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J |P |S|T Journal of
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Journal of Particle Science and Technology 3 (2017) 145-154 Journal of Particle Science and Technology J | P | S | T http://jpst.irost.ir HIGHLIGHTS GRAPHICAL ABSTRACT ARTICLE INFO ABSTRACT IROST Optimization of SiC particle distribution during compocasting of A356-SiC p composites using D-optimal experiment design Hamed Khosravi 1, *, Reza Eslami-Farsani 2 , Mohsen Askari-Paykani 1 Department of Materials Engineering, Faculty of Engineering, University of Sistan and Baluchestan, Zahedan, Iran 2 Faculty of Materials Science and Engineering, K.N. Toosi University of Technology, Tehran, Iran 3 Department of Materials Engineering, Tarbiat Modares University, Tehran, Iran • Compocasting processing of A356- SiC p composites was studied. • Simultaneous effects of process parameters on SiC distribution were studied. • D-optimal design of experiment was used for optimization. Article history: Received 26 July 2017 Revised 6 December 2017 Accepted 8 December 2017 Keywords: A356-SiCp composite Compocasting Optimization Modeling D-optimal experiment design This paper presents an experimental design approach to the process parameter optimization for compocasting of A356-SiC p composites. Toward this end, parameters of stirring temperature, stirring time, stirring speed and SiC content were chosen and three levels of these parameters were considered. The D-optimal design of experiment (DODE) was employed for experimental design and analysis of results. In the experimental stage, different 20 µm-sized SiC particle contents (5, 10 and 15 vol %) were introduced into semisolid-state A356 aluminium alloy. Semisolid stirring was carried out at temperatures of 590, 600 and 610 °C with stirring speeds of 200, 400 and 600 rpm for 10, 20 and 30 min. The effect of these parameters on the distribution of the SiC particles within the matrix, represented by distribution factor (DF), was investigated. The smaller value of DF is indicative of the more uniform distribution of the SiC particles in the matrix. It was observed that the SiC particle content of 15 vol %, stirring temperature of 590 °C, stirring speed of 500 rpm, and stirring time of 30 min were the optimum parameter values producing the best distribution of the SiC particles in the matrix. The statistical test revealed that the main effect of the stirring temperature is the most significant factor. * Corresponding author: Tel.: +9854-31132642 ; Fax: +9854-33447092 ; E-mail address: [email protected] DOI: 10.22104/JPST.2017.2365.1092
Transcript of J |P |S|T Journal of
Journal of Particle Science and Technology 3 (2017) 145-154
Journal of
Particle Science and Technology
J | P | S | T
http://jpst.irost.ir
H I G H L I G H T S G R A P H I C A L A B S T R A C T
A R T I C L E I N F O A B S T R A C T
I R O S T
Optimization of SiC particle distribution during compocasting of A356-SiCp composites using D-optimal experiment designHamed Khosravi1,*, Reza Eslami-Farsani2, Mohsen Askari-Paykani 1 Department of Materials Engineering, Faculty of Engineering, University of Sistan and Baluchestan, Zahedan, Iran2 Faculty of Materials Science and Engineering, K.N. Toosi University of Technology, Tehran, Iran3 Department of Materials Engineering, Tarbiat Modares University, Tehran, Iran
• CompocastingprocessingofA356-SiCpcompositeswasstudied.
• Simultaneous effects of processparameters on SiC distributionwerestudied.
• D-optimal design of experimentwasusedforoptimization.
Article history:Received26July2017Revised6December2017Accepted8December2017
Keywords:A356-SiCpcompositeCompocastingOptimizationModelingD-optimalexperimentdesign
This paper presents an experimental design approach to the process parameteroptimizationforcompocastingofA356-SiCpcomposites.Towardthisend,parametersofstirringtemperature,stirringtime,stirringspeedandSiCcontentwerechosenandthreelevelsoftheseparameterswereconsidered.TheD-optimaldesignofexperiment(DODE)wasemployedforexperimentaldesignandanalysisofresults.Intheexperimentalstage,different20µm-sizedSiCparticlecontents(5,10and15vol%)wereintroducedintosemisolid-stateA356aluminiumalloy.Semisolidstirringwascarriedoutattemperaturesof590,600and610°Cwithstirringspeedsof200,400and600rpmfor10,20and30min.TheeffectoftheseparametersonthedistributionoftheSiCparticleswithinthematrix,representedbydistributionfactor(DF),wasinvestigated.ThesmallervalueofDFisindicativeofthemoreuniformdistributionoftheSiCparticlesinthematrix.ItwasobservedthattheSiCparticlecontentof15vol%,stirringtemperatureof590°C,stirring speed of 500 rpm, and stirring timeof 30minwere the optimumparametervaluesproducingthebestdistributionoftheSiCparticlesinthematrix.Thestatisticaltestrevealedthatthemaineffectofthestirringtemperatureisthemostsignificantfactor.
