Leveraging machine learning to minimize experimental trials and predict hot deformation behaviour in dual phase high entropy alloys

Abstract

In recent time, high entropy alloys (HEAs) are widely used due to their wide design space and remarkable properties allowing a vast range of property variations with myriads of possible combinations of constituent elements. This research utilizes machine learning techniques, including Random Forest (RF), K-Nearest Neighbors (KNN), XGBoost (XGB), Decision Tree (DT), and Support Vector Regressor (SVR), to predict the flow stress behavior of the dual FCC phase CoCrCu1.2FeNi high entropy alloy (HEA) at new temperature and strain rates to reduce the experiments dependency. The RF and KNN models were identified as top performers, with R² values of 0.987 and 0.986, respectively. These models successfully predicted flow stresses, aligning closely with actual measurements. A new flow stress-strain curve was generated at new temperature of 1123K, demonstrating good performance metrics for RF and KNN. with R² values of 0.97 and 0.958 respectively. This approach has the potential to significantly reduce the need for extensive experimental work in determining the mechanical behaviour of HEA, offering substantial savings in time, cost, and energy, and marks a significant advancement in the development of such alloys.