Identification of non-linear behavior models with restricted or redundant data
This work addresses parameter identification challenges in material science, but it is incremental as it combines existing methods.
The study tackled the problem of identifying material parameters for composite laminates with restricted or redundant data, achieving selective parameter identification through optimization of experimental tests on tubular samples.
This study presents a new strategy for the identification of material parameters in the case of restricted or redundant data, based on a hybrid approach combining a genetic algorithm and the Levenberg-Marquardt method. The proposed methodology consists essentially in a statistically based topological analysis of the search domain, after this one has been reduced by the analysis of the parameters ranges. This is used to identify the parameters of a model representing the behavior of damaged elastic, visco-elastic, plastic and visco-plastic composite laminates. Optimization of the experimental tests on tubular samples leads to the selective identification of these parameters.