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Detection and characterization of stress-corrosion cracking on 304 stainless steelby electrochemical noise and acoustic emission techniques

G. Du a , , J. Lia , W.K. Wang a , C. Jiang a , S.Z. Songba State Key Laboratory of Precision Measurement Technology and Instrument, Tianjin University, Tianjin 300072, Chinab School of Materials Sciences and Engineering, Tianjin University, Tianjin 300072, China

a r t i c l e i n f o

Article history:Received 13 January 2011Accepted 7 May 2011Available online 12 May 2011

Keywords:A. Acid solutionsA. Stainless steelC. Acid corrosionC. Stress corrosion

a b s t r a c t

This paper focuses on the corrosion process of 304 stainless steel in acidic NaCl solution during slowstrain rate testing experiment by using electrochemical noise (EN) and acoustic emission (AE) techniques.Meanwhile, the EN and AE characteristics of corrosion process were studied. The results show that stresscorrosion occurs easily in the experimental system, and corrosion forms develops gradually from local-ized corrosion including stress corrosion and pitting corrosion to general corrosion. The AE signal char-acteristics of pitting corrosion, crack and bubble break-up are signicantly different during thecorrosion process.

2011 Elsevier Ltd. All rights reserved.

1. Introduction

Three hundred and four austenitic stainless steel has beenwidely applied in the nuclear industry due to its excellent resis-tance of corrosion and mechanical properties. As more pressurizedwater reactors (PWR) have been brought into service in nuclearpower plants progressively in China, problems of 304 stainlesssteels are increasingly serious. Amongall the risks, stress-corrosioncracking (SCC) is one of the greatest. SCC is a well-known degrada-tion process for metals and alloys. The process is potentially dan-gerous and can lead to catastrophic failures of structuralcomponents [13] . Because of its unpredictability and catastrophicconsequences, the safe application of such materials in nuclearpower systems is a considerable challenge. Thus, research intothe SCC behavior of stainless steels for nuclear power service isof great practical signicance.

Most corrosion measurements are based on electrochemicalmethods. Electrochemical noise (EN) consists of potential and cur-rent uctuations spontaneously generated by corrosion reactions,and non-intrusiveness is one of the main advantages of this tech-nique [4,5] . EN technique has been increasingly used in eld appli-cations [69] . The correlation between corrosion activity and ENresponse has been reported in many studies during the past30 years [1013] . Several studies have indicated that, on the basisof EN measurements, it is possible to detect and distinguish

between different corrosion types [1417] . These can be applied

in the detection and evaluation of corrosion activity.Another technique that is suitable for the detection of individ-ual events during SCC is acoustic emission (AE). AE technique is anon-destructive dynamic testing technique. This technique isbased on measurements of elastic waves, which are the conse-quence of fast energy relaxation at a localized source. Due to itsexcellent capabilities, the AE technique has been widely used indetection of material deformation and corrosion [1835] . In recentyears, since SCC was a combination of mechanical and electro-chemical processes, several workers have applied AE and EN tech-niques simultaneously into the research of SCC. They have foundthat SCC initiation and its early stage propagation could be de-tected by EN measurements, whereas the AE technique is sensitiveto rapid crack propagation involving a relatively large volume of plastic deformation [3641] .

In our previous research, SCC of 304 stainless steel C-ring sam-ple with constant load was detected by EN and AE techniques. Theaverage statistical characteristics of the AE signals generated bythe dominant corrosion behavior were measured. The results indi-cated that the characteristics of AE signals, such as waveform andfrequency spectrum, differ signicantly at different stages of thecorrosion process. Furthermore, the AE test results were conrmedby the EN test results [42] . However, the AE signals as results of corrosion are usually merged with emissions from several typesof acoustic sources. The detected signals which were unrelated tocorrosion-relevant emissions may lead to incorrect AE corrosionanalysis results. As a result, classication and characterization of different AE sources are necessary to carried out.

