, the fatigue stair lines, the critical crack length, the river stream pattern, tongue pattern and tree pattern in fracture area, transgranular and intergramular fractures, etc., have been detected and identified by using a SEM in the study.
Fatigue life showed difference depending on the matrix structure. The apparent difference of the fatigue life between the stress and the strain controlled cycling was well correlated in each material by means of cyclic stress-strain relationship. The grey system theory has been used to deal with partially known and unknown data for stainless alloy. Traditional prediction usually employs the statistical method for large samples of data. According to the above-mentioned results, the grey system theory is also suitable for use to predict the S-N curve the scarcity of available data. St r e ss overload, metallurgical structure, residual stresses, and combined stresses, which tend to alter the conditions of fatigue. Since we have not yet gained a complete understanding of what causes fatigue in metals, it will be necessary to discuss each of these factors from an essentially empirical standpoint. Due to the availability of a great mass of data, it would only to describe the highlights of the relationship between these factors and fatigue.
Constant n will be increased when the stress strength is increased. The rise ratio is rapid when the stress strength is less than the fatigue stress, the rise ratio is slow when the stress strength is bigger than the fatigue stress; Constant C will go down when the stress strength is less than the fatigue stress, constant C will go up when the stress strength is bigger than the fatigue stress; Constant da/dN will go up when the stress strength is less than the fatigue stress, The constant da/dN will be decreased when the stress strength is bigger than the fatigue stress. The fatigue strength is a breakpoint for the material constants n, C and da/dN of the fatigue fracture for the stainless SUS 630.