Wear performance and optimization of h-BN-modified AZ91/TiB 2 magnesium composites under dry sliding conditions

Wear performance and optimization of h-BN-modified AZ91/TiB 2 magnesium composites under dry sliding conditions Sathish Kamaraj Department of Mechanical Engineering, Vels Institute of Science Technology and Advanced Studies, Chennai, Tamil Nadu, India Baskar Sanjeevi Department of Mechanical Engineering, Vels Institute of Science Technology and Advanced Studies, Chennai, Tamil Nadu, India Hariharasakthisudhan Ponnarengan Department of Mechanical Engineering, Dr Mahalingam College of Engineering and Technology, Pollachi, Tamil Nadu, India https://orcid.org/0000-0002-9420-3196 Logesh Kamaraj Department of Mechanical Engineering, Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India Sathickbasha Katharbasha Department of Mechanical Engineering, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, Tamil Nadu, India https://orcid.org/0000-0001-8974-154X

This study investigates the dry sliding tribological behavior of AZ91 magnesium alloy reinforced with titanium diboride (TiB 2 ) and hexagonal boron nitride (h-BN) ceramic particles. The primary objective was to enhance wear resistance and reduce the coefficient of friction (COF) through a synergistic reinforcement approach. Hybrid composites were fabricated using a two-stage stir casting process integrating mechanical and ultrasonic agitation to ensure uniform dispersion of the reinforcements. Experiments were conducted using a Taguchi L16 orthogonal array, and the influence of normal load, sliding speed, h-BN content, and sliding distance on wear and COF was evaluated under ASTM G99 conditions. Signal-to-noise ratio analysis revealed that normal load and h-BN content were the most significant factors influencing wear rate and COF, respectively. Gradient Boosting Regression (GBR) models were developed to predict the responses, achieving R 2 values of 0.816 for wear rate and 0.825 for COF. The GBR models were then integrated with a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to identify Pareto-optimal conditions. The optimum solution (Normal load = 40 N, Speed = 1.0076 m/s, h-BN = 1.1731wt.%, Distance = 1 km) yielded wear rate and COF values of 14.98 mg/km and 0.2517, with <4% deviation from predictions. SEM analysis of worn surfaces confirmed mechanisms including micro grooving, tribolayer formation, and debris-controlled wear. The GBR–MOPSO hybrid framework proved effective for optimizing and understanding the complex wear-friction interplay in AZ91 hybrid composites.
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