This paper gathers the results concerning the identification of the coefficients of the flow law of a Ti6-Al-4V for a flow law defined by a neural network as described and defined by Pantalé et al. This ANN allows the calculation of the flow stress as a function of the plastic strain, the plastic strain rate and the temperature respectively. The method for defining the ANN flow law is not described in this paper, only the identification of the coefficients for a Ti6-Al-4V is part of this paper
Machine learning techniques are increasingly used to predict material behavior in scientific applica...
Introduction Finite element modeling of manufacturing processes has been gaining wider acceptance ov...
AbstractIn this study, Artificial Neural Network (ANN) approach to predict the stress-strain curve o...
In the present study, artificial neural networks (ANNs) were used to model flow stress in Ti-6Al-4V ...
The flow behavior of CMn (Nb-Ti-V) micro alloyed steel was studied by hot compression tests in a wid...
The flow behavior of CMn (Nb-Ti-V) micro alloyed steel was studied by hot compression tests in a wid...
An artificial neural network (ANN) constitutive model and Johnson–Cook (J–C) model were developed fo...
AbstractAn artificial neural network (ANN) constitutive model and Johnson–Cook (J–C) model were deve...
The behavior of the flow stress of Al–Cu–Mg–Ag heat-resistant aluminum alloys during hot compression...
An artificial neural network (ANN) is used to model nonlinear, large deformation plastic behavior of...
The behavior of the flow stress of Al–Cu–Mg–Ag heat-resistant aluminum alloys during hot compression...
Machine learning techniques are increasingly used to predict material behavior in scientific applica...
The behavior of the flow stress of Al–Cu–Mg–Ag heat-resistant aluminum alloys during hot compression...
The application of accurate constitutive relationship in finite element simulation would significant...
The application of accurate constitutive relationship in finite element simulation would significant...
Machine learning techniques are increasingly used to predict material behavior in scientific applica...
Introduction Finite element modeling of manufacturing processes has been gaining wider acceptance ov...
AbstractIn this study, Artificial Neural Network (ANN) approach to predict the stress-strain curve o...
In the present study, artificial neural networks (ANNs) were used to model flow stress in Ti-6Al-4V ...
The flow behavior of CMn (Nb-Ti-V) micro alloyed steel was studied by hot compression tests in a wid...
The flow behavior of CMn (Nb-Ti-V) micro alloyed steel was studied by hot compression tests in a wid...
An artificial neural network (ANN) constitutive model and Johnson–Cook (J–C) model were developed fo...
AbstractAn artificial neural network (ANN) constitutive model and Johnson–Cook (J–C) model were deve...
The behavior of the flow stress of Al–Cu–Mg–Ag heat-resistant aluminum alloys during hot compression...
An artificial neural network (ANN) is used to model nonlinear, large deformation plastic behavior of...
The behavior of the flow stress of Al–Cu–Mg–Ag heat-resistant aluminum alloys during hot compression...
Machine learning techniques are increasingly used to predict material behavior in scientific applica...
The behavior of the flow stress of Al–Cu–Mg–Ag heat-resistant aluminum alloys during hot compression...
The application of accurate constitutive relationship in finite element simulation would significant...
The application of accurate constitutive relationship in finite element simulation would significant...
Machine learning techniques are increasingly used to predict material behavior in scientific applica...
Introduction Finite element modeling of manufacturing processes has been gaining wider acceptance ov...
AbstractIn this study, Artificial Neural Network (ANN) approach to predict the stress-strain curve o...