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Materials Science and Engineering A 456 (2007) 85–92

Effect of processing route on the spatial distributions of constituent particlesand their role in the fracture process in AA5754 alloy sheet materials

Jidong Kang a,∗, David S. Wilkinson a, Dmitri V. Malakhov a, Herdawandi Halim a,Mukesh Jain b, J. David Embury a, Raja K. Mishra c

a Department of Materials Science and Engineering, McMaster University, Hamilton, Ontario L8S 4L7, Canadab Department of Mechanical Engineering, McMaster University, Hamilton, Ontario L8S 4L7, Canada

c General Motors Research & Development Center, Mail Code 480-106-212, 30500 Mound Road, Warren, MI 48090-9055, USA

Received 2 October 2006; received in revised form 17 November 2006; accepted 8 December 2006

Abstract

The solidification process and subsequent thermal mechanical history determines both the nature and distribution of the constituent particles. Thisstudy shows how the spatial distribution can be characterized and related to the fracture properties. It also indicates how the route of solidificationchanges the character of the particles. The main feature of this study is the development of an image analysis technique to characterize the spatialdistribution of the particles in order to compare continuous casting (CC) and direct-chill casting (DC) processing route.© 2006 Elsevier B.V. All rights reserved.

Keywords: Constituent particles; Spatial distribution; AA5754; Fracture process; Image analysis

1. Introduction

Aluminum alloys have been widely utilized in automotiveindustry in recent years. Continuous twin belt casting (CC)of thin slab is now seen as a possible route to significantlyreduce the cost of the final product compared to conventionaldirect-chill cast (DC) ingot. Previous studies indicated that CCmaterials have very similar necking strains as DC materials,but significantly lower fracture strain [1]. In the post neckingstage, shear banding occurs in both materials and is linked tothe final damage and fracture processes [2,3]. The constituentparticles are important in understanding the fracture process ofthese materials. Thus, it is important to delineate: (a) the natureof the particles; (b) their spatial distribution for both CC and DCmaterials.

It is well known that 5xxx aluminum alloys contain differentkinds of constituent particles [4]. However, the particle phasesare still not known unambiguously due the composition varia-tion and the effect of processing history [5]. The complex phaseassemblies present in such alloys can be identified using a varietyof techniques, such as transmission electron microscopy (TEM),

∗ Corresponding author. Tel.: +1 905 525 9140x24485; fax: +1 905 528 9295.E-mail address: kangjd@mcmaster.ca (J. Kang).

energy dispersive X-ray spectroscopy (EDS) and electron micro-probe analysis (EMPA). An alternative method is to directlyextract particles from aluminum alloy sheets and then assesstheir composition using X-ray diffraction [6–8].

Quantitative characterization of inclusions or second phaseparticles (hereafter referred to simply as particles) is crucial toquantifying their effect on mechanical properties [9]. Numerousimage analysis techniques have been used to obtain quantitativeinformation on particle size, volume fraction and spatial distri-bution [10–15]. More recently, various microstructural analysistechniques have been developed to quantify spatial distributionof microstructures, for example, the two-point correlation func-tion [16,17], pair correlation function [18] and the multi-scaleanalysis of area fraction (MSAAF) [19].

For metallic alloys in sheet form, stringers are often seenaligned in the rolling plane which affects the local mechani-cal behavior including fracture processes. To date, no imageanalysis technique has been developed to automatically distin-guish stringers from the isolated particles and obtain quantitativeinformation on which particles in a distribution belong to thestringers.

In the present study, the nature of the constituent particles andtheir spatial distribution are assessed and related to the fractureprocess of the materials. X-ray diffraction of extracted particlesand EDS of polished samples are utilized to explore the nature

0921-5093/$ – see front matter © 2006 Elsevier B.V. All rights reserved.doi:10.1016/j.msea.2006.12.052

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of the particles. Then, an image analysis technique is developedto identify stringers within a particle field. An example is pre-sented to show the significant difference of stringer distributionin continuous cast (CC) and direct-chill cast (DC) aluminumsheet AA5754 with similar chemical composition. The role ofthe spatial distribution of particles in fracture process is thenconsidered.

2. Experimental

The sheet materials used in the present study were twovariants of AA5754, produced by twin belt casting (CC) anddirect-chill casting (DC) each with weight percent Fe of 0.21.Each material was rolled to 1 mm thickness and annealed. Thechemical composition of both materials is given in Table 1.

