Restricted accessResearch articleFirst published online 2025-12
Optimization and modelling of tribological properties in self-lubricating AA2024/nano-graphene/nano-WC hybrid composites using RSM-CCD,TOPSIS,Pareto analysis and ANFIS
The present study investigates the minimization of wear rate and frictional coefficient in AA2024 aluminium matrix composites by varying key parameters: tungsten carbide (WC) reinforcement percentage, sliding speed, load and sliding distance. The objective is to enhance the wear performance characteristics of AA-2024-based composites reinforced with nano-WC and nano-graphene particles. The composites were prepared by incorporating a constant 0.15% nano-graphene and WC at weight percentages of 1%, 2% and 3% by the stir-casting process. The Response Surface Methodology (RSM)-based central composite design technique is used to plan 30 experiments to study the dry sliding behaviour. The ANOVA technique is used to study the influence of input parameters on output responses. It reveals that the reinforcement percentage of WC is the most influential parameter. The wear rate is minimized when the reinforcement percentage increases from 1 to 2 wt.%, but it increases again when the percentage exceeds 2 wt.%. The Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) and Pareto optimality analysis determined a 2 wt.% WC reinforcement provides optimal wear resistance under sliding circumstances of a 10 N load, 1000 m sliding distance and 328 rpm speed. Further, an integrated adaptive neuro-fuzzy inference system and an exhaustive search approach are used to effectively model the dry sliding wear behaviour of AA-2024-based composites with minimal training and validation errors of 0.0397 and 0.0396. Scanning electron microscopy was employed to analyze the samples of worn surfaces, elucidating the wear mechanisms, which included delamination and abrasion deformation modes.
ParkBLeeDJoI, et al. Automated quantification of reinforcement dispersion in B4C/Al metal matrix composites. Compos Part B Eng2020; 181: 107584.
2.
KrishnanPKChristyJVArunachalamR, et al. Production of aluminum alloy-based metal matrix composites using scrap aluminum alloy and waste materials: influence on microstructure and mechanical properties. J Alloys Compd2019; 784: 1047–1061.
3.
MuDZhangZXieY, et al. The microstructures and mechanical properties of a 5vol%SiC/AA2024 nanocomposite fabricated by powder metallurgy. Mater Charact2021; 175: 111090.
4.
GaoMKangHChenZ, et al. Enhanced strength–ductility synergy in a boron carbide reinforced aluminum matrix composite at 77 K. J Alloys Compd2020; 818: 153310.
5.
SalurEAcarerMŞavkliyildizİ.Improving mechanical properties of nano-sized TiC particle reinforced AA7075 Al alloy composites produced by ball milling and hot pressing. Mater Today Commun2021; 27: 102202.
6.
Sharath KumarPSachitTMohanN, et al. Dry sliding wear behaviour of Al–5Si–3Cu–0.5Mn alloy and its WC reinforced composites at elevated temperatures. Mater Today Proc2020; 44: 566–572.
7.
Gxowa-PenxaZDaswaPModibaR, et al. Development and characterization of Al–Al3Ni–Sn metal matrix composite. Mater Chem Phys2021; 259: 124027.
8.
SelvakumarSDinaharanIPalanivelR, et al. Development of stainless steel particulate reinforced AA6082 aluminum matrix composites with enhanced ductility using friction stir processing. Mater Sci Eng A2017; 685: 317–326.
9.
AzimiHNourouziSJamaatiR.Effects of Ti particles and T6 heat treatment on the microstructure and mechanical properties of A356 alloy fabricated by compocasting. Mater Sci Eng A2021; 818: 141443.
10.
BrightRJSelvakumarGSumathiM.Prediction of sliding wear behavior of China clay particle reinforced AA6082 matrix composites using response surface methodology and analysis of the worn surfaces. Surf Topogr Metrol Prop2022; 10(1): 015037.
11.
KamarajLHariharasakthisudhanPArul Marcel MoshiA.Optimizing the ultrasonication effect in stir-casting process of aluminum hybrid composite using desirability function approach and artificial neural network. Proc Inst Mech Eng L J Mater Des Appl2021; 235(9): 146442072110257.
12.
JabinthJSelvakumarN.Enhancing the mechanical, wear behaviour of copper matrix composite with 2V-Gr as reinforcement. Proc Inst Mech Eng J J Eng Tribol2020; 235(7): 135065012096057.
13.
Karthik PandiyanGPrabaharanT. Mechanical and tribological characterization of stir cast AA6061 T6 – SiC composite. Silicon2021; 13: 4575–4582.
14.
PoriaSSahooPSutradharG.Tribological characterization of stir-cast aluminium–TiB2 metal matrix composites. Silicon2016; 8: 591–599.
15.
SinghGGoyalSMirandaG, et al. Parametric study of the dry sliding wear behaviour of AA6082-T6/SiC and AA6082-T6/B4C composites using RSM. J Mech Sci Technol2018; 32: 579–592.
16.
BrightRJSelvakumarGSumathiM, et al. Development, mechanical characterization and analysis of dry sliding wear behavior of AA6082 Metakoalin metal matrix composites. Mater Res Express2019; 6(12): 125117.
17.
KumarCRBalajiAPrabhuP, et al. Mechanical and tribological characterization of hybrid Al6061/B4C/Gr composites by stir casting technique. Surf Rev Lett2022; 29(11): 2250147.