0010-938X/$ - see front matter 2011 Elsevier Ltd. All rights reserved.doi: 10.1016/j.corsci.2011.05.030

Corresponding author. Tel.: +86 22 27890676; fax: +86 22 27890026.E-mail address: [email protected] (G. Du).

Corrosion Science 53 (2011) 29182926

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Corrosion Science

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http://dx.doi.org/10.1016/j.corsci.2011.05.030mailto:[email protected]://dx.doi.org/10.1016/j.corsci.2011.05.030http://www.sciencedirect.com/science/journal/0010938Xhttp://www.elsevier.com/locate/corscihttp://www.elsevier.com/locate/corscihttp://www.sciencedirect.com/science/journal/0010938Xhttp://dx.doi.org/10.1016/j.corsci.2011.05.030mailto:[email protected]://dx.doi.org/10.1016/j.corsci.2011.05.030

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In this study, the main goal was the detection and characteriza-tion of SCC during slow strain rate testing (SSRT) on 304 stainlesssteel specimen by means of EN and AE techniques. The corrosionbehavior of specimen was analyzed, and the correlations of dataobtained by both techniques were studied. The characteristics of different AE sources in corrosion process were studied. The resultsfrom this study could be used in the future to investigate corrosionbehaviors of stainless steel in nuclear power equipment.

2. Materials and methods

2.1. Materials used in the experiment

In the research, a 304 nuclear grade (NG) stainless steel platewas provided by Taiyuan iron and steel company (TISCO) whosechemical composition and mechanical properties can be seen fromTables 1 and 2 . The steel plate was made into the specimen byusing a Wire Cutter Electrical Discharge Machine (WEDM). Thethickness of specimen is 2 mm, and Fig. 1 shows its shape and size.The surface of the specimen was abraded with silicon carbide pa-per (from 400# to 2000#), decontaminated by ultrasonic wave,

and put to use in the experiment after being cleaned with alcohol.

2.2. Experimental setup

Fig. 2 shows the experimental setup. In the process of the exper-iment, the tensile specimen was installed in the electrolytic cell,sealed with silicone rubber, strained by slow strain rate testingsystem. The extension rate was 0.1 l m/s, and the gauge lengthwas 20 mm, so the strain rate equaled to 5 10 6 s 1 , which wasthe value of the extension rate divided by the gauge length. Thetest solution was 0.5 mol/L NaCl blended with 1.5 mol/L H 2SO4 ,which was likely to accelerate corrosion of the specimen [43] .The pH of the solution was 0.6. A long-distance microscope(Questar QM-100) was used to observe in situ the surface of spec-imen. All the tests were accomplished at room temperature.

2.3. Electrochemical noise and acoustic emission measurement systems

EN was measured in a freely corroding system (without anexternally applied current or voltage) between three electrodes.The EN system comprised a CFP industrial controller module(National Instruments, NI), a Field-Point analog input moduleCFP-AI-118 (NI) and a ZF3 Potentiostat (Shanghai Zhengfeng Com-pany), which was used to constitute the zero resistance ammeter(ZRA) test circuit [44,45] . The CFP system was used to acquirepotential and current data. The potential and current were ampli-ed and digitized by using a 16-bit A/D converter with a resolutionof 0.03 mV, and 0.3 nA. The sampling frequency for data acquisi-tion was 2 Hz. In the test circuit, an antimony electrode was usedas the reference electrode. In our previous study, the antimonyelectrode had been applied in the tests with good performance[42] . The reaction of antimony electrode was 2Sb + 3H 2OSb2O3 + 6H+ + 6e . The potential of the antimony reference

Table 1

The chemical composition of 304NG (wt.%).

C Si Mn P S Cr Ni N Cu Co B Fe

6 18.5020.00 9.0010.00 60.035 1.00 2.00 0.030 0.015 0.080 1.00 0.06 0.0018 Balance

Table 2The mechanical properties of 304NG stainless steel.

