Full Article - Open Access.

Idioma principal

ASPECTS OF IDENTIFICATION OF ELASTIC-PLASTIC PARAMETERS USING HEURISTIC ALGORITHMS

Jr, M. Vaz; Cardoso, E. L.;

Full Article:

The intense research in the development of new constitutive models has faced the challenge of devising strategies to determine the corresponding material parameters. The literature has shown a steady growth in application of parameter identification based on optimization techniques to a wide range of engineering problems. Within this framework, in recent years, parameter identification schemes using heuristic approaches have been proposed as possible alternatives to classical identification procedures mainly due to their potential ability to avoid local minima, insensitivity to the order of magnitude of parameters and easy parallelisation. The present work shows that Particle Swarm Optimization, as an example of such methods, can also be successfully applied to identification of inelastic parameters and presents remarkable characteristics when compared to Genetic Algorithms.

Full Article:

Palavras-chave: Parameter Identification, Particle Swarm Optimization, Genetic Algorithm.,

Palavras-chave:

DOI: 10.5151/meceng-wccm2012-16705

Referências bibliográficas
  • [1] Muñoz-Rojas, P.A., da Cunda, L.A.B., Cardoso, E.L., Vaz Jr., M. and Creus, G.J., “A mixed optimization approach for parameter identification applied to the Gurson damage model”, in Vaz Jr., M., de Souza Neto, E.A. and Muñoz-Rojas, P.A. (Eds.), Advanced Computational Materials Modeling: from Classical to Multi-scale Techniques, Wiley- VCH, pp.165-204, 201
  • [2] Abendroth, M., Kuna, M., “Identification of ductile damage and fracture parameters from the small punch test using neural networks”, Engineering Fracture Mechanics, 73, 710-725, 2006.
  • [3] Aguir, H., Belhadjsalah, H., Hambli, R., “Parameter identification of an elasto-plastic behaviour using artificial neural networks-genetic algorithm method”, Materials and Design, 32, 48-53, 2011.
  • [4] Chaparro, B.M., Thuillier, S., Menezes, L.F., Manach, P.Y., Fernandes, J.V., “Material parameters identification: gradient-based, genetic and hybrid optimization algorithms”, Computational Materials Science, 44, 339-346, 2008.
  • [5] Muñoz-Rojas, P.A., Cardoso, E.L., Vaz Jr., M., “Parameter identification of damage models using genetic algorithms”, Experimental Mechanics, 50, 627-634, 2010.
  • [6] Eberhart, R.C., Kennedy, J., “A new optimizer using particle swarm theory”, in: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, IEEE Press, 39-43, 1995.
  • [7] Kennedy, J., Eberhart, R.C., “Particle Swarm Optimization”, in: Proceedings of the IEEE International Conference on Neural Networks, IEEE Press, 1942-1948, 1995.
  • [8] Vaz Jr., M., Cardoso E.L., Stahlschmidt, J., “Particle Swarm Optimization and identification of inelastic material parameters”, Submitted for publication, 2011.
  • [9] de Souza Neto, E.A., Peric, D., Owen, D.R.J., Computational Methods for Plasticity. Theory and Applications, Wiley, 2008.
  • [10] Poli, R., Kennedy, J., Blackwell, T., “Particle swarm optimization - An overview”, Swarm Intelligence, 1, 33-57, 2007.
  • [11] Blum, C., Li, X., “Swarm intelligence in optimization”, in: Blum, C. and Merkle, D. (Eds.), Swarm Intelligence - Introduction and Applications, Springer, 43-85, 2008.
  • [12] Sedighizadeh, D., Masehian, E., “Particle swarm optimization methods, taxonomy and applications”, International Journal of Computer Theory and Engineering, 1, 1793- 8201, 2009.
  • [13] Ardakani, M.D., Khodadad, M., “Identification of thermal conductivity and the shape of an inclusion using the boundary elements method and the particle swarm optimization algorithm”, Inverse Problems in Science and Engineering, 17, 855-870, 2009.
  • [14] Cortes, O., Urquiza, G., Hernandez, J.A., “Inverse heat transfer using Levenberg- Marquardt and particle swarm optimization methods for heat source estimation”, Applied Mechanics and Materials, 15, 35-40, 2009.
  • [15] Tian, N., Sun, J., Xu, W, Lai, C.-H., “Quantum-behaved particle swarm optimization with ring topology and its application in estimating temperature-dependent thermal conductivity”, Numerical Heat Transfer, Part B, 60, 73-95, 2011.
  • [16] Schutte, J.F., Groenwold, A.A., “A study of global optimization using particle swarms”, Journal of Global Optimization, 31, 93-108, 2005.
  • [17] Clerc, M., Kennedy, J., “The particle swarm-explosion, stability, and convergence in a multidimensional complex space”, IEEE Transaction on Evolutionary Computation, 6, 58-73, 2002.
  • [18] Rao, S.S., Engineering Optimization. Theory and Practice, fourth ed., Wiley, 2009.
  • [19] Ponthot, J.-P., Kleinermann, J.-P., “A cascade optimization methodology for automatic parameter identification and shape/process optimization in metal forming simulation”, Computer Methods in Applied Mechanics and Engineering, 195, 5472-5508, 2006.
  • [20] Voce, E., “The relationship between stress and strain for homogeneous deformation”, Journal of Institute of Metals, 74, 537-562, 1948.
  • [21] Arora, J.S., Introduction to Optimum Design, second ed., Elsevier, 2004.
  • [22] Goldberg, D., Sastry, K., Genetic Algorithms: The Design of Innovation, Springer, 2011.
Como citar:

Jr, M. Vaz; Cardoso, E. L.; "ASPECTS OF IDENTIFICATION OF ELASTIC-PLASTIC PARAMETERS USING HEURISTIC ALGORITHMS", p. 239-253 . In: In Proceedings of the 10th World Congress on Computational Mechanics [= Blucher Mechanical Engineering Proceedings, v. 1, n. 1]. São Paulo: Blucher, 2014.
ISSN 2358-0828, DOI 10.5151/meceng-wccm2012-16705

últimos 30 dias | último ano | desde a publicação


downloads


visualizações


indexações