An isothermal compressive experiment using Gleeble 1500 thermal simulator was studied to acquire flow stress at different deformation temperatures, strains and strain rates. The artificial neural networks with the err...An isothermal compressive experiment using Gleeble 1500 thermal simulator was studied to acquire flow stress at different deformation temperatures, strains and strain rates. The artificial neural networks with the error back propagation(BP) algorithm was used to establish constitutive model of 2519 aluminum alloy based on the experiment data. The model results show that the systematical error is small(δ=3.3%) when the value of objective function is 0.2, the number of nodes in the hidden layer is 5 and the learning rate is 0.1. Flow stresses of the material under various thermodynamic conditions are predicted by the neural network model, and the predicted results correspond with the experimental results. A knowledge-based constitutive relation model is developed.展开更多
Viscosity is one of the important thermophysical properties of liquid aluminum alloys,which influences the characteristics of mold filling and solidification and thus the quality of castings.In this study,315 sets of ...Viscosity is one of the important thermophysical properties of liquid aluminum alloys,which influences the characteristics of mold filling and solidification and thus the quality of castings.In this study,315 sets of experimental viscosity data collected from the literatures were used to develop the viscosity prediction model.Back-propagation(BP)neural network method was adopted,with the melt temperature and mass contents of Al,Si,Fe,Cu,Mn,Mg and Zn solutes as the model input,and the viscosity value as the model output.To improve the model accuracy,the influence of different training algorithms and the number of hidden neurons was studied.The initial weight and bias values were also optimized using genetic algorithm,which considerably improve the model accuracy.The average relative error between the predicted and experimental data is less than 5%,confirming that the optimal model has high prediction accuracy and reliability.The predictions by our model for temperature-and solute content-dependent viscosity of pure Al and binary Al alloys are in very good agreement with the experimental results in the literature,indicating that the developed model has a good prediction accuracy.展开更多
The hot deformation behavior of 2A70 aluminum alloy was investigated by means of isothermal compression tests performed on a Gleeble-1500 thermal simulator over 360～480 ℃ with strain rates in the range of 0.01～1 s-...The hot deformation behavior of 2A70 aluminum alloy was investigated by means of isothermal compression tests performed on a Gleeble-1500 thermal simulator over 360～480 ℃ with strain rates in the range of 0.01～1 s-1 and the largest deformation up to 60%. On the basis of experiments, a BP artificial neural network （ANN） model was constructed to predict 2A70 aluminum alloy flow stress. True strain, strain rates and temperatures were input to the network, and flow stress was the only output. The comparison between predicted values and experimental data showed that the relative error for the trained model was less than ±3% for the sampled data while it was less than ±6% for the non-sampled data. Furthermore, the neural network model gives better results than nonlinear regression method. It is evident that the model constructed by BP ANN can be used to accurately predict the 2A70 alloy flow stress.展开更多
Rapid solidifiation is a kind of new process for enhancing the hardness and electrical conductivity of Cu-Cr-Zr copper alloy.The use of BP neural network(NN) is presented to model the non-linear relationship between p...Rapid solidifiation is a kind of new process for enhancing the hardness and electrical conductivity of Cu-Cr-Zr copper alloy.The use of BP neural network(NN) is presented to model the non-linear relationship between parameters of age hardening processes and the mechanical and electrical properties of rapdily solidified Cu-Cr-Zr alloy.The improved model is developed by the Levenberg-Marquardt training algorithm and the good generalization performance is demonstrated.So,an important foundation has been laid for optimisticaly controlling the rapidly solidified aging processes of Cu-Cr-Zr alloy.展开更多
The hat deformation behavior of 2A70 aluminum alloy was investigated by means of isothermal compression tests performed on a Gleeble-1500 thermal simulator over a wide range of temperatures 360-480℃ with strain rates...The hat deformation behavior of 2A70 aluminum alloy was investigated by means of isothermal compression tests performed on a Gleeble-1500 thermal simulator over a wide range of temperatures 360-480℃ with strain rates of 0.01-1s^-1 and the largest deformation of 60%, and the true stress of the material was obtained under the above-mentioned conditions. The experimental results shows that 2A70 aluminum alloy is a kind of aluminum alloy with the property of dynamic recovery; its flow stress declines with the increase of temperature, while its flow stress increases with the increase of strain rates. On the basis of experiments, the constitutive relationship of the 2A70 aluminum alloy was constructed using a BP artificial neural network. Comparison of the predicted values with the experimental data shows that the relative error of the trained model is less than ±3% for the sampled data while it is less than ±6% for the nonsampled data. It is evident that the model constructed by BP ANN can accurately predict the flow stress of the 2A70 alloy.展开更多
The model describing the dependence of the mechanical properties on the chemical composition and as deformation techniques of tungsten heavy alloy is established by the method of improved the backpropagation neural ne...The model describing the dependence of the mechanical properties on the chemical composition and as deformation techniques of tungsten heavy alloy is established by the method of improved the backpropagation neural network. The mechanical properties＇ parameters of tungsten alloy and deformation techniques for tungsten alloy are used as the inputs. The chemical composition and deformation amount of tungsten alloy are used as the outputs. Then they are used for training the neural network. At the same time, the optimal number of the hidden neurons is obtained through the experiential equations, and the varied step learning method is adopted to ensure the stability of the training process. According to the requirements for mechanical properties, the chemical composition and the deformation condition for tungsten heavy alloy can be designed by this artificial neural network system.展开更多
文摘An isothermal compressive experiment using Gleeble 1500 thermal simulator was studied to acquire flow stress at different deformation temperatures, strains and strain rates. The artificial neural networks with the error back propagation(BP) algorithm was used to establish constitutive model of 2519 aluminum alloy based on the experiment data. The model results show that the systematical error is small(δ=3.3%) when the value of objective function is 0.2, the number of nodes in the hidden layer is 5 and the learning rate is 0.1. Flow stresses of the material under various thermodynamic conditions are predicted by the neural network model, and the predicted results correspond with the experimental results. A knowledge-based constitutive relation model is developed.
