scholarly journals Constitutive Modeling of the Flow Stress Behavior for the Hot Deformation of Cu-15Ni-8Sn Alloys

2020 ◽  
Vol 7 ◽  
Author(s):  
Dongxin Niu ◽  
Chao Zhao ◽  
Daoxi Li ◽  
Zhi Wang ◽  
Zongqiang Luo ◽  
...  
Keyword(s):  
Flow Stress ◽  
Relative Error ◽  
Hot Deformation ◽  
Hot Extrusion ◽  
Compression Tests ◽  
Arrhenius Model ◽  
Stress Behavior ◽  
Flow Stress Behavior ◽  
Average Absolute Relative Error ◽  
Jc Model

Three constitutive models, strain-compensated Arrhenius model, modified Johnson–Cook (JC) model, and modified Zerilli–Armstrong (ZA) model, were established for the hot-deformed Cu-15Ni-8Sn alloy based on hot compression tests. By introducing average absolute relative error (AARE), correlation coefficient (R), and relative error, the prediction accuracy of these three models was assessed. The results indicate that strain-compensated Arrhenius model has the highest accuracy at describing the flow stress behavior of the studied alloy, followed by modified JC model and modified ZA model. Moreover, the strain-compensated Arrhenius model established in this work has a great practicability in the hot-extrusion simulation of Cu-15Ni-8Sn alloys. This article provides a theoretical basis for optimizing hot deformation parameters in industrial production of the Cu-15Ni-8Sn alloys.

A modified Arrhenius model for as-quenched Al-Mg-Si alloy considering the effect of cooling rate

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ruichao Guo ◽  
Jianjun Wu ◽  
Yinxiang Ren
Keyword(s):  
Residual Stress ◽  
Flow Stress ◽  
Cooling Rate ◽  
Flow Behavior ◽  
Constitutive Behavior ◽  
Strain Rates ◽  
Arrhenius Model ◽  
Content Type ◽  
Stress Behavior ◽  
Flow Stress Behavior

Purpose Accurate prediction of residual stress requires precise knowledge of the constitutive behavior of as-quenched material. This study aims to model the flow stress behavior for as-quenched Al-Mg-Si alloy. Design Methodology Approach In the present work, the flow behavior of as-quenched Al-Mg-Si alloy is studied by the hot compression tests at various temperatures (573–723 K), strain rates (0.1–1 s−1) and cooling rates (1–10 K/s). Flow stress behavior is then experimentally observed, and an Arrhenius model is used to predict the flow behavior. However, due to the fact that materials parameters and activation energy do not remain constant, the Arrhenius model has an unsatisfied prediction for the flow behavior. Considering the effects of temperatures, strain rates and cooling rates on constitutive behavior, a revised Arrhenius model is developed to describe the flow stress behavior. Findings The experimental results show that the flow stress increases by the increasing cooling rate, increasing strain state and decreasing temperature. In comparison to the experimental data, the revised Arrhenius model has an excellent prediction for as-quenched Al-Mg-Si alloy. Originality Value With the revised Arrhenius model, the flow behaviors at different quenching conditions can be obtained, which is an essential step to the residual stress prediction when the model is implemented in a finite element code, e.g. ABAQUS, in the future.

scholarly journals Constitutive Equations for Describing the Warm and Hot Deformation Behavior of 20Cr2Ni4A Alloy Steel

Metals ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1169
Author(s):  
Haoran Wang ◽  
Wei Wang ◽  
Ruixue Zhai ◽  
Rui Ma ◽  
Jun Zhao ◽  
...  
Keyword(s):  
Flow Stress ◽  
Alloy Steel ◽  
Relative Error ◽  
Constitutive Equations ◽  
Type Equation ◽  
Coefficient Of Determination ◽  
Original Strain ◽  
Compression Tests ◽  
Absolute Relative Error ◽  
Average Absolute Relative Error

Isothermal hot compression tests of 20Cr2Ni4A alloy steel were performed under temperatures of 973–1273 K and strain rates of 0.001–1 s−1. The behavior of the flow stress of 20Cr2Ni4A alloy steel at warm and hot temperatures is complicated because of the influence of the work hardening, the dynamic recovery, and the dynamic recrystallization. Four constitutive equations were used to predict the flow stress of 20Cr2Ni4A alloy steel, including the original strain-compensated Arrhenius-type (osA-type) equation, the new modified strain-compensated Arrhenius-type (msA-type) equation, the original Hensel–Spittel (oHS) equation and the modified Hensel–Spittel (mHS) equation. The msA-type and mHS are developed by revising the deformation temperatures, which can improve prediction accuracy. In addition, we propose a new method of solving the parameters by combining a linear search with multiple linear regression. The new solving method is used to establish the two modified constitutive equations instead of the traditional regression analysis. A comparison of the predicted values based on the four constitutive equations was performed via relative error, average absolute relative error (AARE) and the coefficient of determination (R2). These results show the msA-type and mHS equations are more accurate and efficient in terms of predicting the flow stress of the 20Cr2Ni4A steel at elevated temperature.

scholarly journals Modeling Hot Deformation of 5005 Aluminum Alloy through Locally Constrained Regression Models with Logarithmic Transformations

2021 ◽  
Vol 12 (1) ◽  
pp. 152
Author(s):  
Jeongho Cho ◽  
Shin-Hyung Song
Keyword(s):  
Neural Network ◽  
Aluminum Alloy ◽  
Flow Stress ◽  
Hot Deformation ◽  
Regression Models ◽  
Temperature Deformation ◽  
Constrained Regression ◽  
Stress Behavior ◽  
Flow Stress Behavior ◽  
The Neural Network

This study presents the adoption of locally constrained regression models (LCRMs) with logarithmic transformations in order to model the flow stress behavior of the high-temperature deformation of 5005 aluminum alloy. Hot tensile tests for 5005 aluminum alloy were conducted under the temperatures of 290 °C, 360 °C, 430 °C, and 500 °C, and the strain rates of 0.0003/s, 0.003/s, and 0.03/s. The flow stress behavior was analyzed based on variations in temperature and strain rate. The flow stress during the hot deformation was modeled using the traditional Arrhenius type constitutive equation and the neural network approach. Then, for improved prediction accuracy, the flow stress was modeled using LCRMs. The prediction accuracies of the models were compared by calculating the MAE (Maximum Absolute Error) and RMSE (Root-Mean-Squared Errors) values. The MAE and RMSE of the LCRMs were lower than the errors of the Arrhenius equation and the neural network model. The results show that LCRMs can be useful in modeling the flow stress of 5005 aluminum alloy, and that the developed model can accurately predict the flow stress.

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