* Corresponding author: Tel.: +9854-31132642 ; Fax: +9854-33447092 ; E-mail address: [email protected] DOI: 10.22104/JPST.2017.2365.1092
H. Khosravi et al. / Journal of Particle Science and Technology 3 (2017) 145-154
1. Introduction
Itiswellknownthatmetalmatrixcomposites(MMCs)arecharacterizedbyhighspecificstrength,highspecificstiffness, low thermal expansion coefficient andhigh wear resistance [1,2]. In particular, aluminium-based MMCs have gained extensive applications inautomotiveandaerospaceindustriesduetotheirspecificcharacteristics. Silicon carbide (SiC) has become themain type of reinforcement used for these materials.SiC exhibits good thermal conductivity and chemicalcompatibilitywith aluminium, creating a strong bondbetween particle and matrix [2-5]. MMCs can befabricated via numerous processes mainly powdermetallurgyandcastingtechniques.Thecastingprocessisacosteffectivemethodwhilepowdermetallurgyiscostly.Amongthecastingtechniques,stircastingisthemostfrequentlyusedrouteforproductionofparticulateMMCs.However, it is associatedwith some inherentproblems arisingmainly from both the apparent non-wettability of ceramic reinforcing particles by liquidaluminiumalloysand thedensitydifferencesbetweenthe two phases [6,7]. In order to overcome some ofthesedrawbacksthatresultinnon-uniformdistributionofthereinforcementwithinthematrixalloy,extensiveinterfacialreactions,andformationofbrittlephasesattheparticle/matrix interfaceaswellasahigh levelofporosity new semi-solid processing techniques havebeenconsideredformanufacturingoftheseMMCs[8-10].Compcasting is a semi-solid processing route in
which the ceramic reinforcing particulates are addedto the semi-solidmatrix alloy viamechanical stirringandthencastinamoldforsolidification.Thistechniqueis superior because of its simplicity, flexibility andlowcost, and is considered tobe thebestmethod forpreparationoflargequantitiesofcompositesatlowcost[11,12].FromtheavailableliteraturesonMMCs,itisobvious
that the size, distribution and volume fraction of thereinforcementphaseaswellasthematrixpropertiesarethemain factors affecting the overallmechanical andphysicalproperties[6,7,10,11].Oneof themainchallengesassociatedwith thecast
MMCs is to achieve a homogeneous distribution ofreinforcementwithinthematrixalloy.InordertoachievetheoptimumpropertiesoftheMMCs,thedistributionofthereinforcingparticlesinthematrixalloyshouldbe
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uniformandtheporositylevelsneedtobeminimized.A non-homogeneous particle distribution often arisesas a resultof agglomeration, settling, and segregationof ceramic particles during the processing of thesecomposite materials. The particle distribution hasa significant effect on the mechanical propertiesof MMCs. For example, clustered particles act ascrack initiation sites and have a negative influenceon the mechanical properties of composite materials.Clusteredparticlearrangementssignificantlyreducethefailurestrainofcomposites.ToobtainahomogeneousdistributionofreinforcingparticlesinthecastparticulateMMCs, several factors such as the good wettabilityof the particleswith themolten alloy, propermixing,reinforcement size, reinforcement content, moldtemperatureandsolidificationrateshouldbeconsidered[10,11,13-16].The key processing parameters affecting the
final microstructure of the solidified slurry duringcompocasting processing are stirring time, stirringtemperatureandstirringspeed.Fromanindustrialpointofview,itisessentialtofindoutthebestcombinationofcompocastingparameterstoattainthebestmechanicalandphysicalproperties.In general, an experiment is an observation which
