Cuckoo search-artificial neural network aided the composition design in Al–Cr–Co–Fe–Ni high entropy alloys

In this work, we proposed a data-driven approach combining machine learning (ML), experiments and molecular dynamic (MD) simulation to accelerate the composition design and exploration of wear behavior in Al–Cr–Co–Fe–Ni based high entropy alloys. A Cuckoo Search-Artificial Neural Network (CS-ANN) model was proposed to deal with small sample data and showed fairly strong learning ability and generalization capabilities. Elemental analysis revealed that the microhardness can be enhanced by adding Al or reducing the proportion of Fe and Ni. Then the Al1.1CrCoFe0.9Ni0.9 HEAs were designed and prepared for experimental verification. The microstructure, phase composition, hardness, and wear resistance properties of the alloy were further investigated. Al1.1CrCoFe0.9Ni0.9 HEAs were composed of BCC+B2 phases, exhibited a microhardness of 580.6 HV. The wear rate was measured as 6.04×10⁻⁶ mm³/(Nm) by the reciprocating dry wear tests, the average COF was estimated as ∼0.5. The main wear mechanisms involved abrasive wear and oxidation wear. MD simulations are used to analyze the atomic evolution and deformation mechanism during the nano scratch process. The obtained information would help either facilitate developing novel HEAs or utilize the HEAs effectively to resist wear.