using acoustic emission in fatigue and fracture materials research

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CO TE TS

INTRODUCTIONTRADITIONALACOUSTIC-EMISSIONTECHNIQUE

AE Signals duringTensile TestingAE Signals duringFatigue TestingCorrosionAE in Fatigue andFracture of Composites

SOURCE-FUNCTIONAND WAVEFORMANALYSIS

AE Signals of Cracking in 3-DSolidsWave Propagation inRods and Plates

DISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSReferences

The following article is a component of the ovember 1998 (vol. 50, no. 11) JOM and is presented as JOM-e . Such articles appear exclusively on the web and do

not have print equivalents.

ondestructive Evaluation: Overview

Using Acoustic Emission in Fatigue and FractureMaterials ResearchMiinshiou Huang , Liang Jiang , Peter K. Liaw , Charlie R. Brooks , Rodger Seeley , and Dwaine L.Klarstrom

Acoustic emission is a technique to monitor defect formation and failures in structural materials used in services or laboratories.Moreover, the method has been developed and applied in numerous

structural components, such as steam pipes and pressure vessels, and in the research areas of rocks, composite materials, and metals. In thisarticle, the basic concept, terminology, theoretical modeling, and common equipment setup associated with acoustic emission aredescribed. Most of the literature available uses the traditional technique, which only captures acoustic-emission parameters,including acoustic-emission counts, peak levels, and energies. These

parameters can be correlated with the defect formation and failures.Some of the researchers analyze the waveforms of acoustic emission as

functions of sources and wave-propagation mechanisms. Above all,acoustic emission was found to be an effective way of detecting fatigueand fracture behaviors of materials.

I TRODUCTIO

Acoustic emissions (AEs) are the stress waves produced by the suddeninternal stress redistribution of the materials caused by the changes inthe internal structure. Possible causes of the internal-structure changesare crack initiation and growth, crack opening and closure, dislocationmovement, twinning, and phase transformation in monolithic materialsand fiber breakage and fiber-matrix debonding in composites. 155 Mostof the sources of AEs are damage-related; thus, the detection andmonitoring of these emissions are commonly used to predict materialfailure.

Besides the applications of AE in research endeavors, AE has beenwidely used in industries, including for the detection of faults or leakagein pressure vessels, tanks, and piping systems. AE is also used tomonitor the welding and corrosion progress.

Acoustic Emission in Fatigue and Fracture Materials Research http://www.tms.org/pubs/journals/JOM/9811/Huang/Huang-9811.html

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The difference between the AE technique and other nondestructive evaluation (NDE) methods is that AEdetects the activities inside the materials, while other NDE methods attempt to examine the internalstructures of the materials. Furthermore, AE only needs the input of one or more relatively small sensors onthe surface of the structure or specimen being examined so that the structure or specimen can be subjected tothe in-service or laboratory operation while the AE system continuously monitors the progressive damage.Other NDE methods, such as ultrasound and x-ray, have to access the whole structure or specimen, andtherefore, the structure or specimen often needs to be disassembled and taken to the laboratory to beexamined.

The disadvantage of AE is that commercial AE systems can only estimate qualitatively how much damage isin the material and approximately how long the components will last. So, other NDE methods are still neededto do more thorough examinations and provide quantitative results. Moreover, service environments aregenerally very noisy, and the AE signals are usually very weak. Thus, signal discrimination and noisereduction are very difficult, yet extremely important for successful AE applications. The research on AE can

be generally divided into two categories

Traditional acoustic emissionSource-function and waveform analysis

TRADITIO AL ACOUSTIC-EMISSIO TECH IQUE

The traditional AE method only captures certain parameters (sometimes called AE features), including AEcounts, peak levels, and energies. Then, the AE features are correlated with the defect formation and failures.These AE characteristics are only related to the captured signals and do not account for the source of thesignal and wave propagation.

Figure 1. The definitions for acoustic-emission events.

