Synergistic approach to tribological characterization of hybrid aluminum metal matrix composites with ZrB 2 and fly ash: Experimental and predictive insights
Restricted accessResearch articleFirst published online 2024
Synergistic approach to tribological characterization of hybrid aluminum metal matrix composites with ZrB 2 and fly ash: Experimental and predictive insights
This research delves into the tribological performance of hybrid aluminum metal matrix composites (HAMMCs) incorporating zirconium diboride (ZrB2) particles and fly ash as reinforcing agents. The study employs a linear reciprocating wear test to investigate the impact of dry sliding wear on these HAMMCs under ambient and elevated temperatures. Wear mechanisms are discerned through field emission scanning electron microscopy. Optimization of wear test parameters, coefficient of friction (COF), and wear rate is achieved using the genetic algorithm. Additionally, artificial neural network (ANN) and multiple linear regression analysis are employed to formulate a predictive model for wear, estimating specific wear rate and COF under various testing conditions. The ANN predictions exhibit a deviation ranging from 0% to 1.39% from the experimental values, indicating the model's effectiveness in understanding and predicting wear behavior in the study of HAMMC.
SurappaMK. Aluminium matrix composites: challenges and opportunities. Sadhana2003; 28: 319–334.
2.
KumarBKumarP. Preparation of hybrid reinforced aluminium metal matrix composite by using ZrB2: a systematic review. Mater Today Proc2022; 61: 115–120.
3.
KumarPKumarB. Preparation of hybrid reinforced aluminium (1350H19) metal matrix composite by using alumina and zinc. Mater Today Proc Epub ahead of print 2024. DOI: 10.1016/j.matpr.2023.08.227.
4.
BhoiNKSinghHPratapS. Developments in the aluminum metal matrix composites reinforced by micro/nanoparticles—a review. J Compos Mater2020; 54: 813–833.
5.
NeumanEWHilmasGEFahrenholtzWG. Strength of zirconium diboride to 2300°C. J Am Ceram Soc2013; 96: 47–50.
6.
ZhengKPolitisDJWangL, et al.A review on forming techniques for manufacturing lightweight complex-shaped aluminium panel components. Int J Light Mater Manuf2018; 1: 55–80.
7.
KumarSDRavichandranMJeevikaA, et al.Effect of ZrB2 on microstructural, mechanical and corrosion behaviour of aluminium (AA7178) alloy matrix composite prepared by the stir casting route. Ceram Int2021; 47: 12951–12962.
8.
KumarPKumarB. Effect of ZrB2 and fly ash on tribological and mechanical properties of aluminium 7075-based metal matrix composite at elevated. Int J Met Epub ahead of print 2023; 17: 2928–2947.
9.
NallusamyMSundaramSKalaiselvanK. Fabrication, characterization and analysis of improvements in mechanical properties of AA7075/ZrB2 in-situ composites. Meas J Int Meas Confed2019; 136: 356–366.
10.
David Raja SelvamJDinaharanIRaiRS, et al.Role of zirconium diboride particles on microstructure and wear behaviour of AA7075 in situ aluminium matrix composites at elevated temperature. Tribol—Mater Surfaces Interfaces2019; 13: 230–238.
11.
ArgatovIIChaiYS. An artificial neural network supported regression model for wear rate. Tribol Int2019; 138: 211–214.
12.
PalanisamyPRajendranIShanmugasundaramS. Prediction of tool wear using regression and ANN models in end-milling operation. Int J Adv Manuf Technol2008; 37: 29–41.
13.
BaigRUJavedSKhaisarM, et al.Development of an ANN model for prediction of tool wear in turning EN9 and EN24 steel alloy. Adv Mech Eng2021; 13: 1–14.
14.
NajjarISadounAAlamMN, et al.Prediction of wear rates of Al-TiO2 nanocomposites using artificial neural network modified with particle swarm optimization algorithm. Mater Today Commun2023; 35: 105743.
15.
KannaiyanMKarthikeyanGThankachi RaghuvaranJG. Prediction of specific wear rate for LM25/ZrO2 composites using Levenberg-Marquardt backpropagation algorithm. J Mater Res Technol2020; 9: 530–538.
16.
SheelwantAJadhavPMKumarS, et al.ANN-GA based parametric optimization of Al-TiB2 metal matrix composite material processing technique. Mater Today Commun2021; 27: 102444.
