scholarly journals Prediction of Flow Stress of Annealed 7075 Al Alloy in Hot Deformation Using Strain-Compensated Arrhenius and Neural Network Models

Materials ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 5986
Author(s):  
Hongbin Yang ◽  
Hengyong Bu ◽  
Mengnie Li ◽  
Xin Lu
Keyword(s):  
Neural Network ◽  
Flow Stress ◽  
High Temperature Deformation ◽  
Temperature Deformation ◽  
Al Alloy ◽  
Ann Model ◽  
Neural Network Models ◽  
Prediction Ability ◽  
7075 Al Alloy ◽  
Average Absolute Relative Error

Hot compression experiments of annealed 7075 Al alloy were performed on TA DIL805D at different temperatures (733, 693, 653, 613 and 573 K) with different strain rates (1.0, 0.1, 0.01 and 0.001 s−1.) Based on experimental data, the strain-compensated Arrhenius model (SCAM) and the back-propagation artificial neural network model (BP-ANN) were constructed for the prediction of the flow stress. The predictive power of the two models was estimated by residual analysis, correlation coefficient (R) and average absolute relative error (AARE). The results reveal that the deformation parameters including strain, strain rate, and temperature have a significant effect on the flow stress of the alloy. Compared with the SCAM model, the flow stress predicted by the BP-ANN model is in better agreement with experimental values. For the BP-ANN model, the maximum residual is only 1 MPa, while it is as high as 8 MPa for the SCAM model. The R and AARE for the SCAM model are 0.9967 and 3.26%, while their values for the BP-ANN model are 0.99998 and 0.18%, respectively. All these reflect that the BP-ANN model has more accurate prediction ability than the SCAM model, which can be applied to predict the flow stress of the alloy under high temperature deformation.

Flow Stress Prediction of High-Nb TiAl Alloys under High Temperature Deformation

2012 ◽  
Vol 510 ◽  
pp. 723-728 ◽  
Author(s):  
Liang Cheng ◽  
Hui Chang ◽  
Bin Tang ◽  
Hong Chao Kou ◽  
Jin Shan Li
Keyword(s):  
Neural Network ◽  
High Temperature ◽  
Flow Stress ◽  
Back Propagation ◽  
Three Dimensional ◽  
High Temperature Deformation ◽  
Temperature Deformation ◽  
Ann Model ◽  
The Neural Network ◽  
Hidden Layer

In this work, a back propagation artificial neural network (BP-ANN) model is conducted to predict the flow behaviors of high-Nb TiAl (TNB) alloys during high temperature deformation. The inputs of the neural network are deformation temperature, log strain rate and strain whereas flow stress is the output. There is a single hidden layer with 7 neutrons in the network, and the weights and bias of the network were optimized by Genetic Algorithm (GA). The comparison result suggests a very good correlation between experimental and predicted data. Besides, the non-experimental flow stress predicted by the network is shown to be in good agreement with the results calculated by three dimensional interpolation, which confirmed a good generalization capability of the proposed network.

Back Propagation Artificial Neural Network Approach to Predict the Flow Stress in Isothermal Tensile Test of Medium Carbon Steel Material

2020 ◽  
Vol 977 ◽  
pp. 163-168
Author(s):  
Mohanraj Murugesan ◽  
Dong Won Jung
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Flow Stress ◽  
Carbon Steel ◽  
Back Propagation ◽  
Medium Carbon Steel ◽  
Ann Model ◽  
Medium Carbon ◽  
Average Absolute Relative Error ◽  
Steel Material

Isothermal tensile test of medium carbon steel material was conducted on a computer controlled servo-hydraulic testing machine at the deformation temperatures (923 to 1223 K) and the strain rates (0.05 to 1.0 s-1). 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 the model training and testing purpose, deformation temperature, strain rate and strain data were considered as inputs and in addition, the flow stress data were used a 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 (R2) and the average absolute relative error between the predicted flow stress and the experimental data were determined as 0.997 and 0.913%, respectively. In addition, by evaluating each test conditions, it was found that the average absolute relative error based on an ANN model varied from 0.55% to 1.36% and moreover, the results showed the better predictability compared with the measured data. Overall, the trained BP-ANN model is found to be much more efficient and accurate by means of flow stress prediction with respect to the experimental data for an entire tested conditions.

Hot Deformation Behavior of 2519 Al Alloy during Isothermal Compression

2007 ◽  
Vol 546-549 ◽  
pp. 749-754 ◽  
Author(s):  
Hui Zhong Li ◽  
Xin Ming Zhang ◽  
Min Gan Chen ◽  
Ying Liu ◽  
Hui Gao
Keyword(s):  
Aluminum Alloy ◽  
Flow Stress ◽  
Strain Rate ◽  
Deformation Behavior ◽  
Deformation Temperature ◽  
High Temperature Deformation ◽  
Temperature Deformation ◽  
Al Alloy ◽  
Strain Rates ◽  
Isothermal Compression

The deformation behavior of 2519 aluminum alloy was studied by isothermal compression by Gleeble-1500 simulator in the temperature range from 300 to 450°C under the strain rates of 0.01~10s-1. The results showed that the flow stress was controlled by strain rate and deformation temperature. The flow stress increased with strain rate and decreased with deformation temperature. The flow stress of 2519 aluminum alloy increased with strain and to the constant values at three strain rates of 0.01 s-1,0.1 s-1and1 s-1, indicating the dynamic recovery to occur. The flow stress decreased after a peak value with increase of strain at strain rate 10s-1 and deformation temperature higher than 350°C, showing partly dynamic recrystallization. The flow stress of 2519 aluminum alloy during high temperature deformation can be represented by Zener-Hollomon parameter.

