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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Veröffentlicht in:	Journal of materials engineering and performance Jg. 34; H. 23; S. 28170 - 28182
Hauptverfasser:	Tao, Liang, Feng, Zhiguo, Jiang, Yulian, Mo, Ningning, Lu, Rengang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.12.2025 Springer Nature B.V
Schlagworte:	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
Online-Zugang:	Volltext
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