Tensile strength (MPa) Yield strength (MPa) Elongation (%)

P 520 P 210 P 45

Fig. 1. Geometry of the specimen used in slow strain rate testing experiment.

Fig. 2. Schematic diagram of the measurement system.

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contains the EN data of other segments. Fig. 7 shows the PSD of electrochemical potential noise during SSRT (segment A, G). Table3 indicates as well that noise resistance is in an increasing trend ingeneral. The potential reason is that the corrosion products accu-mulated on the surface of the specimen alleviate the corrosion ef-fect. Uruchurtu has found that, while the roll-off slope of PSD ismuch lower than 20dB dec 1 , the material is in uniform corro-sion or in passivation state. When the roll-off slope is higher than

20dB dec 1 , it is usually the symbol of localized corrosion [47] .From the value of the roll-off slope K of PSD of electrochemical po-tential noise during different time segments shown in Fig. 7 andTable 3 , before 2000 s (A, B, C time segment), the values of roll-off slope K were all greater than 20dB dec 1 , while the amplitudeof potential noise and current density noise were much larger

(Fig. 6 ), which demonstrated that localized corrosion including pit-ting and crack propagation might have occurred on the surface of

the specimen. After 2000 s (E, F, G time segment), as the experi-ment went on, the values of roll-off slope K were all less than

20dB dec 1 (Fig. 7 and Table 3 ), while the amplitude of potentialnoise and current density noise decreased markedly ( Fig. 6 ), whichindicated that the specimen was in the period of general corrosion.Surface morphology of the specimen at various stages of SSRTexperimentwas shown in Fig. 8 . It can be observed that pitting cor-rosion and crack propagation appeared in the specimen surface be-fore 2000 s ( Fig. 8 b and c), which demonstrated that localizedcorrosion had occurred on the surface of specimen at the initialstage of the experiment. As was seen from Fig. 8 d, serious generalcorrosion occurred on the surface of specimen at the later stage of the experiment. Hence, the result of EN data analysis was con-rmed by the experimental evidence.

Fig. 9 shows AE hits ring down counts change with time. The AEtesting system began to receive AE signals from 480 s. This was in

Table 3

Parameter of 304 stainless steel EN analysis during long term SSRT.

Number Starting time (s) Potential standarddeviation (mV)

Current standarddeviation ( l A/cm 2)

Noise resistanceRn (X cm 2)

Roll-off slopeK (dB/dec)

White noiselevel (V 2/Hz)

A 30 0.241 3.87 62.29 15.85 62.44B 600 0.286 3.61 79.35 17.5 54.58C 1200 0.304 3.21 94.64 18.28 56.44D 2000 0.193 2.13 90.62 23.59 72.37

E 4000 0.114 1.12 102.04 29.25 62.81F 5000 0.165 1.41 117.36 30.36 59.95G 6000 0.148 1.05 141.25 31.11 56.3

Fig. 6. Potential and current density noise of 304NG in different time during SSRT (a) segment A: 30542s, and (b) segment G: 60006512s.

Fig. 7. PSD of electrochemical potential noise and current density noise during SSRT (a) segment A: 30542 s, and (b) segment G: 60006512s.


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accordance with the time when a transient peak began to occur inthe current noise of the specimen. Before 1000 s, the AE ring downcounts were increasing with time and reached the rst peak atabout 1000 s and meanwhile pitting corrosion occurred on the sur-face of the specimen ( Fig. 8 b). After 1000 s, the number of AE sig-nals decreased, while the potential was increasing gradually andthe current was decreasing gradually ( Fig. 5 ). After 1500 s, the AEring down counts increased again, and the maximum peak oc-curred at about 2000 s whilst crack propagation occurred on thesurface of the specimen ( Fig. 8 c). After 5000 s, the number of peaks

of AE ring downcounts reduced, and the peak value also decreased,while general corrosion occurred on the surface of the specimen

