Using Genetic Algorithms to Optimize the Material Behaviour Model in Finite Element Models of Processes with Cyclic Loads

Abstract

To ensure realistic results in modelling processes for analysing strains in material using finite element (FE) models, it is essential to have a model of the material that is as close to reality as possible, especially when materials are subject to cyclic loads, because the gap in behaviour between actual materials and simulated models widens as the number of cycles increases owing to the Bauschinger effect, ratchetting, and other effects. This paper sets out a fully automated method for determining the most appropriate material behaviour model (linear or non-linear) for use in numerical simulation programs and the optimum constitutive parameters that define that model, on the basis of experimental data and the combined use of genetic algorithms (GAs) and finite elements (FEs). As a practical example, the method is applied to determine the optimum material model for ZSTE 800 high-strength steel with a view to simulating its behaviour in a cyclic stress—compression process with controlled strain and a variable number of cycles.