Wear and Tribological Characteristics of Novel Metal Matrix Composites

Year : 2026 | Volume : 14 | Special Issue 02 | Page : 1326 1346
    By

    M. Bala Theja,

  • P. Ratna Raju,

  • M. Madhu Shekar,

  1. Associate Professor, Department of Mechanical Engineering, Santhiram Engineering College (Autonomous), Nandyal, Andhra Pradesh, India
  2. Assistant Professor, Department of Mechanical Engineering, J.N.T.U.A. College of Engineering, Kalikiri, Andhra Pradesh, India
  3. Associate Professor, Department of Chemistry, Santhiram Engineering College (Autonomous), Nandyal, Andhra Pradesh, India

Abstract

The development of advanced metal matrix composites (MMCs) with enhanced tribological performance has become increasingly important due to the premature failure of critical engineering components operating under severe wear conditions in automotive, aerospace, marine, defense, and power generation systems. Conventional composites such as Copper–Alumina and Aluminium–Silicon Carbide have demonstrated improved mechanical and wear characteristics; however, their widespread application is often limited by issues including particle agglomeration, non-uniform reinforcement distribution, porosity formation, and the absence of reliable predictive models for process optimization. To address these challenges, the present study proposes an integrated fabrication and data-driven optimization framework for the development of novel hybrid (Al–Cu) matrix composites reinforced with Silicon Carbide (SiC), Alumina (Al₂O₃), and High Entropy Alloy (HEA) particles.

The composites were fabricated using a synergistic combination of powder metallurgy and electroforming techniques to achieve improved microstructural homogeneity and enhanced interfacial bonding between the matrix and reinforcement phases. A comprehensive machine learning pipeline was incorporated to accelerate material design and performance prediction. XGBoost algorithms were employed for accurate wear-rate prediction, while Random Forest models were utilized for phase identification and microstructural classification. Furthermore, Taguchi design of experiments and TOPSIS-based multi-criteria decision-making techniques were implemented to optimize process parameters and reinforcement combinations.

Experimental evaluation revealed that the optimized hybrid composite achieved a hardness of 85 HV and a coefficient of friction of 0.62, exhibiting approximately 75% improvement in wear resistance and 183% enhancement in hardness compared with conventional baseline materials. The proposed methodology provides a scalable and intelligent roadmap for the rapid development of high-performance wear-resistant composites for advanced industrial applications.

Keywords: Intelligent Tribological Design, Predictive Materials Manufacturing, High Entropy Wear Resistant Coatings, AI-Optimized Hybrid Composites, Optimized Metal Matrix Optimization.

[This article belongs to Special Issue under section in Journal of Polymer & Composites (jopc)]

How to cite this article:
M. Bala Theja, P. Ratna Raju, M. Madhu Shekar. Wear and Tribological Characteristics of Novel Metal Matrix Composites. Journal of Polymer & Composites. 2026; 14(02):1326-1346.
How to cite this URL:
M. Bala Theja, P. Ratna Raju, M. Madhu Shekar. Wear and Tribological Characteristics of Novel Metal Matrix Composites. Journal of Polymer & Composites. 2026; 14(02):1326-1346. Available from: https://journals.stmjournals.com/jopc/article=2026/view=246306