leads to characteristic information about a studiedobject [17]. One of the most common and classicalapproaches employed bymany experimenters is one-factor-at-a-time(OFAT), inwhichonefactor isvariedwhile all other variables or factors in the experimentare fixed. The success of this approach depends onguesswork, luck, experience and intuition.Moreover,this type of experimentation requires large resourcesto obtain a limited amount of information about theprocess[17-21].Inmanysituations,inviewofthehighcostofexperimentation,thenumberofobservationsiskepttoaminimum[22-24].Withdesignofexperiment(DOE)thisnumber iskeptas lowaspossibleandthemostinformativecombinationofthefactorsischosen[22,23]. Hence, DOE is an effective and economicalsolution.Theaimofthisso-calleddesignistooptimizeaprocessorsystembyperformingeachexperimentandto draw conclusions about the significant behavior ofthestudiedobjectfromtheresultsoftheexperiments.Inrecentyears,theuseofD-optimaldesignofexperiment(DODE) in industrial experimentation has grownrapidly,dueinpart,tothefactthatthemethodologyisnowbeingintroducedinstandardDOEtextbooks[19-
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21],andalsobecausefacilitiesforconstructingDODEhavebecomegenerallyavailable.Ontheotherhand,unlikestandardclassicaldesigns,
such as factorials and fractional factorials, DODE isusuallynotorthogonal [17,18].This typeofdesign isalways an option regardless of the type ofmodel theexperimenter wishes to fit (for example, first order,firstorderplussomeinteractions,fullquadratic,cubic,etc.)or theobjectivespecifiedfor theexperiment(forexample,screening,responsesurface,etc.)[18].DODEis straight optimization based on a chosen optimalitycriterionandthemodelthatwillbefit.TheoptimalitycriterionusedingeneratingDODEresultsinminimizingthe generalized variance of the parameter estimatesfor apre-specifiedmodel.As a result, the 'optimality'ofagivenDODEismodeldependent[17,18].Thatis,theexperimentermust specifyamodel for thedesignbeforethecomputercangeneratethespecifictreatmentcombinations.Giventhetotalnumberoftreatmentrunsforanexperimentandaspecifiedmodel,thecomputeralgorithmchoosestheoptimalsetofdesignrunsfromacandidatesetofpossibledesigntreatmentruns.Thiscandidate set of treatment runsusually consists of allpossiblecombinationsofvariousfactorlevelsthatonewishestouseintheexperiment.AlthoughsomeresearchershavealreadyutilizedDOE
tooptimizethedifferenttypesofcastingroutes[24],noefforthasyetbeenmadetoperformthisoptimizationonthecom-castingprocess.Inthepresentwork,anattempthas beenmade to develop amodel for predicting theuniformityinSiCparticlewithinthematrixasafunctionofkeyinputparametersinthecompocastingprocessingofA356-SiCpcomposites.
2. Experimental
2.1. Materials and experimental procedure
Al-A356 with a nominal chemical composition, asgiven inTable1, formed thematrixandSiCparticles(average size 20 µm) with 5, 10 and 15 % volumefractionswereusedas the reinforcementphase.A356aluminium alloy is a hypoeutecticAl-Si alloy and itsrelatively broad semisolid interval (32 °C) makes itsuitableforsemisolidprocessing.TheSEMmicrographoftheSiCpowderisshowninFigure1.SiCparticleswereartificiallyoxidizedinairat1000ºCfor120mintoallowalayerofSiO2toformonthemandimprove
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theirwettabilitywithmoltenaluminium.Thistreatmenthelpstheincorporationoftheparticleswhilereducingundesiredinterfacialreactions[11].
Table 1.Chemicalcomposition(wt%)ofA356Alloy.
Si Mg Mn Zn Cu Fe Al
6.93 0.38 0.23 0.26 0.25 0.11 Balance
Fig. 1. SEMimageofSiCparticles.
