STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Polymer & Composites Original Research Special issue

Wear and Tribological Characteristics of Novel Metal Matrix Composites

  1. M. Bala Theja Department of Mechanical Engineering, Santhiram Engineering College (Autonomous), Nandyal
  2. P. Ratna Raju Department of Mechanical Engineering, J.N.T.U.A. College of Engineering, Kalikiri
  3. M. Madhu Shekar Department of Chemistry, Santhiram Engineering College (Autonomous), Nandyal

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

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