Figure 2. A typical AE system setup.

Figure 1 shows a burst AE signal and the commonly used parameters of AE techniques. When the AEtransducer senses a signal over a certain level (i.e., the threshold), an AE event is captured. The amplitude of

the event is defined at the peak of the signal. The number of times the signal rises and crosses the threshold isthe count of the AE event. The time period between the rising edge of the first count and the falling edge of the last count is the duration of the AE event. The time period between the rising edge of the first count andthe peak of the AE event is called the rise time. The area under the envelope of the AE event is the energy.

Figure 2 presents a typical AE system setup. The AE transducers are generally very sensitive piezoelectricsensors. Because the traditional AE technique only uses AE features, the actual waveforms are not critical tothis method. The AE sensors (transducers) used are usually resonance sensors, which are only very sensitiveto a certain frequency. Since the AE signals are very weak, a preamplifier is connected right after the AEtransducer to minimize the noise interference and prevent the signal loss. Sometimes, the transducer and the


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Figure 3. A tensile stress-strain curve

and AE signals.15

Figure 4. Three-dimensional plots of AEevents, cycles, and stresses. 7,15

Figure 5. The AE count versus thenumber of fatigue cycles, where R is theload ratio that is equal to the minimumload over the maximum load duringfatigue testing. 7,15

preamplifier are built as a unit. Then, the signals pass through a filter to remove the noise. The signals areamplified by the main amplifier before being sent to the signal conditioner. After that, the AE features aresubtracted and stored in a computer for further analysis. During investigations, other parameters, such asload, deformation, pressure, and temperature, can also be recorded as parametric inputs.

AE Signals during Tensile Testing

Acoustic-emission activities have been shown to relate to differentstages of tensile tests of materials. 6,14 ,15 Figure 3 presents thecumulative AE count, AE count rate, and stress versus strainrelationship during a tensile test. 15 The cumulative AE count is the sumof the count of all AE events. The AE count rate is the time derivativeof the AE cumulative count. The beginning portion of the linear elasticregion is very quiet (i.e., low count rates and cumulative counts) or isassociated with an incubation stage. The AE activity reaches its peak inthe second stage right before yielding occurs. After the material yields,the AE activity decreases, but is still detectable until the material fails.

AE Signals during Fatigue Testing

Fatigue tests are usually long-term experiments. A great amount of signals, including the noises from the load-chain, are detected by thesensitive AE sensors during fatigue testing. Therefore, signal screeningmethods should be used to filter out the unwanted signals. One of theeffective methods to screen out noises is to put guard sensors at bothends of the gauge section of the specimen. 7,15 According to the timesequence for the guard and main sensors to receive the signals, thesignals originating from outside the test section can be detected anddiscarded.

AE signals during fatigue tests can be caused by various mechanisms,such as dislocation movement, cyclic softening, crack initiation, crack closure, and ultimate failures. Berkovits and Fang 7,15 used three-dimensional (3-D) plots of AE events, cycles, and stresses (Figure 4) todiscriminate the AE signals due to plastic deformations, crack activities,and closures in a smooth-plate sample of INCOLOY 901 subjected tofatigue loading. For the beginning cycles of loading, the AE signals inthe high-stress region are obviously related to the plastic deformation.

Crack initiation was determined by the first appearance of the AEsignal at low stress levels. After the crack initiated, the AE signalsaround the zero stress were thought to be caused by crack-face grindingwhen the cracks were closed. By discarding the AE signals due to thecrack opening and closure, a plot of the AE count versus the number of fatigue cycles was made of the three damage stages (Figure 5). The firststage corresponds to the first few cycles before the cyclic stress-straincurve stabilized. The AE signals in the first stage result from dislocationmovements and cyclic hardening or softening. The second stage, whichis the crack-incubation stage, is very quiet. This stage has a steady-state


