Prediction of flow stress in a wide temperature range involving phase transformation for as-cast Ti–6Al–2Zr–1Mo–1V alloy by artificial neural network

Quan, Guo-zheng; Lv, Wen-quan; Mao, Yuan-ping; Zhang, Yanwei; Zhou, Jie

doi:10.1016/j.matdes.2013.02.033

Cited by 75 publications

(51 citation statements)

References 19 publications

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“…In contrast, the flow stress increases with the increasing of the strain rate while for a fixed temperature, which is owing to an increase of the dislocation multiplication rate and dislocation density [4]. All the true strain-stress can be summarized in three distinct stages of the stress evolution with strain [4,6,7]. At the first stage of the forming process, the flow stress rapidly increases to a critical As shown in Figure 1, the strain rate and temperature have a significant effect on the flow curves.…”

Section: Flow Behavior Characteristics Of Superalloy Nimonic 80amentioning

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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Section: Flow Behavior Characteristics Of Superalloy Nimonic 80amentioning

confidence: 99%

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

2016

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“…(10) does not consider the influence of strain. Afterwards, Quan et al calculated the improved Arrheniustype constitutive model for as-cast Ti-6Al-2Zr-1Mo-1V alloy in the reference 8 , which was incorporated with the influence of strain, as expressed by Eq. (11).…”

Section: The Existing Improved Arrhenius-type Constitutive Model and mentioning

confidence: 99%

“…The improved Arrhenius-type constitutive model cannot accurately track the hot flow behaviors, because the mathematical regression method is difficult to describe the complicated non-linear flow behaviors which accompanied with phase transformation, WH, DRV, and DRX in wide temperature and strain rate intervals. Quan et al established the ANN model for as-cast Ti6Al-2Zr-1Mo-1V alloy with high R-value and small AARE-value , however, the input variables just contain deformation temperature (T) and strain (ε) 8 . The input variables of GA-SVR contain temperature (T), strain (ε), and strain rate (ε).…”

Section: Ann and Ga-svr The Aare-values Of The Improvedmentioning

confidence: 99%

“…It can be concluded that the GA-SVR can predict flow stress data and reduce the interpolation interval to enhance the simulation accuracy. map of stress data corresponding to temperature and strain under constant strain rates 8 . In this work, the stress data at temperatures of 1098 K, 1148 K, 1198 K, 1248 K, and 1298 K under strain rates of 0.01 s -1 , 0.1 s -1 , 1 s -1 and 10 s -1 were predicted for Ti-6Al-2Zr-1Mo-1V alloy by the well-trained GA-SVR.…”

Section: Accuracy Improvement In Finite Elementmentioning

confidence: 99%

“…It is important to establish a model to accurately construct and further predict the highly non-linear flow behaviors. At present, there exist four typical materials constitutive models in modeling hot flow behaviors of metals, namely, empirical/ semiempirical model, analytical model, phenomenological model, and intelligence algorithm [6][7][8][9] .…”

Section: Introductionmentioning

confidence: 99%

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Numerical Description of Hot Flow Behaviors at Ti-6Al-2Zr-1Mo-1V Alloy By GA-SVR and Relative Applications

et al. 2016

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Hot compression tests of as-cast Ti-6Al-2Zr-1Mo-1V alloy in a wide temperature range of 1073-1323 K and strain rate range of 0.01-10 s-1 were conducted by a servo-hydraulic and computer-controlled Gleeble-1500 machine. The hot flow behaviors of Ti-6Al-2Zr-1Mo-1V alloy show highly non-linear relationships with strain, strain rate and temperature. In order to accurately and effectively characterize the complex flow behaviors, support vector regression (SVR) which is a machine learning method was combined with Genetic Algorithm (GA) to characterize the flow behaviors, namely, the GA-SVR. The study abilities, generation abilities, and modeling efficiencies of the improved Arrhenius-type constitutive model, ANN, and GA-SVR for flow behaviors of as-cast Ti-6Al-2Zr-1Mo-1V alloy were detailedly compared. Comparison results show that the study ability of the GA-SVR is as strong as the ANN. The generation abilities and modeling efficiencies of these models were shown as follows in ascending order: the improved Arrhenius-type constitutive model < ANN < GA-SVR. Based on the established GA-SVR, the continuously three-dimensional relationships among flow stress, temperature, strain, and strain rate were constructed, which improve the simulation accuracy and related research fields where stress-strain data play important roles.

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Compression Deformation Behavior and Processing Map of Pure Copper

et al. 2016

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To reveal compression deformation behavior of pure copper, the deformation characteristics of pure copper have been investigated by means of compression tests in the temperature range of 400-900°C and strain rate range of 0.001-1 s -1 . The results show that the flow stress of pure copper increases with increasing strain rate and decreasing deformation temperature, which is characterized by work-hardening, dynamic recovery, dynamic recrystallization, and secondary work-hardening, etc. The activation energy of hot deformation is associated with deformation temperature and strain rate, and the average activation energy is calculated to be 303.8 kJ/mol. The flow stress prediction model based on GA+BP possess, is in very good agreement with the true stress curve, which is of significance to the guidance of hot working of pure copper. The flow instability occurs in the intermediate strain rate region (0.01-0.1 s -1 ) base on the analysis of processing map, the high power dissipation correspond to the dynamic recrystallization. Appropriate reducing the deformation temperature or increasing the strain rate is beneficial for the grain refinement in the steady-state region of the processing map.

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Prediction of flow stress in a wide temperature range involving phase transformation for as-cast Ti–6Al–2Zr–1Mo–1V alloy by artificial neural network

Cited by 75 publications

References 19 publications

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

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

Numerical Description of Hot Flow Behaviors at Ti-6Al-2Zr-1Mo-1V Alloy By GA-SVR and Relative Applications

Compression Deformation Behavior and Processing Map of Pure Copper

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