CONICET | Buscador de Institutos y Recursos Humanos

Modelling the failure of engineering polymers incritical structural applications is still a challenging task that isincreasingly demanded in the industry to optimize part design and estimatecomponent service life. The difficulties include not only developingconstitutive models capable of reproducing the complex polymer response at a reasonablecomputational cost, but also calibrating related parameters. That is to say, away to find specific parameters which best represent the actual behavior of a materialwithin the scope and limitations of a given constitutive or failure model. Theaim of this study is to contribute in finding a robust inverse methodcalibration strategy. To address this issue, a novel approach based on geneticalgorithms optimization (GA) together with Finite Element Analysis (FEA) isproposed to blindly extract key constitutive and failure parameters frominstrumented impact tests on Single Edge Notched Bending (SENB) specimens. Themethod was implemented to infer eight constitutive and failure parameters of apolyamide 12 (PA12) with an elasto-plastic ductile damage model. Triaxialityinduced stable-unstable transition was successfully achieved by varying thenotch depth of SENB specimens. Accordingly, three optimization schemes wereconducted: i) using only unstable experimental data; ii) using only stableexperimental data and iii) using both simultaneously (multi-objective).The setof parameters obtained from each scheme were used to perform predictive FEAsimulations, which were varied with experimental data. It was proven that both propagationregimes provide substantial information to obtain the mechanical response ofthe material. Simulation results evidenced the capability of the proposedstrategy to predict the PA12 impact response and furthermore to fairly reproducea completely different load case: a dynamic tensile test.

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