The Flow Stress Behavior and Constitutive Model of Cr8Mo2SiV Tool Steel during Hot Deformation

2020 ◽  
pp. 2000434
Author(s):  
Chang-Sheng Li ◽  
Shuai He ◽  
Jinyi Ren ◽  
Yahui Han
Keyword(s):  
Flow Stress ◽  
Constitutive Model ◽  
Tool Steel ◽  
Hot Deformation ◽  
Stress Behavior ◽  
Flow Stress Behavior

Predicting the flow stress behavior of Ni-42.5Ti-3Cu during hot deformation using constitutive equations

2013 ◽  
Vol 19 (1) ◽  
pp. 5-9 ◽  
Author(s):  
Amir Etaati ◽  
Kamran Dehghani ◽  
G. R. Ebrahimi ◽  
Hao Wang
Keyword(s):  
Flow Stress ◽  
Constitutive Equations ◽  
Hot Deformation ◽  
Stress Behavior ◽  
Flow Stress Behavior

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.

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.

Texture dependencies on flow stress behavior of magnesium alloy under dynamic compressive loading

Vacuum ◽  
2021 ◽  
pp. 110323
Author(s):  
Faisal Nazeer ◽  
Syed Zohaib Hassan Naqvi ◽  
Abul Kalam ◽  
A.G. Al-Sehemi ◽  
Hussein Alrobi
Keyword(s):  
Flow Stress ◽  
Magnesium Alloy ◽  
Compressive Loading ◽  
Stress Behavior ◽  
Flow Stress Behavior ◽  
Dynamic Compressive Loading
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