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Materials Science and Engineering A 528 (2011) 6708– 6714

Contents lists available at ScienceDirect

Materials Science and Engineering A

journa l h o me pa ge: www.elsev ier .com/ locate /msea

uantification of damage evolution in a 7075 aluminum alloy using an acousticmission technique

arcos Lugoa,∗, J.B. Jordonb, M.F. Horstemeyera,c, M.A. Tschoppa, J. Harrisd, A.M. Gokhaled

Center for Advanced Vehicular Systems (CAVS), Mississippi State University, Box 5405, Mississippi State, MS 39762-5405, USADepartment of Mechanical Engineering, The University of Alabama, Box 870276, Tuscaloosa, AL 35487-0276, USADepartment of Mechanical Engineering, Mississippi State, MS 39762, USAMaterials Science and Engineering Department, Georgia Institute of Technology, 771 Ferst Drive, N.W., Atlanta, GA 30332-0245, USA

r t i c l e i n f o

rticle history:eceived 6 October 2010eceived in revised form 28 March 2011

a b s t r a c t

The use of acoustic emission for quantifying the microstructural damage evolution under tensile loadingis studied for a 7075 aluminum alloy. First, the cracking of intermetallic particles present in the materialwas evaluated using interrupted tensile tests combined with digital image analysis of large optical image

ccepted 12 May 2011vailable online 19 May 2011

eywords:amage evolutioncoustic emission

ntermetallics

montages. Subsequent acoustic emission tests under tensile monotonic loading produced an in situ sig-nature that correlated with the quantitative stereology results obtained destructively. Acoustic emissionis a viable option for quantifying the evolution of microstructure damage in terms of particle cracking forthe 7075 alloy.

© 2011 Elsevier B.V. All rights reserved.

. Introduction

The ability to predict fracture and failure in wrought aluminumlloys is important for quantifying the life of components in theerospace and automotive industry. Continuum models that aimo predict material failure often employ the evolution of damage as

criterion for fracture. For example, the damage variable can oftenepresent the underlying response of the material microstructureo the loading conditions imposed upon the material. A number ofamage models have been developed in recent years [1–7]. How-ver, the functional form of the damage evolution in these modelss primarily based on interrupted tests, whereby the testing samples destroyed to evaluate the level of damage in the microstruc-ure. In addition to being destructive, these sorts of interruptedests are extremely time intensive. A better option for evaluatinghe evolution of microstructure and damage within a material iso use nondestructive techniques. Acoustic emission is a nonde-

tructive technique that is widely used for preventing catastrophicailure from unknown discontinuities in structural components.his method is attractive because of its ability to dynamically quan-

∗ Corresponding author. Tel.: +1 662 325 5437; fax: +1 662 325 5433.E-mail addresses: ml330@msstate.edu (M. Lugo), bjordon@eng.ua.edu

J.B. Jordon), mfhorst@cavs.msstate.edu (M.F. Horstemeyer),tschopp@cavs.msstate.edu (M.A. Tschopp), arun.gokhale@mse.gatech.edu

A.M. Gokhale).

921-5093/$ – see front matter © 2011 Elsevier B.V. All rights reserved.oi:10.1016/j.msea.2011.05.017

tify the changes of microstructure in a number of different materials[8]. Here, we use acoustic emission to quantify damage in a wroughtaluminum alloy with intermetallic particles (7075) and comparewith destructive stereological testing.

Damage in aluminum alloys is related to evolving microstruc-tural features. The failure process in ductile materials is associatedwith local failure of second phase particles, e.g., inclusions, inter-metallic particles, and precipitates [9]. For example, Yeh and Liushowed that cracking of particles in A357 aluminum alloy occursduring plastic deformation. Moreover, Balasundaram et al. [10]have suggested that damage in aluminum alloys is produced bycracking of intermetallic inclusions/particles, growth of voids atcracked particles, and void coalescence. In addition, they report thatthe Fe-rich intermetallic particles dominated the damage progres-sion in both 6061-T6 and 5086-O aluminum alloys. They observedthat the direction of cracks under tension loading was perpen-dicular to the load direction. The source of damage in the 7xxxaluminum alloy is in the form of inclusions [11,12]. Specifically,the 7075 aluminum alloy has two main types of inclusions: Fe-richintermetallic compounds and Mg2Si-based particles [13,14], bothof which have distinguishable characteristics. The Mg2Si particleshave dark equiaxed shapes and the Fe-rich particles have gray andirregular forms.

