Artificial Neural Network Approach for Hardness Prediction in High-Entropy Alloys.

ABSTRACT: High-entropy alloys (HEAs) are highly concentrated, multicomponent alloys that have received significant attention due to their superior properties compared to conventional alloys. The mechanical properties and hardness are interrelated, and it is widely known that the hardness of HEAs depends on the principal alloying elements and their composition. Therefore, the desired hardness prediction to develop new HEAs is more interesting. However, the relationship of these compositions with the HEA hardness is very complex and nonlinear. In this study, we develop an artificial neural network (ANN) model using experimental data sets (535). The compositional elements-Al, Co, Cr, Cu, Mn, Ni, Fe, W, Mo, and Ti-are considered input parameters, and hardness is considered as an output parameter. The de

SUBMITTER: Nchekwube M 

PROVIDER: S-EPMC12565321 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature