Sustainable development and tribological optimization of CaO-reinforced UHMWPE composites via surrogate modeling and evolutionary algorithms

This study presents a sustainable, performance-oriented approach for developing tribologically efficient polymer composites by reinforcing ultra-high molecular weight polyethylene (UHMWPE) with calcium oxide (CaO) synthesized from waste eggshells. CaO particles were prepared through calcination at 850°C and modified using silane to enhance compatibility with the UHMWPE matrix. Composites containing 0.5–1.5 wt% CaO were fabricated via compression molding and evaluated under varying loads and sliding speeds in simulated body fluid using a central composite design. An artificial neural network (ANN) model accurately predicted wear rate (R 2 = 0.9792) and coefficient of friction (COF) (R 2 = 0.9007). Multi-objective optimization using the Non-dominated Sorting Genetic Algorithm III (NSGA-III) produced 30 Pareto-optimal solutions, with a hypervolume of 0.78 and generational distances of 2.65 mg/km (wear rate) and 0.014 (COF). The experimentally validated knee point (110 N, 0.2 m/s, 1 wt% CaO) showed less than 5% deviation from predicted results. SEM analysis of worn surfaces revealed dominant wear mechanisms such as micro-ploughing, particle pullout, and delamination. This work demonstrates the potential of combining waste valorization and AI-based optimization to engineer high-performance, eco-friendly composites suitable for biomedical applications.