Damage evolution normally is divided into three parts: voidnucleation, growth, and coalescence [15]. Horstemeyer et al. [1]have shown that these components evolve simultaneously andhave developed a model for damage evolution that includes void

M. Lugo et al. / Materials Science and Eng

nen

tdadptt

�

wsm

mu

Fig. 1. Location of the metallographic samples.

ucleation, growth, and coalescence implemented within the finitelement method. Using this model, they have shown that voiducleation is highly influenced by the stress state of the material.

Damage evolution can be quantified by measuring the evolu-ion of microstructural features with metrics such as the numberensity, number fraction, and volume fraction [1,2]. Horstemeyernd Gokhale [2] proposed a model capable of capturing damage inuctile materials in terms of void nucleation. The void nucleationarameters in this model were a length scale parameter, d, and par-icle volume fraction, f. They developed the following equation forhe number of voids per area:

(t) = Ccoeff exp

(ε(t)d1/2

KIC f 1/3

{a

[4

27− J2

3

J32

]+ b

J3

J3/22

+ c

∣∣∣∣∣ I1√J2

∣∣∣∣∣})

(1)

here Ccoeff, a, b and c are material constants determined from ten-ion, compression and torsion tests. Further details of this damage

odel can be found in [2].Prior studies have demonstrated that particle cracking in alu-

inum alloys is the main source of acoustic emission (AE) producednder plastic deformation [16,17]. The use of AE to study the

Fig. 2. Initial intensity imag

ineering A 528 (2011) 6708– 6714 6709

mechanical properties of aluminum alloys is not new; early stud-ies were conducted by James and Carpenter [16] and Carpenterand Higgins [17]. They observed that AE in a 7075-T651 alloy wasdifferent than AE in a 7075-T6 aluminum alloy. Their studies sug-gested that the majority of AE produced in the 7075-T651 wasdue to the presence of small intermetallic particles in the matrix.Additionally, McBride et al. [18] conducted a study of 7075 alu-minum alloy to investigate the influence of intermetallic inclusionsize on fatigue crack growth and AE measurements. They studiedAl-7075 with heat treatments 0 and T6 with the following char-acteristics: free of inclusions, and containing inclusions rangingfrom 1 to 10 �m. They did not detect any acoustic activity with theinclusion-free specimens, while significant acoustic activity wasobserved in the specimens with inclusions. They concluded that thesource of acoustic emission of the 7075-T6 with inclusions was thebrittle fracture of intermetallic inclusions. Moreover, Cousland andScala [19] investigated the sources of acoustic emission under ten-sion and compression loading conditions in two aluminum alloys:Al-2024 and Al-2124. The lack of AE in compression led them toconclude that AE in these alloys was produced by inclusion fracture.In addition, they suggested that the maximum peak observed in theacoustic emission curves, occurring just before yield, is associatedwith slip of dislocations.

The objective of this research is to evaluate the use of acous-tic emission for quantifying the microstructural damage evolutionfor the 7075-T651 aluminum alloy. Here, a stereological analysis ofthe material microstructure was conducted on interrupted mono-tonic tensile loading tests to determine the evolution of damagevia microstructural statistics, e.g., particle area fraction, numberdensity, etc. Companion experiments measured the acoustic emis-sion activity under the same monotonic loading conditions. Then,

the microstructure and acoustic emission predictions were cor-related. A better understanding of the cracking process in theseintermetallic particles shed light on the progression of damagein the 7075-T651 alloy and will ultimately improve continuum

e and binary images.

6710 M. Lugo et al. / Materials Science and Engineering A 528 (2011) 6708– 6714

drtm

2

aacct3

2

bwtsdstss

2

tctcl

caTofisAt

sion produced under monotonic tensile loading. AEwin software

Fig. 3. Stress–strain curve for 7075-T651 aluminum alloy.

amage models that aim to capture ductile material failure. Theesults show that acoustic emission is a viable nondestructiveechnique for quantifying damage development in 7075-T651 alu-

inum alloys.

