文献类型：期刊文献

英文题名：Constitutive Modeling of A286 Superalloy: Comparison of Improved Khan-Huang-Liang, Improved Johnson-Cook, and Genetic Algorithm-Backpropagation Artificial Neural Network Models

作者：Tao, Liang Feng, Zhiguo Jiang, Yulian Mo, Ningning Lu, Rengang

第一作者：Tao, Liang;陶亮

通信作者：Feng, ZG[1];Feng, ZG[2]

机构：[1]Guizhou Univ, Sch Mech Engn, Guiyang 550025, Peoples R China;[2]Guizhou Inst Technol, Sch Mech Engn, Guiyang 550003, Peoples R China;[3]Guizhou Univ, Key Lab Special Equipment & Mfg Technol, Guiyang 550025, Peoples R China

第一机构：Guizhou Univ, Sch Mech Engn, Guiyang 550025, Peoples R China

通信机构：corresponding author), Guizhou Univ, Sch Mech Engn, Guiyang 550025, Peoples R China;corresponding author), Guizhou Univ, Key Lab Special Equipment & Mfg Technol, Guiyang 550025, Peoples R China.

年份：2025

外文期刊名：JOURNAL OF MATERIALS ENGINEERING AND PERFORMANCE

收录：;EI(收录号：20252118467941);Scopus(收录号：2-s2.0-105005591781);WOS:【SCI-EXPANDED(收录号:WOS:001493762500001)】；

基金：The authors gratefully acknowledge the support from the National Natural Science Foundation of China (Grant No.52165042), Guizhou Provincial Science and Technology Projects (Grant No. QKHJC ZD [2025] 041), Guizhou Provincial Science and Technology Supporting Program (Grant No.2023G308), Excellent Young Talents Project of Guizhou Province (Grant No.20215617), Guiyang Municipal Project for Fostering Science and Technology Talents (Grant No.202143-1). Guizhou University Talent Introduction Research Fund (Grant No.202209). All authors approved the version of the manuscript to be published.

语种：英文

外文关键词：artificial neural network; constitutive model; genetic algorithm; numerical simulation; superalloy

摘要：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 degrees 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%).