(Fig. 8 d). Du found that the characteristics of AE signals at differentstages of corrosion process (localized corrosion, general corrosion)differ signicantly [42] . Some workers have studied the mode of SCC propagation which generated the AE events. Alvarez andGerberich found that the AE activity during the propagation of transgranular SCC (TGSCC) was one order of magnitude higherthan the AE activity during the intergranular SCC (IGSCC) propaga-tion in SSRT experiment [48,49] . In our study, the material andexperimental conditions were generally similar to theirs, and both304 stainless steel and acidic NaCl solution were in use. Thus, inthis study the mode of SCC propagation generating AE eventswas mainly regarded as TGSCC. From the measurements of corro-sion process by applying EN and AE techniques simultaneously, itcan be found that AE test result was fundamentally consistent withthat of EN test.

In the process of the study, in order to conduct an in-depth

study of the corrosion behavior of the specimen, all the AE signalswere classied, and then the characteristics of different types of AEsources were extracted. Several classifying methods were used tond clusters of similar records. Available methods are MaxMinDistance, K -means, Forgy, Cluster-Seeking, Isodata, and L.V.Q. (aneural net model). K -means is a simple iterative algorithm, aimingto minimize the square error for a given number of clusters. Thealgorithm, starting with the initial clusters specied, assigns theremaining points to one of the predened clusters by nearestneighbor classication. The cluster centers are updated and theprocess continues until none of the patterns changes class mem-bership [50,51] . In this study, K -means clustering algorithm wasapplied to classify the AE signals, which used the characteristicparameters including ring down count, duration, rise time, ampli-

tude, and energy [52] . Several workers participated in someresearch work on the AE sources in corrosion process. Shaikh

Fig. 8. Surface morphology of 304 stainless steel in different time during SSRT (a)initial surface before SSRT, (b)pitting corrosion at 950 s, (c)crackpropagationat 2000 s, and(d) general corrosion at 7000 s.

Fig. 9. AE hits ring down count distribution with time during SSRT.

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pointed out that AE events occurred in bursts during crack growth[53] , and Cakir concluded that there was a close correlation be-tween pitting corrosion and AE events [54] . Besides, Jirarungsatianfound that bubble break-up was one of the categories of AE sourcesin corrosion scenarios [55] . Based on these observations, we be-

lieved that three potential AE sources might exist in our experi-mental system, namely crack propagation, pitting and bubblebreak-up. Therefore, we classied AE signals into three clusters(cluster02). After classication, AE hits ringdown count distribu-tion with time is shown in Fig. 10 . AE signals in cluster 0 were dis-tributed more intensively after 1500 s, the maximum ring downcount being less than 35 ( Fig. 10 b). The AE signals in cluster 1are dispersed before 5000 s, but intensively grouped after 5000 s,and the ring down counts were all above 20 ( Fig. 10 c). The numberof AE signals in cluster 2 was the largest among the three clustersand the ring down counts distributed intensively all the time withthe value less than 25 ( Fig. 10 d).

Fig. 11 shows cumulative curves of the threeclusters in terms of the AE hits ring down counts. As can be seen from Fig. 11 , the ring

down counts of AE signals in cluster 0 increased stably before2000 s, but increased dramatically from 2000 to 5000 s. The ring

down counts of AE signals in cluster 1 increased quite slowly be-fore 5000 s, but increased rapidly after 5000 s (the later stage of SSRT). AE signals in cluster 2 existed during the whole corrosionprocess, and increased stably.

