A comparative study on modified Zerilli–Armstrong, Arrhenius-type and artificial neural network models to predict high-temperature deformation behavior in T24 steel

Li, Hongying; Wang, Xiaofeng; Wei, Dongdong; Hu, Ji-Dong; Li, Yanghua

doi:10.1016/j.msea.2011.12.108

Cited by 66 publications

(23 citation statements)

References 26 publications

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“…In the research, the performance of the ANN model was evaluated by using a wide variety of standard statistical indices, which turned out that the extrapolation ability of neural network model was very high in the proximity of training domain. Li et al [16] conducted a comprehensive and comparative study on Zerilli-Armstrong, Arrhenius-type and ANN models in terms of their prediction ability of hot deformation behavior of T24 steel. Quan et al [17] applied ANN to predict the flow stress of as-cast Ti-6Al-2Zr-1Mo-1V alloy, which suggested that the ANN model has a good capacity to model complicated flow behavior of titanium alloy.…”

Section: Methodsmentioning

confidence: 99%

Modeling the Constitutive Relationship of Al–0.62Mg–0.73Si Alloy Based on Artificial Neural Network

Han

Yan

Sun

et al. 2017

Metals

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Abstract:In this work, the hot deformation behavior of 6A02 aluminum alloy was investigated by isothermal compression tests conducted in the temperature range of 683-783 K and strain-rate range of 0.001-1 s −1 . According to the obtained true stress-true strain curves, the constitutive relationship of the alloy was revealed by establishing the Arrhenius-type constitutive model and back-propagation (BP) neural network model. It is found that the flow characteristic of 6A02 aluminum alloy is closely related to deformation temperature and strain rate, and the true stress decreases with increasing temperatures and decreasing strain rates. The hot deformation activation energy is calculated to be 168.916 kJ mol −1 . The BP neural network model with one hidden layer and 20 neurons in the hidden layer is developed. The accuracy in prediction of the Arrhenius-type constitutive model and BP neural network model is eveluated by using statistics analysis method. It is demonstrated that the BP neural network model has better performance in predicting the flow stress.

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Section: Methodsmentioning

confidence: 99%

Modeling the Constitutive Relationship of Al–0.62Mg–0.73Si Alloy Based on Artificial Neural Network

Han

Yan

Sun

et al. 2017

Metals

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“…Besides, the R-values for the predicted true stress of BP-ANN and modified Arrhenius are 0.9998 and 0.9899, respectively, from another quantitative perspective proving the strong linear relationships between the predicted and experimental true stress. Additionally, the AARE-values relative to the experimental true stress was calculated by Equation (19) and exhibited in Figure 9. According to the calculation results, it is manifest that the AARE-value for the BP-ANN model is 1.20%, but, for the constitutive equation, it reaches a higher level, 3.06%.…”

Section: Prediction Capability Comparison Between the Bp-ann Model Anmentioning

confidence: 99%

Prediction of the Hot Compressive Deformation Behavior for Superalloy Nimonic 80A by BP-ANN Model

2016

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Abstract:In order to predict hot deformation behavior of superalloy nimonic 80A, a back-propagational artificial neural network (BP-ANN) and strain-dependent Arrhenius-type model were established based on the experimental data from isothermal compression tests on a Gleeble-3500 thermo-mechanical simulator at temperatures ranging of 1050-1250˝C, strain rates ranging of 0.01-10.0 s´1. A comparison on a BP-ANN model and modified Arrhenius-type constitutive equation has been implemented in terms of statistical parameters, involving mean value of relative (µ), standard deviation (w), correlation coefficient (R) and average absolute relative error (AARE). The µ-value and w-value of the improved Arrhenius-type model are 3.0012% and 2.0533%, respectively, while their values of the BP-ANN model are 0.0714% and 0.2564%, respectively. Meanwhile, the R-value and ARRE-value for the improved Arrhenius-type model are 0.9899 and 3.06%, while their values for the BP-ANN model are 0.9998 and 1.20%. The results indicate that the BP-ANN model can accurately track the experimental data and show a good generalization capability to predict complex flow behavior. Then, a 3D continuous interaction space for temperature, strain rate, strain and stress was constructed based on the expanded data predicted by a well-trained BP-ANN model. The developed 3D continuous space for hot working parameters articulates the intrinsic relationships of superalloy nimonic 80A.

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“…The output flow stress was varies from 3.86 Mpa to 237.53 Mpa. Therefore, the input and output data were measured in different units, before training, all the data need to be normalized into the dimensionless units to remove the arbitrary effect of similarity between the different data 28,29 . The temperature, strain and flow stress were normalized within the range from 0 to 0.25 using the relation given by Equation 1, the strain rate was normalized within the range from 0.05 to 0.3 using the relation given by Equation 2.…”

Section: Artificial Neural Modelmentioning

confidence: 99%

A characterization for the constitutive relationships of 42CrMo high strength steel by Artificial Neural Network and its application in isothermal deformation

Quan¹,

Liang²,

Lv³

et al. 2014

Mat. Res.

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On hot working process, the prediction of material constitutive relationship can improve the optimization design process. Recently, the artificial neural network models are considered as a powerful tool to describe the elevated temperature deformation behavior of materials. Based on the experimental data from the isothermal compressions of 42CrMo high strength steel, an artificial neural network (ANN) was trained with standard back-propagation learning algorithm to predict the elevated temperature deformation behavior of 42CrMo steel. The inputs of the ANN model are strain, strain rate and temperature, whereas flow stress is the output. According to the predicted and experimental results, it indicates that the developed ANN model shows a good capacity of modeling complex hot deformation behavior and can accurately tracks the experimental data in a wide temperature range and strain rate range. On addition, the predicted data outside of experimental conditions were obtained, indicating good prediction potentiality of the developed ANN model. The q -s curves outside of experimental conditions indicate that the predicted strain-stress curves exhibit a typical dynamic recrystallization softening characteristic of high temperature deformation behavior. Through the coupling of the ANN model and finite element model, the hot compression simulations at the temperature of 1273 K and strain rates of 0.01~10 s -1 were conducted. The results show that the predicted constitutive data outside the experimental conditions successfully improved the prediction accuracy of forming load during the FEM simulation.

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A comparative study on modified Zerilli–Armstrong, Arrhenius-type and artificial neural network models to predict high-temperature deformation behavior in T24 steel

Cited by 66 publications

References 26 publications

Modeling the Constitutive Relationship of Al–0.62Mg–0.73Si Alloy Based on Artificial Neural Network

Modeling the Constitutive Relationship of Al–0.62Mg–0.73Si Alloy Based on Artificial Neural Network

Prediction of the Hot Compressive Deformation Behavior for Superalloy Nimonic 80A by BP-ANN Model

A characterization for the constitutive relationships of 42CrMo high strength steel by Artificial Neural Network and its application in isothermal deformation

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