The phenolic dissolution method was first developed by Satoand Izumi [7,8] and then modified by Gupta et al. [6]. In thepresent study, a sample of about 3 g is dissolved in 80 mL ofboiling phenol for about 60 min. Then 100 mL of a mixture of

Table 1Chemical composition of CC and DC AA5754 sheet materials (in wt.%)

Mg Mn Si Fe Cr Cu Ni V Zn Al

CC 3.50 0.21 0.11 0.21 – – – – – BalanceDC 3.11 0.25 0.06 0.21 0.04 0.01 0.01 0.01 0.02 Balance

40% benzyl alcohol and 60% toluene is added to the flask, fol-lowing centrifuging of contents. The supernatant is decantedand a fresh mixture of benzyl alcohol and toluene is added. Thewash-centrifuging cycle is repeated several times. For the lastcentrifuging, a mixture of 90% toluene and 10% benzyl alcoholis used.

Metallographic samples of the long transverse section (i.e.that containing the rolling and through-thickness directions)of each alloy were prepared using an automatic grind-ing/polishing system with a final polishing with colloidal silica.All microstructures were left unetched.

Fig. 1. Results of stringers showing in CC and DC AA5754 sheet materials. The images on the left side of (a) CC 5754 and (b) DC 5754, are images of particlesafter equalization and removing particles under the resolving limit, the right side are images of stringers superposed upon the tessellation of particles.

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Digital images of the prepared samples were acquired usinga Kodak Megaplus XHF digital camera linked to a NikonMetaphot light microscope with a 40× objective lens. The greenchannel of color images was selected and converted to grey scaleintensities. Adobe PhotoShop was used to process all images fora consistent brightness, contrast and sharpness. Digital imageswere exported to the image analysis software, Northern Eclipse[20], for further processing.

The amount of overdigitization of image is given by,

l = r

d= 0.61λgreen/NA

0.238= 0.327

0.238= 1.37 pixel (1)

where r = 0.61λgreen/NA is the smallest resolvable length ofimage [21], NA the numerical aperture and d is the calibrationof length pixel.

Therefore, a length of less than 2 pixels is considered insignif-icant in the present study. We therefore performed an erosionfollowed by a dilation of 1 pixel for the original images of bothCC and DC materials after equalization, thus eliminating allobjects with one dimension less than 2 pixels (about 0.5 �m).The resulting images are shown on the left side of Fig. 1.

Uniaxial tension tests were performed and specimens takenfrom ruptured tensile samples were collected for both CC andDC materials. The decoration and etching technique describedby Lloyd et al. [22] was used to reveal the localized shear bands.The specimens were mechanically polished and then aged at90 ◦C for about 2 weeks prior to etching in 10% phosphoricacid.

3. The character of constituent particles

In order to explore the nature of the particles, the phenolicdissolution method has been utilized to directly extract particles.The results for weight yield (Fig. 2) show that the CC materialhas a higher weight yield than DC material, although by a muchsmaller fraction than the difference in particle volume fractionobtained from the metallographic analysis that we will deal within the next section. X-ray diffraction was performed to quantifythe chemical composition of the extracted particles. The results

Fig. 2. Weight yield of particles (total weight of extracted particles per weightof alloy dissolved) for particles extracted using the phenolic dissolution method.

Table 2Typical chemical composition of Fe-rich and Si-rich particles in DC and CCsheets

Particle composition DC sheets (Fe-rich) CC sheets (Si-rich)

Mg (at%) 2.81 4.2Al (at%) 83.44 94.74Mn (at%) 4.07 –Fe (at%) 9.68 –Si (at%) – 2.06

show that the highest peak corresponds to Mg2Si in CC materialwhile it corresponds to Fe-rich particles in DC material (Fig. 3).

In order to obtain more quantitative information on the rela-tionship between particle distribution and particle chemistry,EDS analysis was conducted on well-polished sample surfacesin the rolling plane for both CC and DC materials. A typicalresult is shown in Fig. 4 and Table 2. This reveals that a largenumber of the stringers are associated with Mg2Si particles whileFe-rich particles dominate the more random particle populationin DC material.

Fig. 3. X-ray diffraction results of particle powders from: (a) CC AA5754 and (b) DC AA5754 materials.