17.
KumarAMukhopadhyayA. Investigation of reciprocating wear characteristics of carbon steel at varying load, relative humidity, and operating temperature using ANN-NSGA methodology. Proc Inst Mech Eng Part E J Process Mech Eng Epub ahead of print 2023; 238: 723–737.
18.
FathyAMegahedAA. Prediction of abrasive wear rate of in situ Cu-Al2O3 nanocomposite using artificial neural networks. Int J Adv Manuf Technol2012; 62: 953–963.
19.
ShebaniAIwnickiS. Prediction of wheel and rail wear under different contact conditions using artificial neural networks. Wear2018; 406–407: 173–184.
20.
SardarSDeySDasD. Modelling of tribological responses of composites using integrated ANN-GA technique. J Compos Mater2021; 55: 873–896.
21.
StalinBKumarPRRavichandranM, et al.Optimization of wear parameters using Taguchi grey relational analysis and ANN-TLBO algorithm for silicon nitride filled AA6063 matrix composites. Mater Res Express2019; 6: 106590.
22.
ShrivastavaPKDubeyAK. Modelling and multi-objective optimisation of EDDG process using hybrid ANN-GA approach. Int J Abras Technol2016; 7: 226–245.
23.
AttaMMegahedMSaberD. Using ANN and OA techniques to determine the specific wear rate effectors of A356 Al-Si/Al2O3 MMC. Neural Comput Appl2022; 34: 14373–14386.
24.
BertoliniRSimonettoEPezzatoL, et al.Mechanical and corrosion resistance properties of AA7075-T6 sub-zero formed sheets. Int J Adv Manuf Technol2021; 115: 2801–2824.
25.
HalderBKTandonVTarquinA, et al.Influence of coal fly ash on mechanical properties of mortar consisting of total dissolved solids. 2013 World of Coal Ash (WOCA) Conference, Lexington, KY, 22–25 April2013. http://www.flyash.info.
26.
SharmaVKSinghRCChaudharyR. Effect of flyash particles with aluminium melt on the wear of aluminium metal matrix composites. Eng Sci Technol2017; 20: 1318–1323.
27.
KumarBKumarP. Preparation of hybrid reinforced aluminium metal matrix composite by using ZrB2: a systematic review. Mater Today Proc Epub ahead of print 2022; 61: 115–120.
28.
VigneshPRamanathanSAshokkumarM, et al.Microstructure, mechanical, and electrochemical corrosion performance of Ti/HA (hydroxyapatite) particles reinforced mg-3Zn squeeze casted composites. Int J Met Epub ahead of print 2023; 18: 1348–1360.
29.
VigneshPRamanathanSAshokkumarM. Evaluation of the wear performance of novel Titanium/hydroxyapatite-reinforced Mg–3Zn hybrid composites fabricated by squeeze casting. J Bio- Tribo-Corros2023; 9: 38.
30.
ŞenarasAE. Parameter optimization using the surface response technique in automated guided vehicles. Sustain Eng Prod Manuf Technol2019: 187–197.
31.
MohankumarADuraisamyTPackkirisamyV, et al.Response Surface methodology and mayfly optimization for predicting the properties of cold-sprayed AA2024/Al2O3 coatings on AZ31B magnesium alloy. J Mater Eng Perform Epub ahead of print 2023. DOI: 10.1007/s11665-023-08898-y.
32.
AshokkumarMThirumalaikumarasamyDSonarT, et al.Optimization of cold spray coating parameters using RSM for reducing the porosity level of AA2024/Al2O3 coating on AZ31B magnesium alloy. Int J Interact Des Manuf Epub ahead of print 2023. DOI: 10.1007/s12008-023-01597-x.
33.
AshokDBahubalendruniMVARMertensAJ, et al.A novel nature inspired 3D open lattice structure for specific energy absorption. Proc Inst Mech Eng Part E J Process Mech Eng2022; 236: 2434–2440.
34.
DaraABahubalendruniMVARJohnney MertensA, et al. Numerical and experimental investigations of novel nature inspired open lattice cellular structures for enhanced stiffness and specific energy absorption. Mater Today Commun2022; 31: 103286.
35.
