Numerical-Experimental Updating Identification of Elastic Behavior of a Composite Plate Using New Multi-Objective Optimization Procedure


This work focuses on the updating-based identification of the three-dimensional orthotropic elastic behavior of a thin carbon fiber reinforced plastic multilayer composite plate. This consists in identifying the engineering constants that minimize the relative deviations between the first eight experimental and three-dimensional finite element frequencies of the vibrating free plate. For this purpose, a multi-objective optimization procedure is applied; it exploits a Particle Swarm Optimizer algorithm (PSO) that is coupled to a metamodeling by the new response surfaces method procedure (NRSMP); the latter is based on numerical design experiments. The conducted sensitivity analyses indicate that the four engineering constants of the two-dimensional elasticity are the most influent.

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Ghanmi, S. , Bouajila, S. and Guedri, M. (2013) Numerical-Experimental Updating Identification of Elastic Behavior of a Composite Plate Using New Multi-Objective Optimization Procedure. Journal of Surface Engineered Materials and Advanced Technology, 3, 13-20. doi: 10.4236/jsemat.2013.31003.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] J. Cugnoni, “Identification par Recalage Modal et Fréquentiel des Propriétés Constitutives de Coques en Matériaux Composites,” Thèse de Doctorat, école Polytechnique Fédérale de Lausanne, 2005.
[2] M. Hamdi, S. Ghanmi, A. Benjeddou and R. Nasri, “Identification par Optimisation Multi-Objectif du Comportement Elastique Tridimensionnel d’Un Composite Multicouche,” Premier Colloque International IMPACT 2010, 2010, Djerba, Tunisie.
[3] M. Clerc and P. Siarry, Une Nouvelle Méta Heuristique pour l’Optimisation Difficile: La Méthode des Essaims Particulaires,” Vol. 3-7, 2004.
[4] J. Kennedy and R. C. Eberhart, “Particle Swarm Optimization,” Proceedings of the IEEE International Conference on Neural Networks, Perth, 1995, Vol. 4, pp. 1942-1948. doi:10.1109/ICNN.1995.488968
[5] J. F. Schutte, B. Koh, J. A. Reinbolt, R. T. Haftka, A. D. George and B. J. Fregly, “Evaluation of a Particle Swarm Algorithm for Biomechanical Optimization,” Journal of Biomechanical Engineering, Vol. 127, No. 3, 2005, pp. 465-474.
[6] J. J. Droesbeke, J. Fine and G. Saporta, “Plans d’Expériences: Applications à l’Entreprise,” Technip, Paris, 1997.
[7] R. H. Myers and D. C. Montgomery, “Response Surface Methodology,” Wiley, New York, 2002.
[8] G. Chevalier and A. Benjeddou, “Couplages Electro-mécaniques Effectifs dans les Structures Composites Piézoélectriques: Caractérisations Expérimentale et Numérique,” Rev. Compos. Matér. Av., Vol. 19. 2009.
[9] H. Friedmann, “Non-Destructive Parameter Estimation,” In CASSEM D9-Test Report on Experimental Validation for Test/Model Correlation,” March 2008, 5-13.
[10] G. J. Battaglia and J. M. Maynard, “Mean Square Error: A Useful Tool for Statistical Process Management,” AMP J. Tech., Vol. 34, 1996, pp. 1256-1260.
[11] A. A. Groenwold and P. C. Fourie, “The Particle Swarm Optimization in Size and Shape Optimization,” Structural and Multidisciplinary Optimization, Vol. 23, 2002, pp. 259-267. doi:10.1007/s00158-002-0188-0
[12] T. Cura, “Particle Swarm Optimization Approach to Portfolio Optimization,” Nonlinear Analysis: Real World Applications, Vol. 10, No. 4, 2009, pp. 2394-2406.

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