References

[1] P. M. Mathebula, P. Sarmah, and K. Gupta, “Fabrication and metallurgical characterization of hybrid al matrix composites reinforced with SiC and Al2O3 using powder metallurgy,” The International Journal of Advanced Manufacturing Technology 2026, pp. 1–18, May 2026, doi: 10.1007/S00170-026-17757-8.
[2] F. Besharati, M. H. Paydar, and M. R. Nourian, “Fabrication and characterization of copper-alumina composite tubes via electroforming: Microstructure, mechanical properties, and corrosion resistance,” Journal of Materials Research and Technology, vol. 39, pp. 8591–8601, Nov. 2025, doi: 10.1016/J.JMRT.2025.11.176.
[3] V. Boobalan and T. Sathish, “A comprehensive study on boron carbide reinforcement in Aluminum-and its alloy composites,” Mater. Today Proc., vol. 69, pp. 1238–1241, Jan. 2022, doi: 10.1016/J.MATPR.2022.08.319.
[4] S. Sivaraman, N. Radhika, and M. Abubaker Khan, “Machine Learning-Driven Prediction of Wear Rate and Phase Formation in High Entropy Alloy Coatings for Enhanced Durability and Performance,” IEEE Access, vol. 13, pp. 33956–33975, 2025, doi: 10.1109/ACCESS.2025.3542507.
[5] S. Jayasathyakawin and M. Ravichandran, “Fabrication and wear behaviour of Mg-3wt.%Al-x wt. % SiC composites,” Heliyon, vol. 9, no. 2, p. e13679, Feb. 2023, doi: 10.1016/J.HELIYON.2023.E13679.
[6] P. Dhanalakshmi and R. Porkodi, “Extraction of Association Rules from Tobacco Smoke Effect on the Placenta Microarray Dataset using Gene Ontology Based Optmized Association Rule Mining,” Proceedings of the 2018 International Conference on Current Trends towards Converging Technologies, ICCTCT 2018, Nov. 2018, doi: 10.1109/icctct.2018.8550982.
[7] V. K. Lagisetti, A. P. Reddy, and P. V. Krishna, “Dry Sliding Wear Study on AA6061/SiCp Nano and AA6061/SiCp/Gr Hybrid Nanocomposites,” Silicon 2022 14:18, vol. 14, no. 18, pp. 12235–12250, May 2022, doi: 10.1007/S12633-022-01908-Z.
[8] J.-H. Lee and J.-S. Kwak, “Machine learning-aided prediction of burr removal in electropolishing-assisted magnetic abrasive finishing of micro-grooved surfaces,” The International Journal of Advanced Manufacturing Technology, Jun. 2026, doi: 10.1007/S00170-026-18317-W.
[9] A. Kumar and K. M. Moeed, “Hybrid composites materials of reinforced aluminium alloy and its tribological analysis: a review,” Discover Applied Sciences 2025 7:10, vol. 7, no. 10, pp. 1159-, Oct. 2025, doi: 10.1007/S42452-025-06918-1.
[10] B. Prakash, S. Irudayaraj, and N. Sivanantham, “Enhanced Corrosion Resistance of Hybrid LM13 Composites Reinforced With Zirconium Carbide and Flyash Using Squeeze Casting,” Materials and Corrosion, 2026, doi: 10.1002/MACO.70145.
[11] I. A. Alnaser, “Hybrid-reinforced aluminum matrix composites with enhanced mechanical and corrosion performance for marine applications,” J. Alloys Compd., vol. 1060, p. 187315, Mar. 2026, doi: 10.1016/J.JALLCOM.2026.187315.
[12] J. Kang, Y. Niu, Y. Zhou, Y. Fan, and G. Ma, “Wear Resistance Prediction of AlCoCrFeNi-X (Ti, Cu) High-Entropy Alloy Coatings Based on Machine Learning,” Metals 2023, Vol. 13, Page 939, vol. 13, no. 5, p. 939, May 2023, doi: 10.3390/MET13050939.
[13] J. Singh and A. Chauhan, “Characterization of hybrid aluminum matrix composites for advanced applications – A review,” Journal of Materials Research and Technology, vol. 5, no. 2, pp. 159–169, Apr. 2016, doi: 10.1016/J.JMRT.2015.05.004.
[14] S. Jebarose Juliyana et al., “Optimization of Wire EDM Process Parameters for Machining Hybrid Composites Using Grey Relational Analysis,” Crystals 2023, Vol. 13, Page 1549, vol. 13, no. 11, p. 1549, Oct. 2023, doi: 10.3390/CRYST13111549.
