Hybrid FE/ANN and LPR approach for the inverse identification of material parameters from cutting tests
| Autor: | A. Muñoz-Sánchez, Isabel M. González-Farias, X. Soldani, M. Henar Miguélez |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: |
Polynomial regression
Ingeniería Mecánica Engineering Optimization problem Artificial neural network business.industry Mechanical Engineering Cutting simulation Constitutive equation Experimental data Inverse Local polynomial regression Industrial and Manufacturing Engineering Finite element method Computer Science Applications Inverse technique Stress (mechanics) Control and Systems Engineering Artificial intelligence business Algorithm Software |
| Zdroj: | e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid instname |
| Popis: | 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. The authors acknowledge the financial support of this work to the Ministry of Science and Education of Spain (under project DPI2008-06746). Publicado |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|