The aluminium alloy matrix composites weresynthesized by the compocasting method. Figure 2ashowsaschematicrepresentationofthecompocastingapparatususedinthisstudy.Inthefirststage,1kgofA356 aluminium alloywas put in a graphite crucibleandmeltedat750°Cbyanelectricresistancefurnace.Two calibrated thermocouples were inserted into themeltandthefurnacetomeasuretheirtemperatures.SiCparticleswerepreheatedat600°C ina stainless steelcrucible.GivendensityvaluesforAlandSiC(2.7and3.2g/cm3),thecruciblechargewasdeterminedtoobtaintheA356-SiCp composite samples with different SiCcontents.Thesemi-solidstirringprocesswascarriedoutbyagraphiteimpeller(Figure2b)[25]attemperaturesof590,600and610°Cfor10,20and30min.Threedifferentstirringspeedsof200,400and600rpmwerealsoutilized.Inthelaststage,theslurrywasheatedupto 660 °C and stirred at this temperature for another8min.Castingwasdone inacylindrical shapedsteelmold(40mminternaldiameterand30mminheight),preheatedat400°C.The prepared samples were subjected to standard
metallographic procedures and examined via an“Olympus-BX60M”lightmicroscope.
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(2)
whereb0,bi, bii,bijaretheconstant,linear,squareandinteraction regression coefficient terms, respectively,andxiandxjaretheindependentfactors(A,B,CorD).Design-Expert 7 (State-Ease, Inc., Trial version)
software was used for multiple regression analysis,analysis of variance (ANOVA), and analysis of ridgemaximum of data in the response surface regression(RSREG)procedure.ThegoodnessoffitofthemodelwasevaluatedbythecoefficientofdeterminationR2andits statistical significancewas checked by the F-test.
3. Results and discussions
Thisstudydemonstrates theeffectofstirringspeed,stirring time, stirring temperatureandSiCcontent fortheoptimizationofthecompocastingroute.Hence,theknowledgeabouttheprocessisrelativelylimited,andthe design is used to obtain 38 design points withinthewhole range of four factors for experiments.Thedesigns and the response (DF (Y)) are given inTable3. Following the experiments, the response surface isapproximated by DODE.TheresultsoftheDODEarepresentedinanANOVA
table(Table4)withaconfidenceinterval(CI)of95%(P<0.05) for themodel. Instatistics,CI isakindofintervalestimateofapopulationparameterandisusedto indicate the reliability of an estimate.The level ofconfidenceofCIwouldindicatetheprobabilitythattheconfidencerangecapturesthistruepopulationparametergivenadistributionofsamples[17-21].Byconsideringahalfnormalplotandanormalplot(notshownhere),fourmaineffectsandtheirsquaresallwithCI=95%were selectedas significant factors formodeling.Theeffectofa factor isdefinedas thechange in responseproduced by a change in the level of a factor. This
Fig. 2.(a)Schematicrepresentationofthecompocastingapparatusand(b)graphiteimpellerusedinthisstudy.
ThedistributionoftheSiCparticleswithinthematrixalloywascharacterizedbycalculatingthedistributionfactor(DF)definedbyEq.(1)[26].DF (1)
inwhichAf is themean value of the area fraction oftheSiC particlesmeasured on 100fields of a sampleand S.D. is its standard deviation. A non-uniformmicroscopicdistributionofthereinforcingphasewithinasampleisreflectedasarelativelyhighvalueofDF.
2.2. Experimental design and statistical analysis
Toexplore the effect of theoperation factorson theresponse(DF)intheregionofinvestigation,aDODEatthreelevelswasperformed.Stirringspeed(rpm,A),stirringtime(min,B),stirringtemperature(°C,C)andSiC content (vol%,D)were selected as independentfactors. The range of values and coded levels of thefactorsaregiveninTable2.Apolynomialequation(Eq.2)wasusedtopredicttheresponse(DF,Y)asa functionof independent factorsandtheirinteractions.Aninteractionisthefailureoftheonefactortoproducethesameeffectontheresponseatdifferentlevelsofanotherfactor[20].Inthiswork,therewerefour independentfactors; therefore, theresponseforthequadraticpolynomialsbecomes:
fADSDF ..
=
Table 2. Independent Factors and their Levels for DODE ofcompocasting process.
Independentfactors Unit level
-1 0 1
Stirringspeed(A) rpm 200 400 600
Stirringtime(B) min 10 20 30
Stirringtemperature(C) °C 590 600 610
SiCcontent(D) vol% 5 10 15
∑ ∑ ∑∑+++= jiiiiiiii xxxxY ββββ 20
∑ ∑ ∑∑+++= jiiiiiiii xxxxY ββββ 20
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Table 3.DODEtestsandtheresponseforcompocastingprocess.