[15] S. Hamoudi, N. Bezzi, F. Bensebaa, and P. Delaporte, “Alumina–Nano-Nickel Composite Coatings on Al6061 Substrate Obtained by Electrophoretic Deposition,” Journal of Composites Science 2025, Vol. 9, Page 122, vol. 9, no. 3, p. 122, Mar. 2025, doi: 10.3390/JCS9030122.
[16] J. Salguero, J. M. Vazquez-Martinez, I. Del Sol, and M. Batista, “Application of Pin-On-Disc Techniques for the Study of Tribological Interferences in the Dry Machining of A92024-T3 (Al–Cu) Alloys,” Materials 2018, Vol. 11, Page 1236, vol. 11, no. 7, p. 1236, Jul. 2018, doi: 10.3390/MA11071236.
[17] M. A. Taha, M. F. Zawrah, and H. M. Abomostafa, “Fabrication of Al/Al2O3/ SiC/graphene hybrid nanocomposites from Al-dross by powder metallurgy: Sinterability, mechanical and electrical properties,” Ceram. Int., vol. 48, no. 14, pp. 20923–20932, Jul. 2022, doi: 10.1016/J.CERAMINT.2022.04.084.
[18] P. M. Mathebula, P. Sarmah, and K. Gupta, “Fabrication and metallurgical characterization of hybrid al matrix composites reinforced with SiC and Al2O3 using powder metallurgy,” The International Journal of Advanced Manufacturing Technology 2026, pp. 1–18, May 2026, doi: 10.1007/S00170-026-17757-8.
[19] J. Lv et al., “Fabrication and characterization of ni60a alloy coating on copper pipe by plasma cladding with induction heating,” Coatings, vol. 11, no. 9, p. 1080, Sep. 2021, doi: 10.3390/COATINGS11091080/S1.
[20] P. M. Mathebula, P. Sarmah, and K. Gupta, “Fabrication and metallurgical characterization of hybrid al matrix composites reinforced with SiC and Al2O3 using powder metallurgy,” The International Journal of Advanced Manufacturing Technology 2026, pp. 1–18, May 2026, doi: 10.1007/S00170-026-17757-8.
[21] S. Sivaraman, N. Radhika, and M. Abubaker Khan, “Machine Learning-Driven Prediction of Wear Rate and Phase Formation in High Entropy Alloy Coatings for Enhanced Durability and Performance,” IEEE Access, vol. 13, pp. 33956–33975, 2025, doi: 10.1109/ACCESS.2025.3542507.
[22] Armstrong M., Mahadevan S., Selvapalam N., Santulli C., Palanisamy S., Fragassa C. Augmenting the double pipe heat exchanger efficiency using varied molar Ag-ornamented graphene oxide (GO) nanoparticles aqueous hybrid nanofluids. Frontiers in Materials. 2023;10:1240606. doi:10.3389/fmats.2023.1240606.
[23] Mohankumar V., Kapilan S., Karthik A., Bhuvaneshwaran M., Santulli C., Kumar D.T., Palanisamy S., Fragassa C. A Hybrid Design of Experiment Approach in Analyzing the Electrical Discharge Machining Influence on Stir Cast Al7075/B4C Metal Matrix Composites. Metals. 2024;14(2):205. doi:10.3390/met14020205.
[24] Mehra R., Singh H., Palanisamy S., Mohal S., Kamboj N., Kumar S., Bakhrom A., Shojonov M., Shamuratov J., Sankar S.L., Sundararajan K., Belay M. A Taguchi-based optimization on EDM parameters for enhancing surface roughness in ductile cast iron: Artificial Neural Networks (ANN)-validated approach. International Journal of Mechanical and Materials Engineering. 2026;21:16. doi:10.1186/s40712-026-00466-1.
[25] Kirubakaran G., Kannan C.S., Anbalagan R.R., Palanisamy S., Alfarraj S.A., Alharbi S.A., Abbas M., Kalathil S., Belay M. Taguchi-based experimental optimization coupled with explainable machine learning for predictive modelling of FFF-printed CF-PA composites. The International Journal of Advanced Manufacturing Technology. 2026. doi:10.1007/s00170-026-17709-2.
[26] Tadepalli S., Palanisamy S., Tamma E., Santulli C., et al. Experimental performance evaluation of alginate-based Zn-Co-Fe/Ba nanocomposite beads for solar desalination and aniline blue dye removal toward sustainable water purification. Separation and Purification Technology. 2026;395:137222. doi:10.1016/j.seppur.2026.137222.

Special Issue Subscription Original Research
Volume 14
Special Issue 02
Received 04/06/2026
Accepted 06/06/2026
Published 08/06/2026
Publication Time 4 Days
13

Login


My IP

PlumX Metrics