Pareto-based harris hawks optimization of hardness and tribological performance in ZnO–hBN reinforced aluminum matrix composites

• ZnO-hBN nanoparticles were prepared by sol-gel synthesis method and Al-ZnO-hBN composites were prepared by powder metallurgy production methods. • ZnO-hBN reinforcement improved the tribological and mechanical properties. • It was observed that the hardness and wear resistance of the samples increased with increasing hBN additives. • The overlap of both experimental and metaheuristic algorithm-based approaches allowed more predictable results to be obtained. • The proposed methods are expected to reduce the effort and number of experiments to reach optimal mixtures. • It is predicted that more successful results will be obtained and production and testing costs will be reduced by applying the relevant methods in different application areas, especially in the chemical, construction, pharmaceutical, aerospace, medical and automotive sectors. Wear is one of the major degradation mechanisms limiting the structural reliability and service life of aluminum-based materials under contact loading. To overcome this limitation, Al-based hybrid nanocomposites reinforced with sol–gel synthesized ZnO–hBN nanoparticles were successfully fabricated by powder metallurgy, and their tribo-mechanical performance was systematically investigated. A total of 150 experimental datasets were generated under dry sliding conditions using normal loads in the range of 10–50 N. Brinell hardness, wear loss, and coefficient of friction were selected as the key performance responses and evaluated through an integrated experimental, statistical, and optimization framework. To capture the nonlinear relationships between the input parameters and output responses, polynomial regression models were developed, and the most suitable model degrees were identified as 7th for hardness, 3rd for wear loss, and 6th for coefficient of friction. These surrogate models were subsequently incorporated into a Pareto-based Multi-Objective Harris Hawks Optimization framework to determine the optimal parameter combinations. Relative to the best experimental configuration, the Pareto-optimal solution provided an 83.02% increase in Brinell hardness, a 46.61% reduction in wear loss, and a 92.13% reduction in coefficient of friction. The results further revealed that the BN ratio made a strong positive contribution to hardness and a pronounced negative contribution to friction, whereas the Al ratio and sliding distance were the dominant factors governing wear loss. Overall, the findings confirm that ZnO–hBN hybrid reinforcement significantly improves the tribo-mechanical behavior of Al matrix composites and that the proposed experimental–statistical–optimization framework offers an effective and computationally efficient route for the design of high-performance nanocomposites with reduced experimental cost and development time.