In order to know the characteristic of each clusters signals,

wavelet packet decomposition method was applied to extract thefrequency band energy of the AE signals. Wavelet packet decompo-sition (WPD) is a wavelet transform where the signal is passedthrough more lters than the discrete wavelet transform (DWT)[5659] . It could produce a more desirable representation for aparticular signal. The results of characteristic extraction are shownin Figs. 1214 . The AE signals in cluster 0 had lower amplitude, of approximately 3 mV. Their frequency band energy mainly concen-trated below 100 kHz ( Fig. 12 ). The AE signals in cluster 1 had lar-ger amplitude, approximately 10 mV. Their frequency band energymainly concentrated between 250 and 320 kHz ( Fig. 13 ). The AEsignals in cluster 2 having the amplitude of about 4 mV, their spec-tral components were abundant, and the frequency band energymainly distributed above 150 kHz ( Fig. 14 ). As was described

above, the waveform and frequency band energy characteristicsof three cluster AE signals differed signicantly.

Fig. 10. AE hits ring down count distribution with time after cluster analysis (a) original data, (b) cluster 0, (c) cluster 1, and (d) cluster 2.


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The ring-down counts of AE signals in cluster 1 increased quiteslowly before 5000 s, but rapidly after 5000 s, which was consis-tent with the trend of cracks from initiation to propagation duringthe process of SSRT. The frequency band energy of AE signals incluster 1 mainly concentrated on high frequency band (from 250to 320 kHz), which was in accordance with the characteristics of crack signal [60] . Therefore, the AE signals in cluster 1 were consid-ered to be generated by cracking. The amount of AE signals in clus-ter 0 turned to increase slowly after 5000 s, which was consistentwith the result of EN test (the variation trend of standard deviationof potential and current density noise after 5000 s). The frequencyband energy mainly concentrated on low frequency band (below100 kHz). The AE signals in cluster 2 existed in the whole corrosionprocess and increased stably. Spectral components of the AE sig-nals in cluster 2 were more abundant, and the frequency band en-ergy distributed mainly above 150 kHz. In our experiment, a largenumber of bubble break-ups were occurring during the whole pro-cess of pitting corrosion by visual observation. It demonstratedthat the AE signals generated by pitting corrosion and bubblebreak-up were merged in the corrosion process. Jirarungsatianacquired the frequency of bubble break-up both from theoretical

Fig. 11. AE hits ring down counts accumulate with time after cluster analysis.

Fig. 12. Typical waveform and frequency band energy of cluster 0 (a) waveform, and (b) frequency band energy.



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calculation and experiment, and observed that the resonance fre-

quency of bubble break-up was above 125 kHz. He also found thatthe frequency of pitting was between 3 and 125 kHz [55] . Analo-gous conclusions were drawn in our study as well. Therefore, itwas concluded that signals in cluster 0 and cluster 2 were respec-tively generated by pitting corrosion and bubble break-up.

4. Conclusions

Based on the analyzed results of AE and EN measurements, sev-eral conclusions can be drawn:

(1) EN and AE measurement techniques can both be used todetect the occurrence of stress corrosion. The AE and EN testresults are fundamentally very similar. Simultaneous appli-

cation of AE and EN analysis could improve the reliabilityof on-site test results.

(2) In the solution of 1.5 mol/L H 2SO4 + 0.5 mol/L NaCl, underthe SSRT condition, 304NG stainless steel is prone to theoccurrence of SCC. In this system, the corrosion forms of 304NG stainless steel develop gradually from localized cor-rosion to general corrosion.

(3) Based on K -means cluster algorithm, different AE sources areclassied. Andthe results indicate that the AE characteristicsof different AE sources, such as pitting, cracking, and bubblebreak-up, differ signicantly, whichcould be of much help inanalyzing and judging the corrosion situation.

Acknowledgments

This work wassupportedby SpecialFunds forthe MajorState Ba-sicResearchProjects(No. 2006CB605004) andBasicApplicationandCutting-edge Technology Research Plan Program of Tianjin(09JCYBJC02200). The infra-structural supports from the TianjinUniversity and the Tianjin Institute of Seawater Desalination andMultipurpose Utilizationare alsoacknowledged. Thankfor theguid-ance given by Prof. Shijiu Jin from Tianjin University. Thank for thesuggestions given by Mr. Jian Xu from Institute of Metal Research,Chinese Acadamy of Sciences.

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