Add to ORKGTensile tests in extended ranges of strain rate (10-3–1 s-1) and temperature (200-300°C) were performed on AZ31 magnesium alloy sheets in order to investigate their effect on the flow curve. The influence of fibre orientation was also taken into account. These data were used to build an artificial neural network model able to predict the flow curve. The validity of the model was proven by comparing predicted and experimental flow curves using the leave k-out method. It was observed that the artificial neural network was able to predict both the curve shape and stress levels as a function of the process parameters
The aim of the present study was to investigate the modeling and prediction of the high temperature ...
AbstractAn artificial neural network (ANN) constitutive model and Johnson–Cook (J–C) model were deve...
An artificial neural network (ANN) constitutive model and Johnson–Cook (J–C) model were developed fo...
Tensile tests in extended ranges of strain rate (10-3–1 s-1) and temperature (200-300°C) were perfor...
Multivariable empirical models based on artificial neural networks were developed in order to predic...
Multivariable empirical models based on artificial neural networks were developed in order to predic...
Multivariable empirical models based on artificial neural networks were developed in order to predic...
Multivariable empirical models based on artificial neural networks were developed in order to predic...
Multivariable empirical models based on artificial neural networks were developed in order to predic...
Multivariable empirical models based on artificial neural networks were developed in order to predic...
A multivariable empirical model, based on an artificial neural network (ANN), was developed to predi...
A multivariable empirical model, based on an artificial neural network (ANN), was developed to predi...
A multivariable empirical model, based on an artificial neural network (ANN), was developed to predi...
A multivariable empirical model, based on an artificial neural network (ANN), was developed to predi...
A multivariable empirical model, based on an artificial neural network (ANN), was developed to predi...
The aim of the present study was to investigate the modeling and prediction of the high temperature ...
AbstractAn artificial neural network (ANN) constitutive model and Johnson–Cook (J–C) model were deve...
An artificial neural network (ANN) constitutive model and Johnson–Cook (J–C) model were developed fo...
Tensile tests in extended ranges of strain rate (10-3–1 s-1) and temperature (200-300°C) were perfor...
Multivariable empirical models based on artificial neural networks were developed in order to predic...
Multivariable empirical models based on artificial neural networks were developed in order to predic...
Multivariable empirical models based on artificial neural networks were developed in order to predic...
Multivariable empirical models based on artificial neural networks were developed in order to predic...
Multivariable empirical models based on artificial neural networks were developed in order to predic...
Multivariable empirical models based on artificial neural networks were developed in order to predic...
A multivariable empirical model, based on an artificial neural network (ANN), was developed to predi...
A multivariable empirical model, based on an artificial neural network (ANN), was developed to predi...
A multivariable empirical model, based on an artificial neural network (ANN), was developed to predi...
A multivariable empirical model, based on an artificial neural network (ANN), was developed to predi...
A multivariable empirical model, based on an artificial neural network (ANN), was developed to predi...
The aim of the present study was to investigate the modeling and prediction of the high temperature ...
AbstractAn artificial neural network (ANN) constitutive model and Johnson–Cook (J–C) model were deve...
An artificial neural network (ANN) constitutive model and Johnson–Cook (J–C) model were developed fo...