. Materials and test procedures

The material used in this research is a commercially avail-ble 7075-T651 wrought aluminum alloy, where T651 signifies

solution heat treatment and then artificially aged. The mainomponents of this alloy are Al, Zn, Mg, Cu, Fe, and Si. Theomposition of the material has been given elsewhere [7]. Allhe specimens used were taken from plates with dimensions of62 mm × 50 mm × 23 mm.

.1. Tensile tests

The rectangular tensile specimens chosen for this study wereased on the ASTM E8 standard with a 50 mm gage length, 12.7 mmidth, and 6.35 mm thickness. The specimens were machined in

he rolling direction. Interrupted tensile tests were conducted attrain levels of 2, 4, 6, 8, 10, and 12 percent. The tests were con-ucted using an INSTRON electromechanical load frame undertrain control at a strain rate of 0.001/s. An extensometer was usedo measure and control strain. At each interrupted strain level, thepecimens were removed and sectioned for metallography analy-is. Several samples were tested to failure to verify repeatability.

.2. Metallography

Fig. 1 shows the (L-S) planes that were cut from the gage sec-ion for metallographic analysis. The metallographic specimensontained a vertical plane parallel to the applied loading direc-ion (rolling direction). The (L-S) planes provide a clear picture ofracked particles, while the planes perpendicular to the appliedoading do not show these features very readily [13].

The preparation process of the samples consisted of four steps:utting, mounting, grinding and polishing. Aluminum alloys are rel-tively soft and require a specific process for grinding and polishing.he polishing procedure is detailed as follows. First, a progressionf various carbide papers at low pressure was applied to removene scratches. Then, lapping cloths that employ suspended abra-

ive particles were used. The samples were then imaged in anxiovert Zeiss optical microscope with magnification of 800× in

he unetched condition.

Fig. 4. Types of particles and cracks in a 7075-T651 aluminum alloy at 8% strainlevel.

2.3. Digital image analysis

Manual methods for identification are tedious, susceptible toerror, and time consuming. Hence, automating microstructuralfeature characterization via digital image analysis is essential forquantifying the evolution of microstructure. Digital image pro-cessing was used to measure the evolution of features within themicrostructure images as a function of deformation. Automatedimage processing techniques were combined with user input tohelp identify and measure the microstructure features in an accu-rate and repeatable manner. A large montage image was used ateach strain level to accurately measure the small volume fractionof intermetallic particles and the non-uniform particle distribution.

The images used to measure microstructural statistics at eachstrain level consisted of a montage image composed of 25 adjacentfields arranged in a 5 × 5 grid. The microscope contained an auto-matic stage and standard image analysis software in order to mergethe individual images and to create the montage images (area of∼0.540 mm2).

A program was developed to perform the digital image analysisusing the Image Processing Toolkit in MATLAB. The code per-formed the following main tasks: (a) segmented all particles basedon a threshold parameter, (b) distinguished the Fe-rich particlesfrom the Mg2Si particles, (c) separated the cracked Fe-rich parti-cles from the non-cracked Fe-rich particles, and (d) computed themicrostructure statistics. The initial segmentation and identifica-tion was fully automated, relying upon standard image processingmethods for selecting threshold parameters between two distri-butions of intensity values. However, this technique was thenaugmented with manual feedback to ensure that all particles wereidentified and classified correctly. The end result of this code wasthree binary images: one image containing the Fe-rich particles,one image containing the cracked Fe-rich particles, and one imagecontaining the Mg2Si particles.

Fig. 2 shows the initial intensity image as well as the three binaryimages for Fe-rich particles, cracked Fe-rich particles and Mg2Siparticles.

