On the identification of material constitutive model parameters using machine learning algorithms

Abstract

This work aims to evaluate the predictive performance of various Machine Learning algorithms when applied to the prediction of material constitutive parameters, particularly the parameters of the Swift hardening law. For this, datasets were generated from the results of the numerical simulations of uniaxial tensile tests. The Machine Learning algorithms considered for this study are: Gaussian Process, Multi-layer Perceptron, Support Vector Regression, Decision Tree and Random Forest. These algorithms were used to train metamodels based on training sets considering different numbers of materials and input parameters, which were then used to predict the hardening law parameters. The Gaussian Process algorithm achieved the overall best predictive performances. The results obtained show the potential of Machine Learning algorithms for application on the identification of material constitutive parameters.