Application of Various Optimisation Methods in the Multi-Optimisation for Tribological Properties of Al–B₄C Composites

This paper presents an investigation of the tribological performance of AA2024–B4C composites, with a specific focus on the influence of reinforcement and processing parameters. In this study three input parameters were varied: B4C weight percentage, milling time, and normal load, to evaluate their effects on two output parameters: wear loss and the coefficient of friction. AA2024 alloy was used as the matrix alloy, while B4C particles were used as reinforcement. Due to the high hardness and wear resistance of B4C, the optimized composite shows strong potential for use in aerospace structural elements and automotive brake components. The optimisation of tribological behaviour was conducted using a Taguchi-Grey Relational Analysis (Taguchi-GRA) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). A total of 27 combinations of input parameters were analysed, varying the B4C content (0, 10, and 15 wt.%), milling time (0, 15, and 25 h), and normal load (1, 5, and 10 N). Wear loss and the coefficient of friction were numerically evaluated and selected as criteria for optimisation. Artificial Neural Networks (ANNs) were also applied for two outputs simultaneously. TOPSIS identified Alternative 1 as the optimal solution, confirming the results obtained using the Taguchi Grey method. The optimal condition obtained (10 wt.% B4C, 25 h milling time, 10 N load) resulted in a minimum wear loss of 1.7 mg and a coefficient of friction of 0.176, confirming significant enhancement in tribological behaviour. Based on the results, both the B4C content and the applied processing conditions have a significant impact on wear loss and frictional properties. This approach demonstrates high reliability and confidence, enabling the design of future composite materials with optimal properties for specific applications.