2.4. Acoustic emission

An AE system was employed to measure the acoustic emis-

was used to detect and record the acoustic emission signals. Sen-sors were attached to the upper and lower gage section near theshoulder. These sensors were used to detect any acoustic signal

M. Lugo et al. / Materials Science and Engineering A 528 (2011) 6708– 6714 6711

rain: (

rtatsg

3

3

t

3

mtatditdhFtopi

sccHaAi

Fig. 5. Progression of damage in percentage of st

egistered in the specimen. The AE system provides frequency fil-ering in both the preamplifier and on the PCI-2 board. The filter is

bandpass type where selection of the frequency is performed onhe software system. This frequency was set to 100–200 kHz. Sen-ors have a 60 Db sensitivity, and preamplifiers were set at a 60 dBain.

. Results

.1. Tension tests

Fig. 3 shows the stress–strain curve of a representative sampleested to failure. This figure also shows the strain levels evaluated.

.2. Quantitative study of the microstructure

Fig. 4 shows an example of an 800× magnification for a typicalicrostructure section at 8% strain level. This image shows clearly

he two types of particles present in the 7075-T651 aluminumlloy. The gray particles are associated with Fe-rich particles, andhe dark particles are identified as Mg2Si particles, as it has beenocumented in Refs. [7,13,14]. Cracks are clearly visible in this

mage, and most cracks are perpendicular to the loading direc-ion, i.e., the rolling direction. Cracks in the Mg2Si particles areifficult to distinguish. Therefore, cracks in these particles haveistorically been neglected by researchers for two reasons [2,3,5].irst, the number density of these particles is small comparedo the Fe-rich particles. Second, these particles are too dark inptical images to be distinguished from cracks. Hence, in theresent investigation, only cracking in Fe-rich particles was stud-

ed.Fig. 5 shows typical 800× images of the microstructure at each

train level. The first strain level shows only one cracked parti-le in the Fe-rich particles. However, at successive strain levels,racks were clearly observed in several of the Fe-rich particles.

igher strain levels exhibited a higher number of cracks as wells a larger crack opening displacement than at lower strain levels.t 12% strain, practically all the Fe-rich particles cracked as shown

n Fig. 5.

A) 2%, (B) 4%, (C) 6%, (D) 8%, (E) 10%, and (F) 12%.

3.3. Quantitative characterization

In this study, a two-dimensional approach is used, as it hasbeen demonstrated that these features can be obtained fromtwo-dimensional analysis [20]. The distribution of intermetallicparticles is non-uniform and the particle volume fraction is lowin this alloy. Hence, a reasonable sample size must be taken. Themicroscope used in this study is equipped with an automatic stagecapable of creating montages of many adjacent fields. However,to avoid issues related with focusing, montages composed of 25adjacent fields arranged in a 5 × 5 grid were defined. In addition,several samples at each strain level were observed to increase reli-ability of our analysis. To evaluate the accuracy of the results, at-Student distribution was used for the %RA (the relative accu-racy) analysis, similar to [21]. Using the equation developed byDeHoff [21] and setting %RA to 10%, it was determined that fivesamples were required for a statistically accurate analysis. Thus,the metallographic analysis included five samples of each strainlevel.

The evolution of microstructure in Al-7075-T651 can be mea-sured both in terms of the number of cracked particles and thenumber fraction of cracked particles (Fig. 6a and b). In this study,the number density (number per unit area) is defined as the num-ber of cracked Fe-rich particles divided by the total area of the fieldobserved. The number fraction has been defined as the ratio of num-ber density of cracked Fe-rich particles to the number density ofthe total Fe-rich particles [10]. Fig. 6a and b shows the evolution ofnumber density and number fraction, respectively, and their cor-responding standard deviations as a function of strain. Fig. 6a andb shows a strain dependence of both number density and num-ber fraction of cracked particles. As strain increases, the numberof cracked particles increases. Additionally, both variables seem tobehave exponentially. Last, the normalized area fraction, where thisfeature is defined as the ratio of area fraction of cracked Fe-rich par-ticles to the area fraction of the total Fe-rich particles, is shown inFig. 6c.

