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

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 f...

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Vydáno v:	Journal of materials engineering and performance Ročník 34; číslo 23; s. 28170 - 28182
Hlavní autoři:	Tao, Liang, Feng, Zhiguo, Jiang, Yulian, Mo, Ningning, Lu, Rengang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.12.2025 Springer Nature B.V
Témata:	Accuracy Artificial neural networks Back propagation Back propagation networks Characterization and Evaluation of Materials Chemistry and Materials Science Compression tests Computer simulation Constitutive models Correlation coefficients Corrosion and Coatings Deformation Engineering Design Genetic algorithms Heat treating Materials Science Metal fatigue Neural networks Original Research Article Quality Control Reliability Safety and Risk Simulation Software Strain hardening Superalloys Temperature Titanium alloys Tribology Yield strength Yield stress United States > US genetic algorithm artificial neural network constitutive model numerical simulation superalloy
ISSN:	1059-9495, 1544-1024
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Abstract	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%).
AbstractList	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%). 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%).
Author	Tao, Liang Lu, Rengang Jiang, Yulian Feng, Zhiguo Mo, Ningning
Author_xml	– sequence: 1 givenname: Liang surname: Tao fullname: Tao, Liang organization: School of Mechanical Engineering, Guizhou University, School of Mechanical Engineering, Guizhou Institute of Technology – sequence: 2 givenname: Zhiguo surname: Feng fullname: Feng, Zhiguo email: zgfeng@gzu.edu.cn organization: School of Mechanical Engineering, Guizhou University, Key Laboratory of Special Equipment and Manufacturing Technology, Guizhou University – sequence: 3 givenname: Yulian surname: Jiang fullname: Jiang, Yulian organization: School of Mechanical Engineering, Guizhou University – sequence: 4 givenname: Ningning surname: Mo fullname: Mo, Ningning organization: School of Mechanical Engineering, Guizhou University – sequence: 5 givenname: Rengang surname: Lu fullname: Lu, Rengang organization: School of Mechanical Engineering, Guizhou University
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SubjectTerms	Accuracy Artificial neural networks Back propagation Back propagation networks Characterization and Evaluation of Materials Chemistry and Materials Science Compression tests Computer simulation Constitutive models Correlation coefficients Corrosion and Coatings Deformation Engineering Design Genetic algorithms Heat treating Materials Science Metal fatigue Neural networks Original Research Article Quality Control Reliability Safety and Risk Simulation Software Strain hardening Superalloys Temperature Titanium alloys Tribology Yield strength Yield stress
Title	Constitutive Modeling of A286 Superalloy: Comparison of Improved Khan-Huang-Liang, Improved Johnson-Cook, and Genetic Algorithm-Backpropagation Artificial Neural Network Models
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