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Fig. 4. EDS results for the chemical compostion of particle in: (a) CC AA5754and (b) DC AA5754 materials. Boxed numbers represent particles containingFe while the rest represent particles containing Si but without Fe.

4. Spatial distribution of particles

For metallic alloys in sheet form, stringers are often seenaligned in the rolling plane which affects the local mechanicalbehavior including fracture processes. However, the definitionof a stringer has always been problematic in the literature. Forexample, in ASTM standards [23,24], the definition of stringeris mostly intended for application to steel and specified as “anindividual inclusion that is highly elongated in the deforma-tion direction, or three or more inclusions separated by lessthan 40 �m (0.0016 in.) and aligned in the same plane parallelto the deformation direction” [24]. However, this definition isapparently not applicable to aluminum alloys after rolling as theparticle size and spacing is much smaller than 40 �m [4]. Thisis because the particles formed during casting are very hard andbrittle, as well as having irregular shapes. They are therefore notdeformed during rolling but are broken up and distributed alongthe rolling direction [25].

In order to quantitatively identify a stringer and its spatialdistribution in aluminum alloy sheet, it is necessary to havea precise definition suitable for implementation within imageanalysis. Similar to the ASTM standard, we propose to definea stringer in aluminum alloy sheets as “an individual particlethat has become highly elongated in the deformation directionwith a minimum length of the mean nearest-neighbor distancebetween particles, or two or more particles separated by lessthan the mean nearest-neighbor distance between particles andaligned in a plane parallel to the deformation direction”.

The nearest-neighbor distance can be measured by manyautomatic image analyzers. For example, by determining thex and y coordinates of all particle centroids in the dispersion, wecan easily calculate the distances between all pairs of particlesand then determine the smallest one, i.e. the nearest-neighbordistance [13,14] for each particle. The mean and its standarddeviation of these observed nearest-neighbor distances can bethen determined statistically.

An image analysis technique has been developed to differ-entiate stringers from isolated particles. The technique uses aone-directional open and close operator based on the mean ofnearest-neighbor distances of particles to identify which parti-cles are associated with stringers. Details of the technique aregiven in the Appendix A.

The procedure stated in the Appendix A for extractingstringers from isolated particles was applied to the digital imagesof both CC and DC materials. The mean of the nearest-neighbordistances of particles and its standard deviation was calculatedto be 3.60 ± 2.35 �m for CC material and 4.12 ± 2.29 �m forDC. The distances were converted back into pixels using Eq. (1)showing that 1 �m equals 4.2 pixels.

In order to illustrate the location of stringers, images withstringer lines (e.g. Fig. A.1(c)) were superposed on the tessella-tion of particles and are shown on the right side of Fig. 1. Thetessellation of particles was made by a skeletonization of theparticles [20]. From Fig. 1, it is noted that most particles alignedin stringers are related to those tessellation cells that have highaspect ratios and are aligned normal to the rolling direction. Itis also clearly seen that the CC alloy has a higher stringer den-sity than the DC alloy. This is further confirmed by a numericalanalysis of the results giving the volume fraction of stringers inthese two materials (Fig. 5). It is seen that the stringers containabout 50% (by volume) of the particles in CC AA5754 whereasthey contain only about 23% of the particles in DC AA5754.Moreover most of the features identified as stringers in the DCmaterial are single, large particles while in CC material moststringers consist of numerous aligned particles with moderatesize. We also note that the overall volume fraction of particles isfound to be larger in the CC alloy than the DC alloy. By compar-ing with the results in Fig. 3, it is seen from the JCPDS databasethat Mg2Si is much lighter (about 2.0 g/cm3 in density) than Fe-rich particles (greater than 3.6 g/cm3 in density), which explainsthe higher volume fraction of particles in the CC material thanin the DC material for a given weight yield.

Once we have separated the stringers from isolated parti-cles, we can easily determine the stringer spacing using thedilating counts technique [4,13] for images containing just the

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Fig. 5. Volume fraction of stringers and total particles of CC and DC AA5754with similar chemical composition.

stringers (e.g. Fig. A.1(d)). In the present study, we simply treatthe stringer spacing as the equivalent diameter of each tessellatedcell of the stringer lines. The stringer spacing distribution datashow an approximately lognormal distribution in both CC andDC materials. However, the mean stringer spacing is 12.3 �min CC while it is 20.7 �m in DC.