3.4. Acoustic emission

AE measurements were taken during tensile tests that were con-ducted at a constant strain rate of 0.001/s. The AE measurements

6712 M. Lugo et al. / Materials Science and Engineering A 528 (2011) 6708– 6714

Fig. 6. (a) Number density, (b) number fraction, and (c) normalized area fraction fort

wdrutTa

4

4

u

The nucleation model predicts an exponential increase in the num-ber density with increasing strain. For tensile loading, the number

he 7075-T651 aluminum alloy.

ere filtered using a lower threshold of 40 dB, and signals with zerouration and (or) zero rise times were removed. AE counts wereecorded and are shown in Fig. 7a. Several descriptors have beensed to interpret AE signals: peak amplitude, rise time, durationime, summation of counts, and amplitude distribution [8,22,23].wo AE measurements are used in this study: summation of countsnd amplitude distribution (Fig. 7).

. Discussion

.1. Damage evolution

Fig. 8 shows the number of cracked Fe-rich particles per vol-me versus the applied tensile strain. Damage in this type of alloy

Fig. 7. (a) Summation of acoustic emission counts, and (b) amplitude distributionof acoustic emission events for 7075 aluminum alloy.

is due mainly to the progression of cracking in Fe-rich particles aspreviously described. Hence, the number of cracked particles is agood measure of the evolution of damage. Also plotted in Fig. 8 isthe Horstemeyer and Gokhale void nucleation model (Eq. (1)) withthe following constants: a = 0, b = 163500, c = 19990, Ccoeff = 26.5,f = 0.09, d = 2.6 �m, and KIC = 29 MPa m1/2. These constants weredetermined from the interrupted tension tests using a best fitregression analysis method. A good correlation between the pre-dicted nucleation model and experimental results was observed.

Fig. 8. Number density against strain in a 7075 aluminum alloy.

M. Lugo et al. / Materials Science and Engineering A 528 (2011) 6708– 6714 6713

F

dt

4

tpbb

tw(psmat8m

tcdctatti

4

tmpsFuAoaer

could have been accounted for. Perhaps more important, though,is that hairline fractures of particles may not be entirely resolv-able in optical images. To test this hypothesis, scanning electronmicroscopy (SEM) images of a section tested to 4% strain were ana-

ig. 9. Number of acoustic emission counts in a 7075 and 1100 aluminum alloys.

ensity estimated with a 2D technique is approximately equal tohe number using a 3D methodology [6].

.2. Acoustic emission analysis

The behavior of the summation of counts has been related withhe stress–strain behavior [8,17], which is associated with damagerogression under monotonic loading. Fig. 7a shows that the num-er of counts (summation of counts) increases rapidly for strainsetween 0 and 2%.

Two types of AE signals are typically observed in practice: con-inuous and burst-type. The first type (continuous) is associatedith dislocation and grain boundary sliding, while the second type

burst-type) is associated with crack advancement, twinning andhase transformations [22]. Among the features of burst type AEignals are a short rise time and a high amplitude. Some statisticaleasurements used to distinguish one type from the other are the

mplitude distribution, counts to peak, and rise time. Fig. 7b showshat the AE amplitude is low during most of the loading, where5% of the AE activity ranged between 40 dB and 48 dB level. Theinimum and maximum peaks were 40 and 97 dB, respectively.Since AE amplitudes between 40 and 48 dB are predominant in

his strained sample, it is hypothesized that AE signals are due toracking of particles. To test this hypothesis, an AE test was con-ucted with a high purity (99%) Al-1100 aluminum alloy, whichontained no intermetallic particles (i.e., no cracking). Fig. 9 showshe number of AE counts for both the 7075 and 1100 aluminumlloys. The lack of AE response in the Al-1100 alloy confirms thathe majority of AE in the Al-7075 alloy is due to cracking of par-icles, since the 1100 alloy is almost pure aluminum with nontermetallics.

.3. Microstructure properties and acoustic emission

One of the objectives of this study was to understandhe relationship between the AE activity and the underlying

icrostructural features. Therefore, the summation of AE countser volume detected during the tension test and the number den-ity of cracked Fe-rich particles were plotted together, as shown inig. 10. The AE counts per volume were determined using the vol-me of the gage section. A one-to-one relationship between theE and the metallography results as function of strain was not

bserved. Fig. 10 clearly shows that the AE counts saturated atpproximately 5% strain, while the metallography results increasedxponentially and did not saturate. However, the damage metricseached similar values near fracture.

Fig. 10. Acoustic emission counts and number density against strain.