StandardOrder RunOrder Factor1A
Factor2B
Factor3C
Factor4D
ResponseDF(byExperiment)
1 26 200 20 600 5 0.47
2 28 600 30 610 10 0.52
3 8 200 10 590 15 0.51
4 15 600 20 610 5 0.41
5 7 400 30 610 15 0.28
6 25 200 30 610 10 0.34
7 22 400 10 600 15 0.52
8 29 600 30 600 5 0.44
9 13 200 20 600 15 0.34
10 33 600 20 590 5 0.66
11 32 200 20 590 15 0.40
12 34 600 20 610 15 0.36
13 18 200 30 610 15 0.35
14 31 200 20 590 10 0.65
15 19 200 10 600 15 0.41
16 23 600 10 610 15 0.60
17 3 200 10 610 15 0.67
18 17 200 30 610 5 0.64
19 27 200 10 600 5 0.65
20 24 600 10 600 10 0.50
21 12 200 10 600 10 0.59
22 35 600 30 590 15 0.22
23 9 400 30 590 15 0.17
24 6 400 20 610 15 0.46
25 2 400 30 610 15 0.40
26 37 600 20 590 10 0.35
27 30 400 30 600 5 0.39
28 36 400 20 600 10 0.42
29 38 400 30 600 10 0.33
30 20 200 30 590 5 0.41
31 5 400 10 610 5 0.65
32 16 400 10 590 10 0.41
33 11 400 10 610 10 0.60
34 1 400 20 590 5 0.38
35 14 400 10 590 5 0.48
36 4 200 30 590 15 0.29
37 10 200 10 590 5 0.55
38 21 200 10 590 10 0.48
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(residuals) degree of freedom. To categorize theparameterorthemodelasasignificantvalue,calculatedF-valuemust bemore than its value in the statisticaltables.IfthecalculatedvalueofFisgreaterthanthatintheFtableataspecifiedprobabilitylevel,astatisticallysignificant factor or interaction is obtained [20]. ThelackoffitoftheF-valuefortheresponseshowedthatthelackoffitisnotsignificant(p>0.05)relativetothepureerror.Thismodel(Eq.(3))canbeusedtonavigatethedesignspace.The quadratic regression coefficients obtained by
employingaleastsquaresmethodtechniquetopredictquadraticpolynomialmodelsfortheDF(Y)aregivenas Eq. (3). For Y, the linear term and the quadraticterms(withoutinteractionterms)ofA,B,CandDweresignificant(P<0.05).Sum of squares (SS) of each factor quantifies its
importance in the process and as the value of theSSincreases the significance of the corresponding factorin the undergoing process also increases. As showninANOVA table (Table. 4), the effect ofC (stirringtemperature) is the strongest and then B, D, A2 andA, respectively. Ifwe consider themodel equation inactualterms,onecanfindthattheeffectofA2andCispositive(synergisticeffect).However,A,BandDhaveanegative(antagonism)effectonDF.TodecreaseDF,thepositive effect shouldbedescendingandnegativeeffectshouldbeascending.Significantfactorsinthefittedmodel(Eq.(3))were
chosen as the axes for the 3D surface plots (Figures3aand4a)andcontourplots(Figures3band4b).Inacontourplot(baseplotsinthe3Dplots),curvesofequalresponsevaluesaredrawnonaplanewhosecoordinatesrepresent the levels of the independent factors. Eachcontourrepresentsaspecificvaluefortheheightofthe
is frequently called amain effect because it refers toprimary factors of interest in the experiment [21].ANOVA results for DF show a significant model
withadequateprecisionof61.818.Adequateprecisioncomparestherangeofthepredictedvaluesatthedesignpointstotheaveragepredictionerror;ontheotherhand,adequateprecisionmeasuresthesignaltonoiseratioandaratiogreaterthan4isdesirable[18].Here,thevalueoftheratioisgreaterthan4,soitrepresentsanadequatemodel (Eq. (3)) for predicting the results within thedesignspacewithoutdoinganyfurtherexperiments.