5. Discussion

There is a pronounced difference in the nature of particlesseen in DC and CC alloys. A possible explanation can be soughtwithin the concept of the driving forces for the onset of precipita-tion [26]. The liquid phase is supercooled below the equilibriumliquidus temperature, but solid phases are prohibited to formfrom it. Instead of calculating temperature, one can calculatedriving forces of different phases. Any phase having the max-imal driving force is declared the phase that is most likely toprecipitate. Since this computation can be repeated for any tem-perature below liquidus, the influence of supercooling on thenature and sequence of phases expected in an as-cast structurecan be studied. In reality, however, it is known that the FCC phasestarts to form at very low supercoolings (2–4 ◦C) regardlessof cooling rate [27]. Consequently, an analysis of solidifica-tion from the driving forces viewpoint should be conducted inthe following manner. The FCC phase is deemed as the solesolid phase which may form. Redistribution of alloying addi-tions between the growing FCC phase and the remaining meltis described by Scheil-Gulliver formalism. Since solubilities ofsolutes in solid Al are rather low, they are accumulated in theliquid phase, which means that a concentration of, for instance,iron in the unsolidified melt maybe dramatically higher thanits overall concentration in the system. Now the driving forcesfor the onset of precipitation of various intermetallic phasesfrom this remaining supercooled liquid enriched with alloyingelements can be calculated. Results of these computations areshown in Fig. 6. It is clearly seen that deep supercooling (whichis likely to be attained in continuous casting) favors the precip-itation of Mg2Si. It should be emphasized, however, that these

Fig. 6. Driving forces for the CC AA5754.

figures have purely illustrative nature because the thermody-namic properties of some phases are dubious and surface energyhas not been taken into account. However, the conclusion “thegreater the supercooling, the more likely will be the formation ofmagnesium silicide” conforms to experimental findings in thisresearch.

In order to clarify the effect of stringer length in the definition,we have varied the critical spacing used as the depth parameterin the analysis from half to twice the mean of nearest-neighbordistance and repeated the stringer identification procedure. Theresults (Fig. 7) show that the analysis is insensitive to this lengthparameter except in the case of the CC alloy with a depth of7.2 �m. This suggests that the particles in CC stringers arerather closely spaced. Nevertheless, these results confirm thatthe mean nearest-neighbor distance is a suitable parameter in

Fig. 7. Effect of stringer length in the definition on the volume fraction ofstringers.

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Fig. 8. Micrographs showing the interaction of shear banding and damage in the tensile fracture of CC and DC cast materials. The horizontal direction is rollingdirection while the vertical direction is the thickness direction. (a) CC AA5754; (b) DC AA5754.

Fig. A.1. An illustration to show the methodology to separate stringers from isolated particles based on the mean of nearest-neighbor distance of particles. (a)Original image; (b) inverse image; (c) close/open horizontally; (d) dilate vertically; (e) copy (a) in red plane and (d) in green plane, then add (a) and (d). Dash frameis to remove the edge effect; (f) digitizing (8-bit grey) of (e). This is the ‘stringers’; (g) (a–f). This is the ‘isolated particles’. (For interpretation of the references tocolor in this figure legend, the reader is referred to the web version of the article.)

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defining stringer structure, at least for the AA5754 aluminumalloy used in the present study.

As no damage was seen just underneath the fracture surfaces,it was thought that damage may be a consequence of the strainlocalization leading directly to fracture [3]. Therefore, it is rea-sonable to link the spatial distribution of particles to the fractureprocess of both CC and DC materials. The interaction of shearbanding and damage is seen differently in the tensile fractureof CC and DC materials (Fig. 8). The strain to produce dam-age at stringers is lower than that for isolated particles. Thus,when shear bands form in CC material, they produce damagewhich accelerates to final fracture (Fig. 8(a)). For DC mate-rial, the initial shear bands do not cause damage at isolatedparticles, hence necking occurs by continuous shear band for-mation and the strain required for damage is larger as seen inFig. 8(b).

6. Concluding remarks

The nature of the particles and their spatial distribution hasbeen investigated in relation to the fracture process in AA5754.