In an attempt to correlate the AE and the metallography results,an equation of the following form was proposed:

� = N

k+(

N

l

)1/m

(2)

where � is the number density, N is the summation of AE counts pervolume, is the summation of AE counts per volume, and k, l, and mare constants. A best fit regression analysis method was appliedto evaluate the constants. The following values were obtained:k = 16.50, l = 1360, and m = 0.0155.

The relationship between the AE and the metallographic resultsalong with the transfer model (Eq. (2)) are shown in Fig. 11. Excel-lent agreement between the transfer model and the experimentswas observed through the whole range of strain levels under analy-sis. As such, Fig. 11 reveals the strong relationship between damageand AE activity. This result suggests that the number of AE countsis related directly with the cracking of inclusions and is directlyrelated to the number per volume activity. The number of AE countsat the beginning of the deformation process is low, but increasesrapidly as the strain increased. The number of AE counts producedat higher levels is greater than lower levels.

There may be several reasons for the difference between themetallography and the AE results. First, the metallographic analy-sis does not include the Mg2Si particles. Since the metallographicanalysis neglected the darker Mg2Si particles, the number of crackparticles per volume may be influenced by these particles if they

Fig. 11. Number of Fe-rich cracked particles as a function of acoustic emissionnumber of counts.

6714 M. Lugo et al. / Materials Science and Eng

l(ptdmittp

cscstsshCaNasclA

5

maFcmatc(batsi

[

[

[

[

[

[

[

[

[

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[[

Fig. 12. Mg2Si and Fe-rich particles captured by a SEM.

yzed. Two important findings were revealed in these SEM images:1) Mg2Si particles showed cracks, and (2) a majority of the Mg2Siarticles were cracked. Fig. 12 shows an example SEM image withhe two types of particles. The Mg2Si particles were small and veryark, and cracks were difficult to capture in optical images at theagnification used to analyze the Fe-rich particles. Moreover, it is

mportant to note that many of the Fe-rich particles and Mg2Si par-icles exhibited multiple cracks. Multiple cracking of particles addso an early increase in AE activity and saturation, as shown in theresent study.

Another reason for the differences between the AE and opti-al imaging results maybe because the AE measurements areensitive to the 3D number density and number fraction of theracked particles. On the other hand, the metallographic mea-urements are performed on 2D metallographic sections throughhe 3D microstructure. As the initial microstructural geometry istatistically the same in all the specimens, the 2D number den-ity and number fraction of the cracked particles is expected toave strong correlation with the corresponding 3D parameters.onsequently, a strong correlation between the acoustic emissionnd metallographic measurements is experimentally observed.onetheless, the 2D and 3D particle cracking damage parametersre not expected to be numerically equal. This is the likely rea-on for the observed nonlinear correlation (rather than a linearorrelation) between the damage parameters measured metal-ographically and the corresponding parameters measured usingE.

. Conclusions

A study to investigate the damage evolution in a 7075 alu-inum alloy has been performed that incorporates the use of

coustic emissions in order to quantify the evolution of damage.irst, the particle cracking behavior of the 7075 aluminum alloy washaracterized under monotonic tensile loading. Using stereologicalethods and digital image analysis, a quantitative microstructural

nalysis was conducted. Regarding the microstructural propertieshe following observations can be made: (a) under monotonic loadracking of Fe-rich particles, here expressed by the number density1/mm3) increased in an exponential pattern and (b) the num-er fraction of cracked particles followed a nonlinear pattern that

pproaches a limit near 35%. Also, the acoustic emission activity ofhe 7075 aluminum alloy under tensile loading was measured. Theummation of acoustic emission counts revealed that much activ-ty takes place in the initial stage of the deformation process, with

[

ineering A 528 (2011) 6708– 6714

less activity observed at the later stages. This last observation sug-gests that perhaps most particles are cracked in the early stagesof the damage process. In addition, it was seen that the acousticemission activity is related directly with damage progress in thisalloy. A strong correlation was found to exist between the acous-tic emission counts and the number of cracked particles. Finally,a transfer function was proposed to relate the acoustic emissionmeasurements to the number density of cracked particles.

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