The Material Parameter Identification for Functionally Graded Materials by the Isoparametric Graded Finite Element

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Abstract:

A material parameter identification method is proposed for functionally graded materials (FGMs) which are modeled by the isoparametric graded finite elements (IGFE). The material parameter identification problem is formulated as the problem of minimizing the objective function defined as a square sum of differences between the measured displacement and the computed displacement by the IGFE. Levenberg-Marquardt optimization method, in which the sensitivity analysis of displacements with respect to the material parameters is based on the finite difference approximation method, is used to solve the minimization problem. Numerical example is given to illustrate the validity of the proposed method for parameter identification.

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Advanced Materials Research (Volumes 446-449)

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3609-3614

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January 2012

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] A. E. Giannakopoulos and S. Suresh: Int. J. Solids Structures Vol. 34 (1997), pp.2357-2392

Google Scholar

[2] A. E. Giannakopoulos and S. Suresh: Int. J. Solids Structures Vol. 34 (1997), pp.2393-2428

Google Scholar

[3] S. Suresh, A. E. Giannakopoulos and J. Alcala: Acta Mater. Vol. 45 (1997), pp.1307-1321

Google Scholar

[4] T. Nakamura, T. Wang and S. Sampath: Acta Mater. Vol. 48 (2000), pp.4293-4306

Google Scholar

[5] Y. Gua, T. Nakamura, L. Prchlik, S. Sampath and J. Wallace: Mater. Sci. and Eng. A Vol. 345 (2003), pp.223-233

Google Scholar

[6] M. Bocciarelli, G. Bolzon and G. Maier: Comput. Mater. Sci. Vol. 43 (2008), p.16–26

Google Scholar

[7] G.R. Liu, X. Han and K.Y. Lam: Compos.: Part B Vol. 30 (1999), p.383–394

Google Scholar

[8] G.R. Liu, X. Han, Y.G. Xu and K.Y. Lam: Compos. Sci. and Tech. Vol. 61 (2001), p.1401–1411

Google Scholar

[9] X. Han, G. R. Liu, K. Y. Lama and T. Ohyoshi: J. Sound Vib. Vol. 236 (2000), pp.307-321

Google Scholar

[10] X. Han, D. Xua and G.R. Liu: Neurocomputing Vol. 51 (2003), pp.341-360

Google Scholar

[11] L. X. Huang, Q. Yao, L. Wang and X. J. Zhou: Advanced Materials Research Vols. 243-249 (2011), pp.6011-6017

Google Scholar

[12] Y.L. Kang, X.H. Lin, and Q.H. Qin: Compos. Struct. Vol. 66 (2004), pp.449-458

Google Scholar

[13] J. H. Kim, G. H. Paulino: J. Appl. Mech. Vol. 69 (2002), pp.502-514

Google Scholar

[14] J. E. Dennis, Jr., R. B. Schnabel: Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Prentice-Hall, Inc., Englewood Cliffs, New Jersey 1983).

Google Scholar