Optimizing the ultrasonication effect in stir-casting process of aluminum hybrid composite using desirability function approach and artificial neural network
Restricted accessResearch articleFirst published online October, 2021
Optimizing the ultrasonication effect in stir-casting process of aluminum hybrid composite using desirability function approach and artificial neural network
The ultrasonic-assisted stir-casting technique improves the uniform dispersion of nano-reinforcements in aluminum hybrid metal matrix composites. In the present study, the process parameters of the ultrasonic-assisted stir-casting method, such as ultrasonic vibration time, and depth of ultrasonic vibration along with the speed of mechanical stirrer, are optimized on A356 hybrid composite material optimally reinforced with aluminum nitride, multiwalled carbon nanotubes, graphite particles, and aluminum metal powder using the desirability function approach. The process parameters are optimized against the response factors such as porosity, ultimate tensile strength, and wear rate of the composites. The optimum combination of input factors is identified as stirring speed (600 r/min), ultrasonic vibration time (2 min), and depth of ultrasonic vibration (40 mm) among the selected range. The corresponding output response values are found to be porosity (1.4%), ultimate tensile strength (247 MPa), and wear rate (0.0013 mm3/min). The ANOVA results have revealed that depth of ultrasonic vibration showed significant contribution among the input factors. An artificial neural network model is developed and validated for the given set of experimental data.
MahmoudaERITakahashiMShibayanagiT, et al.Wear characteristics of surface-hybrid-MMCs layer fabricated on aluminum plate by friction stir processing. Wear2010;
268: 1111–1121.
2.
AroraHSSinghHDhindawBK.Wear behaviour of a Mg alloy subjected to friction stir processing. Wear2013;
303: 65–77.
3.
PanagopoulosCNGeorgarakisKGAnagnostopoulouA.The influence of grain size on the sliding wear behaviour of zinc. Mater Lett2006;
60: 133–136.
4.
Mathan KumarNSenthil KumaranSKumaraswamidhasLA.An investigation of mechanical properties and material removal rate, tool wear rate in EDM machining process of Al 2618 alloy reinforced with Si3N4, AlN and ZrB2 composites. J Alloy Compd2015;
650: 318–327.
5.
AmbigaiRPrabhuS.Optimization of friction and wear behaviour of Al−Si3N4 nano composite and Al−Gr−Si3N4 hybrid composite under dry sliding conditions. Trans Nonferr Metal Soc2017;
27: 986−997.
6.
PrakashKSThankachanTRadhakrishnanR.Parametric optimization of dry sliding wear loss of copper−MWCNT composites. Trans Nonferr Metal Soc2017;
27: 627−637.
7.
BaradeswaranAVettivelSCPerumalAE, et al.Experimental investigation on mechanical behaviour, modeling and optimization of wear parameters of B4C and graphite reinforced aluminium hybrid composites. Mater Des2014;
63: 620–632.
8.
HariharasakthisudhanPMoshiAAMBharathiSRS, et al.Regression and Grey relational analysis on friction and wear behavior of AA6061/Al2O3/Si3N4/graphite hybrid nano composites. Mater Res Express2019;
6: 085017.
9.
RameshBNSureshaB.Optimization of tribological parameters in abrasive wear mode of carbon-epoxy hybrid composites. Mater Des2014;
59: 38–49.
10.
MirshahiFMeratianM.High temperature tensile properties of modified Mg/Mg2Si in situ composite. Mater Des2012;
33: 557–562.
11.
SwaminathanSSrinivasaRB and V. Jayaram.The production of AlN-rich matrix composites by the reactive infiltration of Al alloys in nitrogen. Acta Mater2002;
50: 3093–3104.
12.
GopalPMPrakashKSNagarajaS, et al.Effect of weight fraction and particle size of CRT glass on the tribological behaviour of Mg-CRT-BN hybrid composites. Tribol Int2017;
116: 338–350.
13.
SinghGGoyalS.Dry sliding wear behaviour of AA6082-T6/SiC/B4C hybrid metal matrix composites using response surface methodology. Proc IMechE, Part L: J Materials: Design and Applications2016, 232: 952–964.
14.
ParmeshwarPRitapureKhardeYR.SiC contents and pin temperature effect on tribological properties of Al25Zn/SiC composites. Int J Refract Met H2019;
82: 234–244.
15.
SinghJChauhanA.Investigations on dry sliding frictional and wear characteristics of SiC and red mud reinforced Al2024 matrix hybrid composites using Taguchi’s approach. Proc IMechE, Part L: J Materials: Design and Applications2018;
0: 1–16.
16.
RajuSSRaoGSSamantraC.Wear behavioral assessment of Al-CSAp-MMCs using grey-fuzzy approach.Measurement2019;
140: 254–268.
17.
