Machine learning assisted optimization of tribological parameters of Al–Co–Cr–Fe–Ni high-entropy alloy

Abstract

ABSTRACT High-entropy alloys (HEA) have become increasingly important in the materials world due to their ability to combine multiple principal elements in an alloy system to achieve desired mechanical and tribological properties. In this study, Al–Co–Cr–Fe–Ni HEA alloy is synthesized using vacuum induction melting and its tribological properties are analyzed under dry conditions, which is important since dry sliding is a common mode of wear in many engineering applications. To enhance its tribological performance, evolutionary data-driven modeling and optimization techniques are applied to determine the best possible solutions for the loading condition at different frequency levels. A neural net-based algorithm (EvoNN) is utilized in the data-driven modeling process to generate and train models, which are then used in the optimization process. The reference vector-based algorithm (cRVEA) is incorporated with the EvoNN to evaluate the optimum wear, coefficient of friction (COF), and surface roughness results in multidimensional hyperspace. The results obtained from this study fall within the experimental data range and satisfy the tribological requirements under optimum conditions. The excellent wear resistance of HEAs due to their high hardness, strength, and resistance to deformation makes them suitable for use in various wear applications, including cutting tools, bearings, and gears. Overall, this study highlights the potential of HEAs and data-driven modeling techniques for the development of advanced materials with superior properties and performance.