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Figure 6. Acoustic-emission activities of

a smooth-bar fatigue sample of anULTIMET superalloy at differentfatigue cycles. 24

dislocation motion that will eventually result in microvoids and initiatemicrocracks. The third stage is an AE-active stage. In this stage, cracksstart to grow and propagate. Many of the AE signals in the third stagecan come from the crack-tip plastic deformation, fracture of hardinclusions, microcrack coalescence, transgranular cleavage, andfracture along grain boundaries. 15

Fatigue Crack Initiation

High-sensitivity AE sensors can pick up signals at an early stage of fatigue crack initiation, such as dislocation movement and slip-bandformation. A combination of AE and electron microscopy during

fatigue testing of carefully polished smooth samples can be a very powerful tool in the study of fatigue crack-initiation behavior. We investigated the surface of polished smooth-bar samples under a scanning electronmicroscope (SEM) at different stages of fatigue tests when heavy AE activities were observed. Figure 6shows several jumps on the curve of the cumulative AE count versus cycles. 24 The test was conducted on acobalt-based superalloy, ULTIMET alloy, from Haynes International. The maximum stress was 644.8 MPawith a ratio of 0.05, where

R = min/ max

min and max are the applied minimum and maximum stresses, respectively. The frequency was 5 Hz initiallyto study crack-initiation behavior. After 2,000 cycles, the frequency was increased to 25 Hz to shorten thetest time.

The sample was electropolished before the test started and examined under an SEM. The surface was nearly perfect except for some defects, as shown in Figure 7a. After approximately 600 cycles (Figure 7b), heavyAE activities were observed, the test was stopped, and the specimen was examined. The same procedure wasrepeated for 2,000 and 16,000 cycles, during which time the specimen was examined following heavy AE

activities (Figures 7c and 7d). It was clear that the slip bands grew denser and deeper after each jump of cumulative AE counts. However, how the slip bands evolved between jumps is still not clear. Further investigation is needed to determine whether slip bands suddenly grew when jumps of acoustic activitiesoccurred or grew steadily throughout the fatigue test. The areas near the surface defects were examinedcarefully, but intensive slip bands around the defects were not found.

a b


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Figure 8. The AE cumulative countversus crack-growth rate and stress-intensity-factor range in the log-log plotfor AISI 316 stainless steel. 38 (Note thatduplicate samples, T6 and F6, aresolution-annealed; T3 and F3 are agedat 1,023 K for 2 h; 4 and 5 are aged at

Figure 9. The AE countrate versus (Ba'/E) 1/2 K/(1 R) in the log-log plotfor steel and aluminumalloys. 21

c d

Figure 7. Micrographs of the surface of a smooth-bar fatigue sample of an ULTIMET superalloy (a) prior to testing and at (b) 600 cycles, (c)2,000 cycles, and (d) 16,000 cycles. 24

Fatigue Crack Growth

Acoustic emissions also have been used during fatigue crack-propagation tests. All the results from the previous research agree that as the AE count rate increases, the crack-growth rate increases. Nevertheless,how the increase of the AE count rate is related to that of the crack-growth rate varies with differentmaterials. 15,26,2931 ,38,51 The investigations on INCOLOY 901, 15 aluminum alloys, 29,30 and somesteels 26,51 indicated that a log-log plot of the AE cumulative counts and crack-growth rate versus stress-intensity-factor range was linear. The same linear log-log relationship was also found between the AE countrate and the J-integral range of JIS SS41 steel. 31

On the other hand, the log-log plot of the AE cumulative count versus crack-growth rate and stress-intensity-factor range in the AISI 316 stainless steel 38 was bilinear within the linear Paris regime (Figure 8). Theexplanation is that for many steels and aluminum alloys, the linear Paris regime actually can be divided intotwo sub-regimes in which the fatigue crack-growth mechanisms are different. At lower growth rates, thecrack may not grow in each cycle, so the AE count drops very fast in this region. 38 In Figure 8, the heattreatments, including aging time, had significant effects on the AE counts, even though the rates of fatiguecrack propagation are insensitive to heat treatments. Thus, AE could be used to monitor aging conditions of in-service structural components.