Y=0.42_0.030A_0.037B+0.11C_0.069D+0.070A2(3)
Thequalityoffittingsoftheequationswasexpressedby the coefficient of regression "AdjustedR-squared"or in a better way by "Predicted R-squared". The"AdjustedR-squared"valuesindicatevariabilityintheobservedresponsevalueswhichcanbeexplainedbytheexperimental factorsand their interactions.Thecloser"Predicted R-Squared" and "Adjusted R-Squared"valuesare to1, thebetter thefit [27].The "PredictedR-squared"of0.9684is inreasonableagreementwiththe"AdjustedR-squared"of0.9743.ThemodelF-valueof 281.69 implies that themodel is significant (Fmodel
= 281.69>>Ftable (5,32)=2.530) and there is only a0.01%chancethata"ModelF-value"couldoccurduetonoise.F-valueisthetestforcomparingthevarianceassociatedwiththattermwiththeresidualvariance.Itisthemeansquareforatermdividedbythemeansquarefortheresidual.Thistermshouldbeaslargeaspossible[18]. Tables of F-value (a,b) for different confidenceintervalsexistinstatisticalreferences[17].Where,thefirst number in parenthesis is the parameter ormodeldegree of freedom and the second one is the error’s
Table 4.ANOVAwithCI=95%formodelandfactors.
Source Sumofsquares Degreeoffreedom Meansquare Fvalue P-valueProb>F
Model 0.60 5 0.12 281.69 <0.0001 Significant
A 0.019 1 0.019 45.71 <0.0001
B 0.14 1 0.14 327.98 <0.0001
C 0.29 1 0.29 687.10 <0.0001
D 0.12 1 0.12 292.61 <0.0001
A2 0.040 1 0.040 93.53 <0.0001
Residual 0.014 32 0.0004261
CorrectedTotal 0.61 37
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Fig. 3.a)Responsesurfaceandb)contourplotsfortheeffectofthestirringtimeandstirringspeedontheDF.
Fig. 4.a)Responsesurfaceandb)contourplotsfortheeffectofthestirringtemperatureandSiCcontentontheDF.
surfaceabovetheplanedefinedforacombinationofthelevelsofthefactors.FromFigures 3 and 4,DF decreases by decreasing
the amount of stirring temperature and increasing theamountofSiCcontentandstirringtime.Ontheotherhand,therelationshipbetweenthestirringspeedandDFwasalmostparabolic.Thistrendisingoodagreementwith the trend of factor effects. The observed valueswere reasonablyclose to thepredictedonesasshowninFigure5.
Fig. 5.Predictedvs.actualplotofDF.
Thenormalityofthedatacanbecheckedbyplottinga normal probability plot of the residuals. If the datapoints on the plot fall fairly close to a straight line,thenthedataarenormallydistributed[21].Thenormalprobability plot of the residuals for SF (not shownhere)depictedthatthedatapointswerefairlyclosetothestraightlineandthisindicatesthattheexperimentscomefromanormallydistributedpopulation.The confirmation experiments were conducted in
threedifferentconditions.TheresultsarelistedinTable5. If the average of the results of the confirmation iswithinthelimitsoftheCI,thenthesignificantfactorsaswellastheappropriatelevelsforobtainingthedesiredresultsareproperlychosen[17-21].FromTable5,theexperimental responses are in 95%CI range and thismodelcanbeusedtonavigatewithinthedesignspace.Inordertotestthevalidityoftheoptimizedconditions
given by the model, an experiment was also carriedout with parameters as suggested by the model. TheconditionsusedintheconfirmatoryexperimentaregiveninTable6.TheDFvalueattheoptimalconditionwasfoundtobe0.16(Table6),whichisconsistentwiththemodel.Therefore, the formulatedmodel is acceptablyvalid. Itshouldbenoted that thesmallervalueofDF
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isindicativeofthemoreuniformdistributionoftheSiCparticlesinthematrix[11].Figure6demonstratestheopticalmicrographsofthecompositesamplesfabricatedbydifferentcompocastingprocessparametersandSiCcontendsforconfirmationandoptimalconditiontests.Figure3showsthatDFoftheSiCparticlesdecreases
withincreasingthesemisolidstirringtime,representingamorehomogenousSiCdistributionwithinthematrix.At lower stirring time (10 min), in some zones thematrix is free fromSiCparticlesand inother regionsclustering of the SiC particles is visible. This showsthat this stirring time is insufficient for obtaining an
acceptableSiCdistributioninthematrix.Higherstirringtimeresultsinabetterdistributionoftheparticles.FromFigure4,itcanbeseenthatdecreasedstirringtemperatureresulted in amore homogeneous distribution of theseparticleswithinthematrix,asindicatedbysmallerDFvalues. This means that by increasing the semisolidstirring temperature, a less homogeneous distributionof the SiC particles is obtained in the matrix alloy.Decreasingthestirringtemperaturefrom610to590°C(atthefixedstirringspeedandstirringtimeof400rpmand20min,respectively)leadstoa45%decreaseintheDFvalue,whichisattributedtotheincreasedviscosityof the semisolid slurry.According to the equilibriumbinaryAl-Sidiagram,A356aluminiumalloysolidifiesat a broad temperature interval (32 °C) between 583to615°C.Thisalloyconsistsof45%,35%and18%solidfractionsinsemisolidslurryat590,600and610°C, respectively [28]. This shows that the viscosityof the alloy increases as the semisolid temperaturedecreases. The restricted movement of the particleswithin the slurry during semisolid stirring preventstheSiCparticlesfromsettlingasaconsequenceoftheincreased effective viscosity; consequently, a moreuniformparticledistributionisobtained.ThepresenceofasolidphaseinthesemisolidslurrycanalsohelpthebreakdownoftheSiCclustersduringstirring.The results of this study show a remarkable
improvement in the uniformity of the SiC particledistribution (as reflectedby thedecreasedDF)whenthestirringspeedof500rpmwasused(Figure3andTable6).Particleclusteringisobservableatarelatively
Table 5.Resultsofconfirmationtests.
