Microstructural based models for bcc and fcc metals with temperature and strain rate dependency

Voyiadjis, George Z.; Abed, Farid

doi:10.1016/j.mechmat.2004.02.003

Cited by 277 publications

(140 citation statements)

References 39 publications

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“…Contrarily to bcc materials, the flow stress for fcc materials is known to combine multiplicatively the influence of both strain hardening and thermal/viscous contributions (see Zerilli &Armstrong, 1987, andAbed, 2005 …”

Section: Dynamic Modelling 252mentioning

confidence: 99%

Adiabatic Shear: Pre- and Post-Critical Dynamic Plasticity Modelling and Study of Impact Penetration. Heat Generation in this Context

Longère¹,

Dragon²

2010

Dynamic Modelling

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Section: Dynamic Modelling 252mentioning

confidence: 99%

Adiabatic Shear: Pre- and Post-Critical Dynamic Plasticity Modelling and Study of Impact Penetration. Heat Generation in this Context

Longère¹,

Dragon²

2010

Dynamic Modelling

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“…10 . The physical-based analytical model should deeply understand many microscopic deformation mechanisms and further establish mathematic model for them, otherwise, the physical-based analytical model cannot accurately characterize the highly non-linear flow behaviors 11,12 . Besides, the analytical models require a large amount of precise experiment data to mathematically model complicated microscopic deformation mechanisms 13,14 .…”

Section: Introductionmentioning

confidence: 99%

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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“…14 . The analytical models such as Fields-Backofen (FB) and Mechanical Threshold Stress (MTS) need clear and deep understanding of the evolution of microstructure, and then the models which reveal the evolution law of microstructures are incorporated into the analytical models 15,16 . Moreover, analytical models need a large number of experimental data from accurately controlled experiments and the construction of mathematical relations on intricate microscopic evolutions.…”

Section: Introductionmentioning

confidence: 99%

Modelling the Hot Flow Behaviors of AZ80 Alloy by BP-ANN and the Applications in Accuracy Improvement of Computations

et al. 2015

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Hot compressions of as-cast AZ80 magnesium alloy in a wide temperature range of 523-673 K and strain rate range of 0.01-10 s -1 with a height reduction of 60% were conducted by a Gleeble-1500 thermo-mechanical test simulator. The hot flow behaviors show highly non-linear intrinsic relationships with temperature, strain and strain rate. In order to model the complicated flow behaviors, error back-propagation algorithm, a representative method to minimize the target error, was selected to train the artificial neural network. A comparative study was made on the predictabilities of the improved Arrhenius-type and BP-ANN model by using two standard statistical parameters including correlation coefficient (R) and average absolute relative error (AARE). Comparison results show that the well-trained BP-ANN has higher prediction accuracy. Three highlight applications were presented. Firstly, the strain-stress data volume was expanded by BP-ANN predictions above experimental conditions. Secondly, the expanded data were applied in the simulations of isothermal compressions, and high simulation accuracy for the load-stroke curve was achieved. Thirdly, a three-dimensional (3D) interaction space of stress, strain, temperature and strain rate was constructed based on the intensive data, which supplies the stress data to arbitrary temperature, strain rate, and strain.

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Microstructural based models for bcc and fcc metals with temperature and strain rate dependency

Cited by 277 publications

References 39 publications

Adiabatic Shear: Pre- and Post-Critical Dynamic Plasticity Modelling and Study of Impact Penetration. Heat Generation in this Context

Adiabatic Shear: Pre- and Post-Critical Dynamic Plasticity Modelling and Study of Impact Penetration. Heat Generation in this Context

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

Modelling the Hot Flow Behaviors of AZ80 Alloy by BP-ANN and the Applications in Accuracy Improvement of Computations

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