Modelling of the Steel High-Temperature Deformation Behaviour Using Artificial Neural Network

Hot forming is an essential part of the manufacturing of most steel products. The hot deformation behaviour is determined by temperature, strain rate, strain and chemical composition of the steel. To date, constitutive models are constructed for many steels; however, their specific chemical composit...

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Bibliographic Details
Published in:Metals (Basel ) 2022-03, Vol.12 (3), p.447
Main Authors: Churyumov, Alexander, Kazakova, Alena, Churyumova, Tatiana
Format: Article
Language:English
Subjects:
Accuracy
Alloying elements
artificial neural network
Artificial neural networks
Chemical composition
Compression tests
constitutive model
Constitutive models
Corrosion resistance
Corrosion resistant alloys
Corrosion resistant steels
Cr12Ni3Cu steel
Datasets
Deformation
Heat resistant steels
High alloy steels
High temperature
hot deformation
Hot forming
Investigations
Manufacturing
Mathematical models
Model accuracy
Neural networks
Reproducibility
Rheology
Software
Steel construction
Steel products
Strain rate
Stress-strain curves
Thermal simulators
thermomechanical simulator gleeble
Yield strength
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Summary:Hot forming is an essential part of the manufacturing of most steel products. The hot deformation behaviour is determined by temperature, strain rate, strain and chemical composition of the steel. To date, constitutive models are constructed for many steels; however, their specific chemical composition limits their application. In this paper, a novel artificial neural network (ANN) model was built to determine the steel flow stress with high accuracy in the wide range of the concentration of the elements in high-alloyed, corrosion-resistant steels. The additional compression tests for stainless Cr12Ni3Cu steel were carried out at the strain rates of 0.1–10 s−1 and the temperatures of 900–1200 °C using thermomechanical simulator Gleeble 3800. The ANN-based model showed high accuracy for both training (the error was 6.6%) and approvement (11.5%) datasets. The values of the effective activation energy for experimental (410 ± 16 kJ/mol) and predicted peak stress values (380 ± 29 kJ/mol) are in good agreement. The implementation of the constructed ANN-based model showed a significant influence of the Cr12Ni3Cu chemical composition variation within the grade on the flow stress at a steady state of the hot deformation.
ISSN:2075-4701
2075-4701
DOI:10.3390/met12030447