Note that the relation between the AE count rateversus stress-intensity-factor range is dependenton the R ratio. Harris and Dunegan 21 found a wayto collapse all the relations at different R ratiosinto one by plotting the AE count rate versus thefunction (Ba'/E) 1/2 K/(1 R), where B is the

thickness of the specimen, E is the Youngsmodulus, a' is the crack-growth rate (a' = da/dN),and K is the stress-intensity-factor range. Theresults for both steel and aluminum alloys areshown in Figure 9. The importance of the

parameter, (Ba'/E) 1/2 K/(1 R), lies in the factthat this parameter equals the square root of theenergy released per cycle, which ideally should bedirectly related to the AE energy, asdemonstrated.


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the same temperature for 8 h.)Corrosion

Oftentimes, structural materials experience fatigue loading during and after exposure to air, water, or other corrosive environments. Therefore, corrosion failures usually need to be included when investigating fatigue

problems. Moreover, corrosion failures also occur under static loading, which must be considered instructural-integrity analyses.

Yuyama55

reviewed the fundamental aspects of AE in corrosion. His article reviews the applications of theAE technique to detecting and monitoring active corrosion, stress corrosion cracking, hydrogenembrittlement, corrosion fatigue, and intergranular stress corrosion cracking in aluminum, aluminum alloys,magnesium alloys, steels, stainless steels, and others (e.g., copper and its alloys, uranium alloys, titanium, andzirconium alloys). Possible sources of AE signals are crack initiation and growth induced by stress corrosioncracking and hydrogen embrittlement; dissolution of metal; hydrogen gas evolution; the breakdown of thick surface-oxide films; the fracture or decohesion of phases, such as precipitates, second-phase particles, andnonmetallic inclusions; twinning deformation in the plastic zone of a crack; slip deformation; and phasetransformation. The mechanisms of possible AE sources are illustrated in Figure 10.

Figure 10. Schematic AE sources during corrosion, stress-corrosioncracking (SCC), and corrosion-fatigue processes. 55

AE in Fatigue and Fracture of Composites

Because of more complex damage mechanisms, such as reinforcement (fibers or particulates) failures,delamination, matrix cracking, and debonding of the fiber and matrix, composite materials provide a greater variety of AE sources in fatigue and fracture experiments. AE signals resulting from different damagemechanisms often vary significantly. Furthermore, most of the composites consist of at least one hard or

brittle phase, which causes stronger AE signals. Because of the great interest in composite materials fromindustries and academic institutions, there are more publications on AE in composites than other materials.

Hamstad 19 reviewed the areas in which AE has been used for composite studies. Pertinent investigationsincluded time-dependent composite properties, correlations of AE with stress levels, applications tomatrix-cure studies, relationships of AE-detected damage to other measures of damage, the effects of matrixmaterial, interface studies, AE and dimensional stability, AE applied to orientation studies, and environmentaleffects.


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Figure 11. Investigation of the sourcelocation of AE signals by the arrivaltimes of several AE sensors in acylindrical bar of Haynes HR-120 TM .24

SOURCE-FU CTIO A D WAVEFORM A ALYSIS

The reason that traditional AE only uses some of the features of AE signals is because of the limitations of sensors as well as data-capturing and analysis capabilities. In recent years, due to the improvement of transducer technology, wide-band, high-sensitivity sensors have been developed to capture the wholewaveform. The rapid advancement of computer technology has made quick data acquisition and analysis of AE waveforms feasible. It is now possible to characterize the nature of AE sources from the waveform

captured by the new AE systems.

