Strain Compensation Constitutive Model and Parameter Optimization for Nb-Contained 316LN

Li, Jingdan; Liu, Jiansheng

doi:10.3390/met9020212

Cited by 9 publications

(6 citation statements)

References 33 publications

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“…He et al [16] verified existing flow stress models capability to capture the flow behavior in Ti2AlNb-based alloys and the constitutive equation displayed a more precise description against the experimental data. Yang et al [17] and Li et al [18] researched the advantage of using the strain-compensated AC equation to establish the flow stress model for super-austenitic and Nb-contained 316LN stainless steel materials during hot working, respectively. They claimed that the modified AC equation provided a high accuracy in flow stress predictions.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Machine Learning Optimization Approach to Predict Hot Deformation Behavior of Medium Carbon Steel Material

Murugesan

Sajjad

Jung

2019

Metals

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The isothermal tensile test of medium carbon steel material was conducted at deformation temperatures varying from 650 to 950 ∘ C with an interval of 100 ∘ C and strain rates ranging from 0.05 to 1.0 s − 1 . In addition, the scanning electron microscopy (SEM) procedures were exploited to study about the surface morphology of medium carbon steel material. Using the experimental data, the artificial neural network (ANN) model with a back-propagation (BP) algorithm was proposed to predict the hot deformation behavior of medium carbon steel material. For model training and testing purpose, the variables such as deformation temperature, strain rate, and strain data were considered as inputs and the flow stress data were used as targets. Before running the neural network, the test data were normalized to effectively run the problem and after solving the problem, the obtained results were again converted in order to achieve the actual data. According to the predicted results, the coefficient of determination ( R 2 ) and the average absolute relative error between the predicted flow stress and the experimental data were determined as 0.999 and 1.335%, respectively. For improving the model predictability, the constrained nonlinear function based optimization procedures was adopted to obtain the best candidate selections of weights and biases. By evaluating each test conditions, it was found that the average absolute relative error based on the optimized ANN-BP model varied from 0.728% to 1.775%. Overall, the trained ANN-BP models proved to be much more efficient and accurate by means of flow stress prediction against the experimental data for all the tested conditions. These optimized results displayed that an ANN-BP model is more accurate for flow stress prediction than that of the conventional flow stress models.

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Section: Introductionmentioning

confidence: 99%

Hybrid Machine Learning Optimization Approach to Predict Hot Deformation Behavior of Medium Carbon Steel Material

Murugesan

Sajjad

Jung

2019

Metals

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“…Equation (15) is constructed by taking into account the strain effect on the hardening process; meanwhile, the relation (shown in Equation (8)) between σ r and ε is used to describe the strain-hardening process. Taking Equation (8) into Equation (15), the fractional softening model is expressed as follows:…”

Section: Description Of Fractional Softeningmentioning

confidence: 99%

“…Among the constitutive models mentioned above, the Arrhenius-type model with sine-hyperbolic law has been successfully and widely applied for predicting the flow behavior of alloys in a hot deformation. For example, Liu et al, Tabei et al and Zhang et al used the model to describe the flow behavior of the 316LN alloy, Ti-6Al-4V alloy and 6N01 aluminum alloy at elevated temperatures, respectively [15][16][17]. Although widely used in the hot deformation area, the strain effects on flow behavior are not considered in the model, which reduces the accuracy in some conditions.…”

Section: Introductionmentioning

confidence: 99%

Avrami Kinetic-Based Constitutive Relationship for Armco-Type Pure Iron in Hot Deformation

Zhang

Fan

Zhang

et al. 2019

Metals

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The work presents a full mathematical description of the stress-strain compression curves in a wide range of strain rates and deformation temperatures for Armco-type pure iron. The constructed models are based on a dislocation structure evolution equation (in the case of dynamic recovery (DRV)) and Avrami kinetic-based model (in the case of dynamic recrystallization (DRX)). The fractional softening model is modified as: X = ( σ 2 − σ r 2 ) / ( σ d s 2 − σ r 2 ) considering the strain hardening of un-recrystallized regions. The Avrami kinetic equation is modified and used to describe the DRX process considering the strain rate and temperature. The relations between the Avrami constant k ∗ , time exponent n ∗ , strain rate ε ˙ , temperature T and Z parameter are discussed. The yield stress σ y , saturation stress σ r s , steady stress σ d s and critical strain ε c are expressed as the functions of the Z parameter. A constitutive model is constructed based on the strain-hardening model, fractional softening model and modified Avrami kinetic equation. The DRV and DRX characters of Armco-type pure iron are clearly presented in these flow stress curves determined by the model.

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“…A similar result was obtained by developing constitutive models of 21–4 N heat resistant steel [ 9 ]. Moreover, the strain-compensated Arrhenius-type equation is most widely applied to describe the high-temperature flow behavior of metals and alloys [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. These results mean that the strain-compensated Arrhenius-type model has a relatively strong predictive ability.…”

Section: Introductionmentioning

confidence: 99%

Constitutive Equations for Describing the Hot Compressed Behavior of TC4–DT Titanium Alloy

Wang

et al. 2020

Materials

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Isothermal hot compression tests of TC4–DT titanium alloy were performed under temperatures of 1203–1293 K and strain rates of 0.001–10 s−1. The purpose of this study is to develop a new high-precision modified constitutive model that can describe the deformation behavior of TC4–DT titanium alloy. Both the modified strain-compensated Arrhenius-type equation and the modified Hensel–Spittel equation were established by revising the strain rate. The parameters in the above two modified constitutive equation were solved by combining regression analysis with iterative methods, which was used instead on the traditional linear regression methods. In addition, both the original strain-compensated Arrhenius-type equation and Hensel–Spittel equation were established to compare with the new modified constitutive equations. A comparison of the predicted values based on the four constitutive equations was performed via relative error, average absolute relative error (AARE) and the correlation coefficient (R). These results show the modified Arrhenius-type equation and the modified Hensel–Spittel equation is more accurate and efficient with a similar prediction accuracy. The AARE-value of the two modified constitutive equation is relatively low under various strain rates and their fluctuation is small as the strain rate changes.

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Strain Compensation Constitutive Model and Parameter Optimization for Nb-Contained 316LN

Cited by 9 publications

References 33 publications

Hybrid Machine Learning Optimization Approach to Predict Hot Deformation Behavior of Medium Carbon Steel Material

Hybrid Machine Learning Optimization Approach to Predict Hot Deformation Behavior of Medium Carbon Steel Material

Avrami Kinetic-Based Constitutive Relationship for Armco-Type Pure Iron in Hot Deformation

Constitutive Equations for Describing the Hot Compressed Behavior of TC4–DT Titanium Alloy

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