PratapSDattaSSharmaV. Controlling recast layer and material removal in reverse µEDM for sustainable fabrication of microelectrode arrays. Proc Inst Mech Eng Part E J Process Mech Eng2024: 09544089241234499.
36.
HasanMSKordijaziARohatgiPK, et al.Triboinformatics approach for friction and wear prediction of Al-graphite composites using machine learning methods. J Tribol Epub ahead of print April 19, 2021; 144: 011701 (13 pages). DOI: 10.1115/1.4050525.
37.
BetkerALSzturmTMoussaviZ. Application of feedforward backpropagation neural network to center of mass estimation for use in a clinical environment. Annu Int Conf IEEE Eng Med Biol—Proc2003; 3: 2714–2717.
38.
ShaikNBPedapatiSRAliS, et al.A feed-forward back propagation neural network approach to predict the life condition of crude oil pipeline. Processes2020; 8: 661.
39.
TanZXThambiratnamDPChanTHT, et al.Damage detection in steel-concrete composite bridge using vibration characteristics and artificial neural network. Struct Infrastruct Eng2020; 16: 1247–1261.
40.
KhandakarAChowdhuryMEHKaziMK, et al.Machine learning based photovoltaics (PV) power prediction using different environmental parameters of Qatar. Energies Epub ahead of print 2019; 12: 2782. DOI: 10.3390/en12142782.
41.
SarajlićDAbdel-IlahLFojnicaA, et al.Prediction of the size of nanoparticles and microspore surface area using artificial neural network. Genet Appl2018; 1: 65.
42.
DeshpandeYVAndhareABPadolePM. Application of ANN to estimate surface roughness using cutting parameters, force, sound and vibration in turning of Inconel 718. SN Appl Sci2019; 1: 1–9.
43.
SaravananSDSenthilkumarM. Prediction of tribological behaviour of rice husk ash reinforced aluminum alloy matrix composites using artificial neural network. Russ J Non-Ferrous Met2015; 56: 97–106.
44.
Mathan KumarNSenthil KumaranSKumaraswamidhasLA. Wear behaviour of Al 2618 alloy reinforced with Si3N3, AlN and ZrB2 in situ composites at elevated temperatures. Alexandria Eng J2016; 55: 19–36.
45.
DaraAJohnney MertensARaju BahubalendruniMVA. Characterization of penetrate and interpenetrate tessellated cellular lattice structures for energy absorption. Proc Inst Mech Eng Part L J Mater Des Appl2022; 237: 906–913.
46.
SharmaAKBhandariRAherwarA, et al.A study of advancement in application opportunities of aluminum metal matrix composites. Mater Today Proc2020; 26: 2419–2424.
47.
AshokDBahubalendruniMVARMhaskarA, et al.Experimental and numerical investigation on 2.5-dimensional nature-inspired infill structures under out-plane quasi-static loading. Proc Inst Mech Eng Part E J Process Mech Eng2023: 09544089231197853.
48.
Taraghi OsgueiAKhamoushiI. Genetic algorithm-based search heuristic for turbine balancing problem. Proc Inst Mech Eng Part E J Process Mech Eng2022; 236: 2630–2638.
49.
VigneshPRamanathanSAshokkumarM, et al.Biodegradable Mg–3Zn alloy/titanium–hydroxyapatite hybrid composites: corrosion and cytotoxicity evaluation for orthopedic implant applications. Trans Indian Inst Met2024: 1–10.
50.
KumarPKumarB. Effect of T6 heat treatment on mechanical and tribological properties of fabricated AA7075/ZrB2/fly ash hybrid aluminum metal matrix composite by ultrasonic-assisted stir casting enroute. Int J Met Epub ahead of print 2023. DOI: 10.1007/s40962-023-01177-5.
51.
ÖzyürekDKalyonAYildirimM, et al.Experimental investigation and prediction of wear properties of Al/SiC metal matrix composites produced by thixomoulding method using artificial neural networks. Mater Des2014; 63: 270–277.
52.
KumarPKumarB. Numerical simulation of linear reciprocating wear mechanism of hybrid aluminum metal matrix composite using finite element method. Model Simul Mater Sci Eng2023; 31: 085013.
53.
PõdraPAnderssonS. Simulating sliding wear with finite element method. Tribol Int1999; 32: 71–81.
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