Research has been conducted on the techniques used to characterizethe source of AE by examining the AE signals and using wave

propagation for source-function and waveform analyses. The location-filtering technique discriminates the AE from noise signals by using thespeed of wave propagation. Figure 11 shows an example of a location-filtering method that employs four AE sensors to identify the sources of the signals in a smooth cylindrical-bar fatigue sample of a nickel-basedsuperalloy, Haynes HR-120 TM .24 The source of the AE signal islocated in the gauge-length section only when the two inner sensors

pick up the signal earlier than the two outer sensors, as exhibited inFigure 11. If one of the outer sensors picks up a signal first, the signalhas likely originated from the hydraulic system, load train, or other noise sources.

Ideally, it is possible to solve inverse wave-propagation problems to identify the source of the AE signalsdetected using one or more sensors by analyzing the whole waveforms. But it is extremely complex anddifficult to solve the inverse problem without any information of the source. Alternately, many researchershave been analytically studying the responses of some known AE sources, such as the elastic wave emittedfrom a finite mode I through-crack, 33 from which the characteristics of AE sources can be identified.

AE Signals of Cracking in 3-D Solids

To conduct the source-function and waveform analyses, a fundamental knowledge of wave propagation isessential. The wave propagation in a 3-D elastic body is actually a dynamic problem of elasticity. Without the

body force, the governing equation of elasticity can be expressed in terms of the displacement vector field, u ,as

(1)

where 2= 2/ x2+ 2/ y2+ 2/ z2, (x, y, z) is the coordinate system, is the density of the material, and and are Lame's constants. According to Helmholtz's theorem, 18,40 the vector field, u , can be written as thesummation of a gradient of a scalar field, , and a curl of a zero-divergence vector field, H

(2)

Substituting Equation 2 into Equation 1, the wave equations can be obtained as

(3)


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Figure 12. Waveforms for finite

cracks of normal tear (1) in =15 and (2) = 60, where U andT are the nondimensionalamplitude and time, respectively,and is the angle from thecracking surface. 33

where c 1 and c 2 are the velocities of the primary and secondary waves, respectively.

With proper formulation of initial and boundary conditions, the AE response of a bulk material can be solvedwith the above equations. The problem regarding the initiation of mode I, II, or III through-cracks has beenformulated. 33 For example, the boundary and initiation conditions of a mode I through-crack are

(4)

where H(t) is the Heaviside step function, t is the time, l is the half length of afinite through-crack, 0 is the material-breaking strength, and ij are the stresscomponents.

The waveforms of a finite through-crack in two directions calculated usingEquations 24 are exhibited in Figure 12. The directional dependence of theAE amplitude for mode I, II, or III is presented in Figure 13.

Wave Propagation in Rods and Plates

Wave propagation in structural components, such as rods and plates, is morecomplex than in large bulk materials. Because of the additional boundaryconditions, the AE signals detected by the sensors are actually superimposed

by multiple reflections of original signals. So far, only simple modes can beroughly distinguished and discussed in the AE research. Figure 14 shows anexample of an AE signal of a plate. The signal is actually a combination of the extensional and flexural modesand their reflections.

DISCUSSIO

The most important contribution of AE to industry is that AE can provide early warnings of severe, suddenfailures. One mode of such failures can involve fatigue damage. The most dangerous characteristic of fatiguefailures is that they usually happen without warning or with very insignificant warnings. AE can detect theaccumulation of microdamage inside components, especially under service conditions.

a b c

Figure 13. The relative amplitudes of primary (P) and secondary (S) waves atdifferent directions/angles with respect to a finite crack of (a) normal tear (Mode I), (b) transverse shear (Mode II), and (c) longitudinal shear (ModeIII). 33


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Figure 14. An AE signal of a plate, which isactually a combination of extensional and

flexural modes and their reflections.39

Although AE has been used in materials-related studies for about four decades, there are still many problems.The most important difficulty is associated with the reliability of AE results. Many researchers simply use AEequipment to collect a large amount of data and use the results to explain the material failures qualitatively,without paying too much attention to the sensor calibration and attachment. Sometimes, no distinctions aremade between noises and real AE signals. Therefore, it is very difficult to compare results from different

papers. This also causes confusion in investigating the reliability of various AE systems.