MahmoudTS.Artificial neural network prediction of the wear rate of powder metallurgy Al/Al2O3 metal matrix composites. Proc IMechE, Part L: J Materials: Design and Applications2011;
226: 3–15.
18.
HayajnehMHassanAMAlrashdanA, et al.Prediction of tribological behavior of aluminum–copper based composite using artificial neural network. J Alloy Compd2009;
470: 584–588.
19.
ShirvanimoghaddamKKhayyamHAbdizadehH, et al.Effect of B4C, TiB2 and ZrSiO4 ceramic particles on mechanical properties of aluminium matrix composites: experimental investigation and predictive modeling. Ceram Int2015;
42: 6206–6220.
20.
IvanIArgatovaChaibSY. An artificial neural network supported regression model for wear rate. Tribol Int2019;
138: 211–214.
21.
RashedFSMahmoudTS.Prediction of wear behavior of A356/SiCp MMCs using neural networks. Tribol Int2009;
42: 642–648.
22.
RamnathBVElanchezhianCJaivigneshM, et al.Evaluation of mechanical properties of aluminium alloy–alumina–boron carbide metal matrix composites. Mater Des2014;
58: 332–338.
23.
SuHGaoWZhangH, et al.Optimization of stirring parameters through numerical simulation for the preparation of aluminum matrix composite by stir casting process. J Manuf Sci Eng2010;
132: 1–7.
24.
IdrisiAHMouradAHI.Conventional stir casting versus ultrasonic assisted stir casting process: mechanical and physical characteristics of AMCs. J Alloy Compd2019;
805: 502–508.
25.
SrinivasVJayarajAVenkataramanaVSN, et al.Effect of ultrasonic stir casting technique on mechanical and tribological properties of aluminium-multi-walled carbon nanotube nanocomposites. J Bio Tribo Corros2020;
6.
26.
AybarçUErtuğrulOSeydibeyoğluMO.Effect of Al2O3 particle size on mechanical properties of ultrasonic-assisted stir-casted Al A356 matrix composites. Inter Metal Cast2020;
15: 638–649.
27.
AybarcUYavuzHDispinarD.The use of stirring methods for the production of SiC-reinforced aluminum matrix composite and validation via simulation studies. Inter Metal Cast2019;
13: 190–200.
28.
CaoGKonishiHLiX.Recent developments on ultrasonic cavitation based solidification processing of bulk magnesium nanocomposites. Inter Metal Cast2008;
2: 57–65.
29.
LiuXJiaSNastacL.Ultrasonic cavitation-assisted molten metal processing of cast A356-nanocomposites. Inter Metal Cast2014;
8: 51–58.
30.
JianXXuCMeekT, et al.Effect of ultrasonic vibration on the solidification structure of A356 alloy. Trans Am F2005;
113: 131–138.
31.
LogeshKHariharasakthisudhanPMoshiAAM, et al.Mechanical properties and microstructure of A356 alloy reinforced AlN/MWCNT/graphite/Al composites fabricated by stir casting. Mater Res Express2000;
7: 015004.
HariharasakthisudhanPJoseS.Influence of metal powder premixing on mechanical behavior of dual reinforcement (Al2O3 (µm)/Si3N4 (nm)) in AA6061 matrix. J Alloy Compd2018;
731: 100–110.
34.
HariharasakthisudhanPJoseSManisekarK.Dry sliding wear behaviour of single and dual ceramic reinforcements premixed with Al powder in AA6061 matrix. J Mater Res Technol2019;
8: 275–283.
35.
ASTM 99-95a. Standard test method for wear testing with a pin-on-disc apparatus. West Conshohocken, PA: ASTM, 1995 (reapproved 2000).
36.
SadiVMalau VWildanMW, et al.Optimization of Stir casting process parameters to minimize the specific wear of Al-SiC composites by Taguchi method. Int J Eng Technol2015;
7: 17–26.
37.
BharathiSRSRavindranDMoshiAAM.Multi response optimization of CNC turning parameters on Austenitic stainless steel 303 using Taguchi based grey relational analysis. Trans Can Soc Mech Eng2020;
44: 592−601.
38.
SenthilkumarSKarthickAMadavanR, et al.Optimization of transformer oil blended with natural ester oils using Taguchi-based grey relational analysis. Fuel2021;
288: 119629.
39.
MohapatraTSahooSSPadhiBN.Analysis, prediction and multi-response optimization of heat transfer characteristics of a three fluid heat exchanger using response surface methodology and desirability function approach. Appl Therm Eng2019;
151: 536–555.
40.
MobinMMousaviSMKomakiM, et al.A hybrid desirability function approach for tuning parameters in evolutionary optimization algorithms. Measurement2018;
114: 417–427.
41.
BharathiSRSRavindranDMoshiAAM.Experimental investigation of surface roughness and chip morphology during machining of austenitic stainless steel 303 with PVD coated (TiAlN) insert. Surf Topogr-Metrol2021;
9: 025022.
