This repository contains some Python programs for Artificial Neural Network applicated to Constitutive Laws. This work is related to the following paper:
Efficient Implementation of Non-linear Flow Law Using Neural Network into the Abaqus Explicit FEM code
Abstract: Machine learning techniques are increasingly used to predict material behavior in scientific applications and offer a significant advantage over conventional numerical methods. In this work, an Artificial Neural Network (ANN) model is used in a finite element formulation to define the flow law of a metallic material as a function of plastic strain
This repository contains the following directories:
- ANN-Johnson-Cook : Identification of a Johnson-Cook flow law using the ANN
- ANN-Zhou-Law : A constitutive law data taken from the literature
- Abaqus : Testing of the VUHARD subroutines on the Abaqus FEM code
ANN-Learning.ipynb : main program used to train the Neural Network
PythonToFortran-3layers.ipynb : Program to convert an ANN to a Fortran VUHARD subroutine
Olivier Pantalé
Full Professor of Mechanics
email : [email protected]
Laboratoire Génie de Production
Ecole Nationale d'Ingénieurs de Tarbes
Université de Toulouse
47 Avenue d'Azereix - BP 1629
65016 TARBES - CEDEX - FRANCE