2022
DOI: 10.1002/srin.202200443
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A Modified Arrhenius‐Type Constitutive Model and its Implementation by Means of the Safe Version of Newton–Raphson Method

Abstract: Arrhenius‐type constitutive models are widely used in thermomechanical processing due to their ease of parameter calibration and ability to track the deformation behavior with various materials. Herein, a modified Arrhenius model is proposed, and the model is implemented in the finite element (FE) simulation to improve the simulation accuracy. The VUMAT Fortran subroutine of the model is established for the commercial nonlinear FE software Abaqus/Explicit based on an implicit time integration scheme and the ra… Show more

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Cited by 6 publications
(11 citation statements)
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“…As introduced in Section 1, implementing a flow law in a FE code requires both the computation of the flow stress σ y as a function of the input data, performed using the previous Equations ( 5)-( 11), but also the evaluation of the three derivatives of σ y with respect to the input data to use a Newton-Raphson algorithm within the stress integration scheme, as proposed by many authors [24,28,45,46] based on the radial return algorithm in the Abaqus FE code. It is, therefore, necessary to perform a numerical evaluation of these three derivatives based on the ANN to obtain these quantities.…”
Section: Computation Of the Derivatives Of The Neural Networkmentioning
confidence: 99%
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“…As introduced in Section 1, implementing a flow law in a FE code requires both the computation of the flow stress σ y as a function of the input data, performed using the previous Equations ( 5)-( 11), but also the evaluation of the three derivatives of σ y with respect to the input data to use a Newton-Raphson algorithm within the stress integration scheme, as proposed by many authors [24,28,45,46] based on the radial return algorithm in the Abaqus FE code. It is, therefore, necessary to perform a numerical evaluation of these three derivatives based on the ANN to obtain these quantities.…”
Section: Computation Of the Derivatives Of The Neural Networkmentioning
confidence: 99%
“…ε and temperatures T. From the observations made, we can define two main classes of behavior laws: the flow laws based on physics and the empirical flow laws. From the mechanics of continuous media and experimental tests and depending on the materials used, several flow models have been developed in the past, including the Johnson-Cook flow law [8,9], the Zerilli-Armstrong flow law [10] and their respective derived forms [11][12][13][14][15][16][17][18][19], the Hansel-Spittle [20,21] or the Arrhenius [22][23][24] flow laws, to name only a few of the most widely used in the metal-forming processes at high temperature. As an example, and because it is widely used in numerical simulation of metal forming processes, the equation that describes the Johnson-Cook flow law [8] is given as follows:…”
Section: Introductionmentioning
confidence: 99%
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