Stirringspeed(rpm) Stirringtime(min) Stirringtemperature(°C) SiCcontent(vol.%) DF
1 Model 400 20 590 10 0.30
Confirmationtest 0.32
2 Model 200 30 600 5 0.51
Confirmationtest 0.53
3 Model 400 20 610 10 0.53
Confirmationtest 0.52
Table 6.DFatoptimalconditions.
Parameter Stirringspeed Stirringtime Stirringtemperature SiCcontent DF
Model 500rpm 30min 590°C 15vol% 0.16
Confirmationtest 0.19
Fig. 6.OpticalmicrographsoftheA356-SiCpcompositesfabricatedbythedifferentcompocastingprocessparametersandSiCcontents(a-c)confirmationtestsand(d)atoptimalcondition.
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lowstirringspeed(i.e.200rpm),andinsomeregionsthe matrix is free of SiC particles (Figure 6b). Byincreasing the stirring speed to 500 rpm, a betterdistribution of the SiC particles within the matrixalloyisobtainable.Theseresultsareinagreementwithsomerelatedstudies[10,12],andcanbeattributedtotheincreaseofshearforcesappliedbyincreasingthestirringspeed,whichcanimprovetheuniformityoftheSiCparticledistributionasaresultofalargervortexwithintheslurry.Ontheotherhand,thehigherstirringspeed(from500to600rpm)imposedaconsiderablenon-uniformityintheSiCparticledistribution,whichcanbeattributedtotheincreasedagitationseverityoftheslurry,resultinginclusteringoftheSiCparticles.TheeffectoftheSiCcontentontheuniformityofthe
particlesdistributionwithinthematrixisgiveninFigure4. From this figure, improvement in the uniformityof theSiCparticledistribution isobtainablewhentheparticlecontentincreases.Thiscanbeattributedtothe(a) restricted movement of particles within the meltduringsolidificationasaconsequenceoftheincreasedeffective viscosity of the slurry and (b) finer matrixmicrostructure as a result of increased barriers forgrowthofα-Alphase.
4. Conclusion
Compocasting processing of Al-A356-SiCp
composites was studied and modeled using theD-optimaldesignofexperiment(DODE).Theeffectsofcompocastingprocessparameters(stirringtemperature,stirringtimeandstirringspeed)aswellasSiCcontenton the uniformity in the particle distribution wereinvestigated. The conclusions drawn from the resultscanbesummarizedasfollows:1.Theoptimumvaluesofstirringtemperature,stirringtimeandstirringspeedwerefoundtobe590°C,30minand500rpm,respectively.
2. The correlation coefficient (R2) of the regressionmodel was 0.97, which confirms the excellentaccuracyofthemodel.
3. The most important factor affecting the SiCdistributionwithinthematrixalloywasfoundtobethestirringtemperature.
4. The uniformity in the SiC distribution improvedby increasing theSiCcontentandstirring timeanddecreasing the stirring temperature. A remarkableimprovement in the uniformity of the SiC particle
distributionwasachievedwhenthestirringspeedof500rpmwasused.
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