Hot deformation characteristics of NiTiV shape memory alloy and modeling using constitutive equations and artificial neural networks

Santosh, S.; Sampath, Santosh; Mouliswar, R. R

doi:10.1016/j.jallcom.2021.163451

Cited by 49 publications

(14 citation statements)

References 31 publications

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“…It reflects the plastic deformation capacity and the power consumption ratio of energy used to change the microstructure in each process condition. The power consumption rate

is related to the strain-rate-sensitivity index m , represented in Equation (1) [ 28 ].

…”

Section: Resultsmentioning

confidence: 99%

“…Combined with the instability criterion presented in Equation (2) [ 28 ], it can determine whether the material is stable under the corresponding deformation conditions.

…”

Section: Resultsmentioning

confidence: 99%

“…Deformation conditions in this region are unfavorable for plastic forming. Under this situation, micro-defects may occur [ 28 ]. In Figure 2 a, at 623 K, there is a hump, which may result from it being in an unstable region where the work-hardening and softening mechanisms are in an unstable equilibrium [ 29 ].…”

Section: Resultsmentioning

confidence: 99%

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Dynamic Constitutive Relationship of Mg–Gd–Y–Zr–Ag Alloy during High Temperature Deformation Process

Peng

Zhang

et al. 2023

Materials

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The thermal deformation behavior of the Mg–Gd–Y–Zr–Ag alloy was studied by isothermal hot compression tests at high temperatures. The flow stress increased with increased strain rates and decreased temperatures, first increasing and finally remaining stable with increased strain. A hot processing map was built. Using the processing map and microstructural analysis, the temperature should remain at 673–773 K for this alloy to ensure the deformation quality. The primary softening mechanism is discontinuous dynamic recrystallization (DDRX). Rising temperatures and declining strain rates facilitated the emergence and growth of Dynamic recrystallization (DRX) grains. An original JC (O–JC) model and a modified JC (M–JC) model were established. The M–JC model indicated a better prediction than the O–JC model. Still, it was deficient in predicting flow stresses with insufficient coupling effects. Hence, based on the M–JC model, a newly modified JC (NM–JC) model, which further enhances the interaction between strain and strain rate as well as strain and temperature, is proposed. Its projected values can better align with the tested values.

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“…It reflects the plastic deformation capacity and the power consumption ratio of energy used to change the microstructure in each process condition. The power consumption rate

is related to the strain-rate-sensitivity index m , represented in Equation (1) [ 28 ].

…”

Section: Resultsmentioning

confidence: 99%

“…Combined with the instability criterion presented in Equation (2) [ 28 ], it can determine whether the material is stable under the corresponding deformation conditions.

…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Constitutive Relationship of Mg–Gd–Y–Zr–Ag Alloy during High Temperature Deformation Process

Peng

Zhang

et al. 2023

Materials

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“…Since studying the absolute plastic properties of non-metallic inclusions is difficult to carry out, most experimental research sought to study the ability to deform non-metallic inclusions in the metal matrix, using relative indices comparing the plastic properties of non-metallic inclusions with those of steel in which they are included. To fully justify the need for experimental research, it should be mentioned that steel is currently one of the most used alloys in modern society [ 1 , 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Experimental Research for the Establishment of the Optimal Forging and Heat Treatment Technical Parameters for Special Purpose Forged Semi-Finishes

et al. 2023

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The authors present in this paper the experimental results and conclusions obtained after conducting a comparative study on three samples of forged semi-finished products from the steel brands 10CrMo9-10, 25CrMo4, and 42CrMo4. These are common heat-resistant alloy steels used in various industries nationally and internationally. This study aimed to test under the same identical experimental conditions of forging and heat treatment of three samples made of three different brands of steels 10CrMo9-10, 25CrMo4, and 42CrMo4. Analyzing the experimental results obtained, it can be seen for which of the three brands of tested steels the best forging and heat treatment parameters are obtained. Following experimental research, the best material was determined by analyzing the results obtained for the mechanical characteristics (tensile tests according to DNVGL-RP0034-SFC2 and NACE MR0175–hardness 207-235 HBW) and austenitic grain size. The authors determined that among the three types of steels analyzed, 10CrMo9-10 best meets the imposed requirements. This statement is in view of the comparative analysis of the results of experimental research.

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“…It describes the dynamic response of material stress to temperature, deformation rate, strain, and other process parameters under specific conditions. The Arrhenius-type models [5] are widely used in TMP, [6][7][8] owing to their simple form, easy calibration of parameters, and the ability to relatively accurately track the deformation behavior of various materials [9][10][11] throughout the entire thermomechanical process. The Arrhenius equation can be used to determine the activation energy of hot deformation [12] in relation to microstructure evolution as well.…”

Section: Introductionmentioning

confidence: 99%

A Modified Arrhenius‐Type Constitutive Model and its Implementation by Means of the Safe Version of Newton–Raphson Method

Liang

Kong

Zhang

et al. 2022

steel research int.

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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 radial return mapping algorithms. A root‐finding method called the safe version of Newton–Raphson method is applied to calculate the plastic corrector term. The efﬁciency and accuracy of both analytical and numerical methods are evaluated for calculating the derivatives in this algorithm. The implementation of the user‐defined modified Arrhenius‐type material model is successfully applied to a thermal–mechanical coupling FE model for the hot rolling of heavy steel plate. The goal optimization function is used to calibrate the material parameters for the model. The plastic strain field and temperature field are in accordance with the theoretical derivation and experimental results. It provides valuable references for characterizing and optimizing thermomechanical processing.

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Hot deformation characteristics of NiTiV shape memory alloy and modeling using constitutive equations and artificial neural networks

Cited by 49 publications

References 31 publications

Dynamic Constitutive Relationship of Mg–Gd–Y–Zr–Ag Alloy during High Temperature Deformation Process

Dynamic Constitutive Relationship of Mg–Gd–Y–Zr–Ag Alloy during High Temperature Deformation Process

Experimental Research for the Establishment of the Optimal Forging and Heat Treatment Technical Parameters for Special Purpose Forged Semi-Finishes

A Modified Arrhenius‐Type Constitutive Model and its Implementation by Means of the Safe Version of Newton–Raphson Method

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