Applying AE in various environments, especially high-temperature

conditions, is also a challenge. The operational temperature rangesof sensors, couplants, wave guides, and other connections need to

be considered. Most couplants, such as resin, petroleum grease,and water, can be used only between room temperature and100C. High vacuum stop-cock grease and Dow Corning 200 fluidcan be employed up to 200C. A 50% indium-50% gallium mixturecan be used up to 700C. For higher temperature usage, new wavecoupling techniques need to be designed.

When comparing AE results in various materials, one finds thatthere are more publications regarding composites, rocks, and

ceramics than metals. The reason for this trend is that the AEsignals are stronger and vary more significantly in composites,

rocks, and ceramics than in metals. On the other hand, because of the homogeneity and simplicity of metals,the wave propagation in metals should not be as complex as in composites and rocks, since there are lessreflections, diffractions, and scattering in metals. Therefore, metals represent a good subject for studyingsource-function and waveform analyses.

For future developments of source-function and waveform analyses, analytical formulations and closed-formsolutions are not possible for complex AE sources and boundary conditions. Numerical techniques, such asfinite-element and finite-difference methods, and large-scale computational facilities will be very helpful for forward analyses when the sources of AEs are known by observations and proper assumptions. However, for

reverse analyses to identify unknown sources, suitable theories and techniques still need to be developed.

CO CLUSIO S

The use of AE will become more prevalent because it can provide unique insights into damage processes. The problems of AEincluding noise reduction, reliability, and difficulty in solving the inverse problems of wave propagation in source-function and waveform analysesrepresent areas of future endeavor in AE scienceand technology. There is also a great need for work to develop AE theories for new applications.

ACK OWLEDGEME TS

This work is supported by the ational Science Foundation Division of Design, Manufacture, and Industrial Innovation , under Grant o. DMI-9724476, and the Combined Research-Curriculum Development Program under EEC-9527527 to the University of Tennessee, Knoxville , with Ms. Delcie R. Durham and Ms. Mary Poats as program managers, respectively. We would like to acknowledge the financial support of

Haynes International, Inc. , and the Center of Materials Processing at University of Tennessee, Knoxville .We are grateful to Mr. Doug Fielden, Mr. Greg Jones, Mr. Ted Long, and Mr. Mark Potter for their great help in setting up the electrohydraulic machines.


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ABOUT THE AUTHORS

Miinshiou Huang earned his Ph.D. in mechanical engineering at Northwestern University in 1997. He iscurrently a research associate at the University of Tennessee . Dr. Huang is a member of TMS.

Liang Jiang earned his M.S. in metallurgical engineering at the University of Science and Technology,Beijing , in 1995. He is currently a graduate research assistant at the University of Tennessee . Mr. Liang is

also a member of TMS.

Peter K. Liaw earned his Ph.D. in materials science and engineering at Northwestern University in 1980. Heis currently a professor and Ivan Racheff Chair of Excellence in the Department of Materials Science andEngineering at the University of Tennessee . Dr. Liaw is also a member of TMS.

Charlie R. Brooks earned his Ph.D. in metallurgical engineering at the University of Tennessee in 1962. Heis currently a professor at the University of Tennessee .

Rodger Seeley earned his M.S. in materials engineering at Youngstown State University in 1974. He iscurrently manager of technical services for Haynes International .

Dwaine L. Klarstrom earned his Ph.D. in metallurgical engineering at the University of WisconsinMadisonin 1970. He is currently director of research and product development at Haynes International . Dr. Klarstromis also a member of TMS.

For more information, contact M. Huang, University of Tennessee, Department of Materials Scienceand Engineering, 323 Dougherty Engineering Building, Knoxville, Tennessee 37996-2200; (423)974-0645; fax (423) 974-4115; e-mail [email protected].

Copyright held by The Minerals, Metals & Materials Society , 1998


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