Constitutive Modeling of A286 Superalloy: Comparison of Improved Khan-Huang-Liang, Improved Johnson-Cook, and Genetic Algorithm-Backpropagation Artificial Neural Network Models - Tao, Liang; Feng, Zhiguo; Jiang, Yulian; Mo, Ningning; Lu, Rengang (Journal of materials engineering and performance, 2025)
This study aims to develop high-precision constitutive models for A286 superalloy to accurately predict material flow stress and provide robust support for numerical simulations. Uniaxial quasi-static compression tests were performed to obtain the flow stress of A286 superalloy across strain rates from 0.01 to 10 s −1 and temperatures from 25 to 600 °C. Based on the experimental data, three constitutive models were developed: an improved Khan-Huang-Liang (KHL) model, an improved Johnson-Cook (J-C) model, and a genetic algorithm-backpropagation artificial neural network (GA-BP ANN) model. The accuracy of the three models in predicting flow stress and their performance in numerical simulations were evaluated using the correlation coefficient ( R ), average absolute relative error ( AARE ), and relative error ( RE ). Results demonstrate that all three models effectively predict the material’s flow stress and are successfully applied in thin-walled tube simulations. Among them, the GA-BP ANN model achieves the highest accuracy in predicting flow stress ( R = 0.999, AARE = 1.6%) and simulation precision ( RE = − 5.1%), followed by the improved J-C model ( R = 0.979, AARE = 1.7%, RE = − 5.3%), and the improved KHL model with the lowest performance ( R = 0.953, AARE = 3.5%, RE = − 6.2%).