This study introduces a machine learning (ML) framework to predict the Coefficient of Friction (CoF) and Specific Wear Rate (SWR) in aluminum-based composites reinforced with SiC and MoS2. Utilizing a robust dataset of 948 records, eight ML models were developed and optimized via GridSearchCV. Hyperparameter tuning was transformative, with the optimized Ridge model achieving exceptional test R² values of 0.96 for CoF and 0.90 for SWR. Feature importance analysis identified Material, Sliding Load, and MoS2% as critical factors. The models infer a key synergistic mechanism: SiC enhances wear resistance, while MoS2 reduces friction. This work validates a finely-tuned ML approach as a highly accurate and efficient computational alternative to traditional experimental methods for tribological performance modeling and MMC design.
SureshS. (ed.) Fundamentals of metal-matrix composites. Amsterdam, Netherlands: Elsevier, 2013.
2.
SurappaMK. Dry sliding wear of fly ash particle reinforced A356 al composites. Wear2008; 265: 349–360.
3.
RohatgiPK. Metal matrix composites. Def Sci J1993; 43: 323–349.
4.
MishraAKKumarVPathakBN. Al 6061 hybrid metal matrix composites with SiC & Al2O3 using stir casting: A review. Mater Today Proc2023; 82: 101–108.
5.
KumarMMishraAK. Mechanical behavior of al 6063/MoS2/Al2O3 hybrid metal matrix composites. International Journal of Scientific and Research Publications2014; 4(12): 1–4.
6.
MishraAKSrivastavaRK. Wear behaviour of al-6061/SiC metal matrix composites. Journal of The Institution of Engineers (India): Series C2017; 98: 97–103.
7.
MishraAKSheokandRSrivastavaRK. Tribological behaviour of al-6061/SiC metal matrix composite by taguchi’s techniques. International Journal of Scientific and Research Publications2012; 2: 1–8.
8.
AravindanMKBalamuruganK. Tribological and corrosion behaviour of al 6063/SiC metal matrix composites. International Journal of Advanced Engineering Technology2016; 2: 994–999.
9.
PradhanSBarmanTKSahooP, et al. Tribological behavior of al-SiC metal matrix composite in acidic medium. Int J Eng Technol2016; 8: 24–31.
10.
KronbergerGKommendaMLughoferE, et al. Using robust generalized fuzzy modeling and enhanced symbolic regression to model tribological systems. Appl Soft Comput2018; 69: 610–624.
11.
SivaramanSRadhikaN. Predictive analytics of wear performance in high entropy alloy coatings through machine learning. Phys Scr2024; 99: 076014.
12.
AmmisettiDKKruthiventiSS. Experimental investigation of the influence of various wear parameters on the tribological characteristics of AZ91 hybrid composites and their machine learning modeling. J Tribol2024; 146. doi: https://doi.org/10.1115/1.4064397
13.
KumarPKumarB. Synergistic approach to tribological characterization of hybrid aluminum metal matrix composites with ZrB2 and fly ash: experimental and predictive insights. Proc Inst Mech Eng Part E J Process Mech Eng2024: 09544089241255931. doi: https://doi.org/10.1177/0954408924125593
14.
NingHChenFSuY, et al.Modeling and prediction of tribological properties of copper/aluminum-graphite self-lubricating composites using machine learning algorithms. Friction2024; 12: 1322–1340.
15.
HasanMSKordijaziARohatgiPK, et al.Triboinformatic modeling of dry friction and wear of aluminum base alloys using machine learning algorithms. Tribol Int2021; 161: 107065.
16.
HasanMSKordijaziARohatgiPK, et al.Machine learning models of the transition from solid to liquid lubricated friction and wear in aluminum-graphite composites. Tribol Int2022; 165: 107326.
17.
RathodNUSeramVKarthikK. Prediction of coefficient of friction and sliding wear rates of cast Al6061-Si3N4 composites using ANN Approach. Int J Sci Eng Res2015; 3: 1–4.
18.
KumarVGautamGSinghMK, et al.Prediction of frictional behaviour through regression equations: a statistical modelling approach validated with machine learning. Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology2025; 239: 827–838.
19.
SinghJSinghSVasudevH, et al.Artificial neural network model for wear characteristic analysis of WC-10Co4Cr and stellite 6 thermal spray coatings. Tribol Int2024; 199: 109924.
20.
BhowmikABhattacharjeeBAdewale AkinwandeA, et al.Optimization of tribological performance of TiB2-reinforced Al6063 composite using grey-fuzzy tool. Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology2024; 238: 1066–1078.
21.
RanjanANagdeveLKumarH, et al.Tribological behaviour of stir casted hybrid-Al metal matrix composites using taguchi technique. Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology2023; 237: 894–910.
22.
PrajapatiDKKatiyarJKPrakashC. Machine learning approach for the prediction of mixed lubrication parameters for different surface topographies of non-conformal rough contacts. Industrial Lubrication and Tribology2023; 75: 1022–1030.
23.
MohammedASDodlaSKatiyarJK, et al.Prediction of friction coefficient of su-8 and its composite coatings using machine learning techniques. Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology2023; 237: 943–953.
24.
MishraAKKumarVSrivastavaRK. Optimization of tribological performance of Al-6061T6-15% SiCp-15% Al2O3 hybrid metal matrix composites using taguchi method & grey relational analysis. Journal of Minerals and Materials Characterization and Engineering2014; 2: 351–361.
25.
KarthikeyanANallusamyS. Experimental analysis on sliding wear behaviour of aluminium-6063 with SiC particulate composites. International Journal of Engineering Research in Africa2017; 31: 36–43.
GauravA. Data-driven machine learning regression methods to predict the residual strength in FRP composites subjected to fatigue. Polymer Composites, 2025.
28.
YanHTanJChenH, et al.Machine learning-based prediction of tribological properties of epoxy composite coating. Polymers (Basel)2025; 17: 282.
29.
YinBBLiewKM. Machine learning and materials informatics approaches for evaluating the interfacial properties of fiber-reinforced composites. Compos Struct2021; 273: 114328.
30.
RajputRRautASettiSG. Prediction of mechanical properties of aluminium metal matrix hybrid composites synthesized using stir casting process by machine learning. Mater Today Proc2022; 59: 1735–1742.