Characterization of High Temperature Deformation Behavior of BFe10-1-2 Cupronickel Alloy Using Orthogonal Analysis

2017 ◽  
Vol 36 (7) ◽  
pp. 701-710
Author(s):  
Jun Cai ◽  
Kuaishe Wang ◽  
Xiaolu Zhang ◽  
Wen Wang
Keyword(s):  
High Temperature ◽  
Flow Stress ◽  
Constitutive Equation ◽  
Strain Rate ◽  
Deformation Behavior ◽  
High Temperature Deformation ◽  
Temperature Deformation ◽  
Compression Tests ◽  
Statistical Parameters ◽  
Orthogonal Analysis

AbstractHigh temperature deformation behavior of BFe10-1-2 cupronickel alloy was investigated by means of isothermal compression tests in the temperature range of 1,023~1,273 K and strain rate range of 0.001~10 s–1. Based on orthogonal experiment and variance analysis, the significance of the effects of strain, strain rate and deformation temperature on the flow stress was evaluated. Thereafter, a constitutive equation was developed on the basis of the orthogonal analysis conclusions. Subsequently, standard statistical parameters were introduced to verify the validity of developed constitutive equation. The results indicated that the predicted flow stress values from the constitutive equation could track the experimental data of BFe10-1-2 cupronickel alloy under most deformation conditions.

Constitutive Analysis of the High-Temperature Deformation Behavior of Single Phase α-Ti and α+β Ti-6Al-4V Alloy

2007 ◽  
Vol 539-543 ◽  
pp. 3607-3612 ◽  
Author(s):  
Jeoung Han Kim ◽  
Jong Taek Yeom ◽  
Nho Kwang Park ◽  
Chong Soo Lee
Keyword(s):  
High Temperature ◽  
Flow Stress ◽  
Deformation Behavior ◽  
Single Phase ◽  
Volume Fraction ◽  
High Temperature Deformation ◽  
Temperature Deformation ◽  
Β Phase ◽  
Α Phase ◽  
Self Consistent

The high-temperature deformation behavior of the single-phase α (Ti-7.0Al-1.5V) and α + β (Ti-6Al-4V) alloy were determined and compared within the framework of self-consistent scheme at various temperature ranges. For this purpose, isothermal hot compression tests were conducted at temperatures between 650°C ~ 950°C to determine the effect of α/β phase volume fraction on average flow stress under hot-working condition. The flow behavior of α phase was estimated from the compression test results of single-phase α alloy whose chemical composition is close to that of α phase of Ti-6Al-4V alloy. On the other hand, the flow stress of β phase in Ti-6Al-4V was predicted by using self-consistent method. The flow stress of α phase was higher than that of β phase above 750°C, while the β phase revealed higher flow stress than α phase at 650°C. Also, at temperature above 750°C, the predicted strain rate of β phase was higher than that of α phase. It was found that the relative strength between α and β phase significantly varied with temperature.

scholarly journals High-Temperature Flow Behaviour and Constitutive Equations for a TC17 Titanium Alloy

2019 ◽  
Vol 38 (2019) ◽  
pp. 168-177 ◽  
Author(s):  
Liu Shi-feng ◽  
Shi Jia-min ◽  
Yang Xiao-kang ◽  
Cai Jun ◽  
Wang Qing-juan
Keyword(s):  
Titanium Alloy ◽  
High Temperature ◽  
Constitutive Equation ◽  
Constitutive Equations ◽  
Deformation Behaviour ◽  
High Temperature Deformation ◽  
Temperature Deformation ◽  
Compression Tests ◽  
Average Absolute Relative Error ◽  
Wide Range

AbstractIn this study, the high-temperature deformation behaviour of a TC17 titanium alloy was investigated by isothermal hot compression tests in a wide range of temperatures (973–1223 K) and strain rates (0.001–10 s−1). Then, the constitutive equations of different phase regimes (α + β and single β phases) were developed on the basis of experimental stress-strain data. The influence of the strain has been incorporated in the constitutive equation by considering its effect on different material constants for the TC17 titanium alloy. Furthermore, the predictability of the developed constitutive equation was verified by the correlation coefficient and average absolute relative error. The results indicated that the obtained constitutive equations could predict the high-temperature flow stress of a TC17 titanium alloy with good correlation and generalization.

Development of Neural Network Models for a Crude Oil Distillation Column

2012 ◽  
Author(s):  
Khairiyah Mohd. Yusof ◽  
Fakhri Karray ◽  
Peter L. Douglas
Keyword(s):  
Neural Network ◽  
Steady State ◽  
Crude Oil ◽  
Distillation Column ◽  
Network Models ◽  
Ann Model ◽  
Neural Network Models ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Crude Oil Distillation

This paper discusses the development of artificial neural network (ANN) models for a crude oil distillation column. Since the model is developed for real time optimisation (RTO) applications they are steady state, multivariable models. Training and testing data used to develop the models were generated from a reconciled steady-state model simulated in a process simulator. The radial basis function networks (RBFN), a type of feedforward ANN model, were able to model the crude tower very well, with the root mean square error for the prediction of each variable less than 1%. Grouping related output variables in a network model was found to give better predictions than lumping all the variables in a single model; this also allowed the overall complex, multivariable model to be simplified into smaller models that are more manageable. In addition, the RBFN models were also able to satisfactorily perform range and dimensional extrapolation, which is necessary for models that are used in RTO.

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