18.
GobikannanSGopalakannanSSivarajP.Multiresponse optimisation and investigation of dry sliding wear behaviour of Al7075 surface hybrid nanocomposite using response surface methodology. Surf Rev Lett2022; 29(10): 2250140.
19.
SinghSSinghGKumarL, et al. Microstructural analysis and tribological behavior of aluminum alloy reinforced with hybrid alumina/nanographite particles. Proc Inst Mech Eng J J Eng Tribol2014; 229(5): e06399.
20.
GotagunakiSMudakappanavarVSSureshR, et al. Studies on the mechanical properties and wear behavior of an AZ91D magnesium metal matrix composite utilizing the stir casting method. Metallogr Microstruct Anal2023; 12: 986–998.
21.
KnyazhevENikolaevaAChumaevskiiA, et al. Iron-added aluminum matrix composites prepared by friction stir processing: structure, mechanical and tribological properties. Metallogr Microstruct Anal2024; 13: 376–399.
22.
KothiyalPMuralidharaBHulugappaB, et al. Effect of Al2O3 reinforcement on behavioural characteristics of aluminium metal matrix nanocomposites. Metallogr Microstruct Anal2024; 13: 462–477.
23.
ParikhVKBadgujarADGhetiyaND.Investigation of microstructural and wear properties of stir cast and friction stir processed AA 2014-based metal matrix composites. Adv Mater Process Technol2022; 9(4): 1920–1940.
24.
Karthik PandiyanGJamesDJDPrabaharanT, et al. Synthesis, characterisations and applications of boron nitride based hybrid metal matrix composites. Adv Mater Process Technol2024; 2024: 1–24.
25.
PrinceRMRArulkirubakaranDDebnathT, et al. Mechanical and wear behaviour of TiB2–B4C reinforced Al7075 alloy hybrid composites for aerospace applications. Adv Mater Process Technol2022; 8(4): 4209–4228.
26.
YadavAPPadapAK.Optimization of dry sliding wear parameters through RSM approach of fine-grained 7075 Al alloy developed by MAC. Proc Inst Mech Eng C J Mech Eng Sci2023; 237(21): e08958.
27.
HussainMZKhanSSarmahP.Effect of dry sliding wear parameters on the specific wear rate of α-MnO2-epoxy nanocomposites. Proc Inst Mech Eng C J Mech Eng Sci2022; 237(6): e032405.
28.
TalluriBKNarasimha RaoR.Multi-response optimisation of wear parameters of B4C reinforced Al-Fe-Si composites: using Taguchi-grey relational analysis. Proc Inst Mech Eng E J Process Mech Eng2024.
29.
Ravi KumarKSreebalajiVS. Desirability based multiobjective optimisation of abrasive wear and frictional behaviour of aluminium (Al/3.25Cu/8.5Si)/fly ash composites. Tribol Mater Surf Interfaces2015; 9(3): 1751584X15Y.000.
30.
PackkirisamyVThirugnanasambandamABhowmikA, et al. Synergistic approach to wear rate forecasting in AZ31/5% Yttria stabilized zirconia reinforced composite through response surface methodology–genetic algorithm integration. Proc Inst Mech Eng J J Eng Tribol2024; 239(2): e04546.
31.
DeyDBhowmikABiswasA.A grey-fuzzy based multi-response optimisation study on the friction and wear characteristics of titanium diboride reinforced aluminium matrix composite. Proc Inst Mech Eng B J Eng Manuf2023; 237(14): 095440542211479.
32.
ChenJLoSDoQH.Forecasting monthly electricity demands: an application of neural networks trained by heuristic algorithms. Information2017; 8(1): 31.
33.
MashalyAFAlazbaAA.Membership function comparative investigation on productivity forecasting of solar still using adaptive neuro-fuzzy inference system approach. Environ Prog Sustain Energy2017; 37(1): 249–259.
34.
DewanMWHuggettDJWarren LiaoT, et al. Prediction of tensile strength of friction stir weld joints with adaptive neuro-fuzzy inference system (ANFIS) and neural network. Mater Des2016; 92: 288–299.
35.
SubburajAJoseph DecruzAMMAChandra MoorthyVA, et al. Mechanical characterization and micro-structural analysis on AA2024 hybrid composites reinforced with WC and graphene nanoparticles. Trans Indian Inst Met2022; 75: 1721–1730.
36.
Veera AjayCManisekarKAndrewsA. Investigating and predicting tribological characteristics of AZ31 alloy composites reinforced with nano-Al2O3 and micro-Sn particles: a comparative analysis using CCD–RSM and ANN models. Phys Scr2024; 99(8): 45–62.
37.
Vignesh KumarDArulselvanSArul Marcel MoshiA, et al. Mechanical characterization and frictional wear behavior analysis on nano tungsten carbide and molybdenum disulfide particles reinforced aluminium 7075 composites. Proc Inst Mech Eng E J Process Mech Eng2023; 238(3): 095440892211507.
38.
Veera AjayCManisekarKThoufiq MohammedK. In vitro degradation and dry sliding wear behaviour of AZ31-Sn/Al2O3 nano metal matrix composites in simulated body fluid for biomedical application. Proc Inst Mech Eng L J Mater Des Appl2023; 238(1): 112–228.
