Hybrid FE/ANN and LPR approach for the inverse identification of material parameters from cutting tests
Fecha
2010-09-14Estado
info:eu-repo/semantics/publishedVersionMetadatos
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. Accuracy of numerical models based in finite
elements (FE), extensively used for simulation of cutting
processes, depends strongly on the identification of proper
material parameters. Experimental identification of the
constitutive law parameters for simulation of cutting
processes involves unsolved problems such as the complex
testing techniques or the difficulty to reproduce the stress
triaxiality state during cutting. This work proposes a
methodology for the inverse identification of the material
parameters from cutting test. Two hybrid approaches are
compared. One of them based on FE and artificial neural
networks (ANN). The other one based on FE and local
polynomial regression (LPR). Firstly, a FE model is
validated with experimental data. Then, ANN and LPR
are trained with FE simulations. Finally, the estimated ANN
and LPR models are used for the inverse identification of
material parameters. This identification is solved as an
optimization problem. The FE/LPR approach shows good
performance, outperforming the FE/ANN approach.
Hybrid FE/ANN and LPR approach for the inverse identification of material parameters from cutting tests
Tipo de Actividad
Artículos en revistasISSN
0268-3768Palabras Clave
.Inverse technique, Cutting simulation , FE , ANN , Local polynomial regression