The X-ray diffraction results of particle powders extractedusing a phenolic dissolution method indicate that the CC mate-rial is dominated by Mg2Si particles while Fe-rich particlesdominate in DC materials. EDS results further reveal that most ofthe particles associated with stringers in CC material are Mg2Siwhile the large isolated particles in the DC material consist ofFe-rich particles.

An image analysis technique has been successfully devel-oped to differentiate particle stringers from isolated particles.The technique has been successfully applied to both continuous(twin belt) cast and direct-chill cast AA5754 sheets with simi-lar chemical composition showing a significant difference in therelative stringer density in the two materials. This leads to thedifferences in fracture processes of these two materials.

Acknowledgments

JK is grateful to Mr. Nigel Banner at Empix Imaging, Inc.,Mississauga, Ontario, Canada, for many valuable discussions onimage analysis using Northern Eclipse software.

The authors wish to thank Ms. Angela Blanchard, NovelisGlobal Technology Centre, Kingston, Ontario, Canada, in car-rying out metallographic work under the auspices of the Centrefor Automotive Materials and Manufacturing. The authors arealso grateful to valuable discussions with staff at General MotorsR&D Center, Warren, MI, USA. The financial support of Gen-eral Motors of Canada and Natural Science and EngineeringResearch Council of Canada, are gratefully acknowledged.

Appendix A. An image analysis technique todifferentiate stringers from isolated particles

Several important operations used in image analyses in thepresent study are briefly explained below (more details are to befound in the Northern Eclipse software manual [20]).

• Erode: This operator removes the outermost layer of pixels ofall objects in the scene. If the object is below a critical size,it is eliminated.

• Dilate: This operator adds a new layer of pixels to the outmostlayer of all objects in the scene. It may cause objects to join.

• Open: This operator performs a series of erosions followedby an equal number of dilations as specified by the depthparameter (in pixel numbers).

• Close: This operator performs a series of dilations followedby an equal number of erosions as specified by the depthparameter (in pixel numbers).

• Difference: This operator subtracts the larger pixel value fromthe smaller pixel value (positive results only are allowed).

• Copy plane: This function is used to separate or combineimages plane by plane. Under plane, one can have severaloptions: red plane means the red component of the RGB imagedata. Green plane means the green component of the RGBimage data.

• Skeletonize: This operator erodes an object until it is 1 pixelacross.

A model particle distribution (see Fig. A.1) is used todemonstrate the procedure of the proposed stringer definitiontechnique. This involves the following steps:

(1) Starting with original image (Fig. A.1(a)) identify and countthe volume fraction of all particles in a field, denoted asVF(1). In practice, a threshold corresponding to the opticalresolution is set; thus, only particles larger than the opticalresolution are counted.

(2) Invert the original image and save as Fig. A.1(b). This givesa determined edge effect for the following operations.

(3) Perform a “close” operation along rolling direction (thehorizontal direction in Fig. A.1) using the mean of nearest-neighbor distance between particles as the depth parameterand then perform an “open” operation also along rollingdirection using the same depth parameter. This produces animage that contains all of the particle stringers but no iso-lated particles (see Fig. A.1(c)). This is because during theclose operation on the inverse image all white regions closerin the rolling direction than the near neighbor spacing arejoined and become a single large particle.

(4) Note partial part along the vertical direction may not beincluded in the above operation. A dilation operation inthe vertical direction is necessary to compensate for this(Fig. A.1(d)).

(5) In order to separate stringers from particles, we copyFig. A.1(a) into a red plane and Fig. A.1(d) into a greenplane, then perform a difference operation (Fig. A.1(e)).After this operation, the particles associated with stringersare black while the stringer lines are red. Meanwhile,particles not associated with stringers are green and thebackground is yellow. In order to remove the edge effect, amask is then added that eliminates particles that are withinone mean interparticle distance from the edge.

(6) When digitizing the image within the aforementioned mask,only those particles that are within stringers remains in the

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image, volume fraction of which is denoted as VF(2) (seeFig. A.1(f)).

(7) The same mask is applied to the original image (Fig. A.1(a)),and the resulting image is subtracted from the stringer image(Fig. A.1(f)), leaving only the isolated particles with a vol-ume fraction VF(3). Note that VF(3) usually contains someof the area between some particles due the image opera-tions: thus the present methodology gives a lower bound ofthe stringer volume fraction.

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