Modeling the flow behavior of Haynes 214 superalloy during hot deformation using mathematical and artificial intelligence-based models

Abstract

This work proposes, enhances, and compares various mathematical and artificial intelligence-based models for the modeling and prediction of the flow behavior of Haynes 214 superalloy at hot deformation. The utilized models are as follows: Johnson-Cook (JC), three modifications of JC (M1_JC, M2_JC, and M3_JC), Artificial Neural Network (ANN), and Subtractive Clustering-Fuzzy Interference System (SC-FIS). The predictions of the flow behavior are evaluated and assessed using various statistical error measures, namely, correlation coefficient (R), Relative Error (RE), and Root Mean Square Error (RMSE). The results showed that the M3_JC is the best addressed mathematical model in terms of flow prediction accuracy, while the Artificial Intelligence (AI) based SC-FIS model outperformed all of the six addressed mathematical and AI-based models with an R value of 0.999, RE range of − 0.79–1.15% and an RMSE value of as low as 0.89 MPa.