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.

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.

Application of ANN Back-Propagation for Fracture Design Parameters in Extra-Low Cycle Rotating Bending Fatigue

2009 ◽  
Vol 610-613 ◽  
pp. 450-453
Author(s):  
Hong Yan Duan ◽  
You Tang Li ◽  
Jin Zhang ◽  
Gui Ping He
Keyword(s):  
Neural Network ◽  
Neural Network Model ◽  
Back Propagation ◽  
Training Data ◽  
Design Parameters ◽  
Ann Model ◽  
Bending Fatigue ◽  
The Neural Network ◽  
Rotating Bending Fatigue ◽  
Rotating Bending

The fracture problems of ecomaterial (aluminum alloyed cast iron) under extra-low cycle rotating bending fatigue loading were studied using artificial neural networks (ANN) in this paper. The training data were used in the formation of training set of ANN. The ANN model exhibited excellent in results comparison with the experimental results. It was concluded that predicted fracture design parameters by the trained neural network model seem more reasonable compared to approximate methods. It is possible to claim that, ANN is fairly promising prediction technique if properly used. Training ANN model was introduced at first. And then the Training data for the development of the neural network model was obtained from the experiments. The input parameters, notch depth, the presetting deflection and tip radius of the notch, and the output parameters, the cycle times of fracture were used during the network training. The neural network architecture is designed. The ANN model was developed using back propagation architecture with three layers jump connections, where every layer was connected or linked to every previous layer. The number of hidden neurons was determined according to special formula. The performance of system is summarized at last. In order to facilitate the comparisons of predicted values, the error evaluation and mean relative error are obtained. The result show that the training model has good performance, and the experimental data and predicted data from ANN are in good coherence.

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.

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.

Modelling Dynamic Recovery and Recrystallization of Metals by a New Non-Equilibrium Thermodynamics Approach

2007 ◽  
Vol 558-559 ◽  
pp. 517-522
Author(s):  
Ming Xin Huang ◽  
Pedro E.J. Rivera-Díaz-del-Castillo ◽  
Sybrand van der Zwaag
Keyword(s):  
Steady State ◽  
High Temperature ◽  
Flow Stress ◽  
Dislocation Density ◽  
Dynamic Recovery ◽  
High Temperature Deformation ◽  
Temperature Deformation ◽  
Equilibrium Thermodynamics ◽  
Burgers Vector ◽  
Non Equilibrium

A non-equilibrium thermodynamics-based approach is proposed to predict the dislocation density and flow stress at the steady state of high temperature deformation. For a material undergoing dynamic recovery and recrystallization, it is found that the total dislocation density can be expressed as ( )2 ρ = λε& b , where ε& is the strain rate, b is the magnitude of the Burgers vector and λ is a dynamic recovery and recrystallization related parameter.

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