基金the GM Research Foundation,China(No.GAC2094)Jiangsu Key Laboratory of Advanced Metallic Materials,China(No.BM2007204)the Fundamental Research Funds for the Central Universities,China(No.2242016K40011)。
文摘Viscosity is one of the important thermophysical properties of liquid aluminum alloys,which influences the characteristics of mold filling and solidification and thus the quality of castings.In this study,315 sets of experimental viscosity data collected from the literatures were used to develop the viscosity prediction model.Back-propagation(BP)neural network method was adopted,with the melt temperature and mass contents of Al,Si,Fe,Cu,Mn,Mg and Zn solutes as the model input,and the viscosity value as the model output.To improve the model accuracy,the influence of different training algorithms and the number of hidden neurons was studied.The initial weight and bias values were also optimized using genetic algorithm,which considerably improve the model accuracy.The average relative error between the predicted and experimental data is less than 5%,confirming that the optimal model has high prediction accuracy and reliability.The predictions by our model for temperature-and solute content-dependent viscosity of pure Al and binary Al alloys are in very good agreement with the experimental results in the literature,indicating that the developed model has a good prediction accuracy.
文摘The hot deformation behavior of 2A70 aluminum alloy was investigated by means of isothermal compression tests performed on a Gleeble-1500 thermal simulator over 360～480 ℃ with strain rates in the range of 0.01～1 s-1 and the largest deformation up to 60%. On the basis of experiments, a BP artificial neural network （ANN） model was constructed to predict 2A70 aluminum alloy flow stress. True strain, strain rates and temperatures were input to the network, and flow stress was the only output. The comparison between predicted values and experimental data showed that the relative error for the trained model was less than ±3% for the sampled data while it was less than ±6% for the non-sampled data. Furthermore, the neural network model gives better results than nonlinear regression method. It is evident that the model constructed by BP ANN can be used to accurately predict the 2A70 alloy flow stress.
文摘Rapid solidifiation is a kind of new process for enhancing the hardness and electrical conductivity of Cu-Cr-Zr copper alloy.The use of BP neural network(NN) is presented to model the non-linear relationship between parameters of age hardening processes and the mechanical and electrical properties of rapdily solidified Cu-Cr-Zr alloy.The improved model is developed by the Levenberg-Marquardt training algorithm and the good generalization performance is demonstrated.So,an important foundation has been laid for optimisticaly controlling the rapidly solidified aging processes of Cu-Cr-Zr alloy.
文摘The hat deformation behavior of 2A70 aluminum alloy was investigated by means of isothermal compression tests performed on a Gleeble-1500 thermal simulator over a wide range of temperatures 360-480℃ with strain rates of 0.01-1s^-1 and the largest deformation of 60%, and the true stress of the material was obtained under the above-mentioned conditions. The experimental results shows that 2A70 aluminum alloy is a kind of aluminum alloy with the property of dynamic recovery; its flow stress declines with the increase of temperature, while its flow stress increases with the increase of strain rates. On the basis of experiments, the constitutive relationship of the 2A70 aluminum alloy was constructed using a BP artificial neural network. Comparison of the predicted values with the experimental data shows that the relative error of the trained model is less than ±3% for the sampled data while it is less than ±6% for the nonsampled data. It is evident that the model constructed by BP ANN can accurately predict the flow stress of the 2A70 alloy.
文摘The model describing the dependence of the mechanical properties on the chemical composition and as deformation techniques of tungsten heavy alloy is established by the method of improved the backpropagation neural network. The mechanical properties＇ parameters of tungsten alloy and deformation techniques for tungsten alloy are used as the inputs. The chemical composition and deformation amount of tungsten alloy are used as the outputs. Then they are used for training the neural network. At the same time, the optimal number of the hidden neurons is obtained through the experiential equations, and the varied step learning method is adopted to ensure the stability of the training process. According to the requirements for mechanical properties, the chemical composition and the deformation condition for tungsten heavy alloy can be designed by this artificial neural network system.
