Identification of inelastic parameters based on deep drawing forming operations using a global–local hybrid Particle Swarm approach

Miguel Vaz; Marco A. Luersen; Pablo A. Muñoz-Rojas; Robson G. Trentin

doi:10.1016/j.crme.2015.07.015

Identification of inelastic parameters based on deep drawing forming operations using a global–local hybrid Particle Swarm approach

Miguel Vaz ¹ ; Marco A. Luersen ² ; Pablo A. Muñoz-Rojas ¹ ; Robson G. Trentin ³

¹ Department of Mechanical Engineering, State University of Santa Catarina, Campus Universitario, 89219-710 Joinville, Brazil
² Department of Mechanical Engineering, Federal University of Technology – Parana, Av. Sete de Setembro, 80230-901 Curitiba, Brazil
³ Department of Mechanical Engineering, Federal University of Technology – Parana, Via do Conhecimento, 85503-390 Pato Branco, Brazil

Comptes Rendus. Mécanique, Volume 344 (2016) no. 4-5, pp. 319-334.

Résumé

Application of optimization techniques to the identification of inelastic material parameters has substantially increased in recent years. The complex stress–strain paths and high nonlinearity, typical of this class of problems, require the development of robust and efficient techniques for inverse problems able to account for an irregular topography of the fitness surface. Within this framework, this work investigates the application of the gradient-based Sequential Quadratic Programming method, of the Nelder–Mead downhill simplex algorithm, of Particle Swarm Optimization (PSO), and of a global–local PSO–Nelder–Mead hybrid scheme to the identification of inelastic parameters based on a deep drawing operation. The hybrid technique has shown to be the best strategy by combining the good PSO performance to approach the global minimum basin of attraction with the efficiency demonstrated by the Nelder–Mead algorithm to obtain the minimum itself.

Reçu le : 2015-06-08
Accepté le : 2015-07-15
Publié le : 2016-02-23

DOI : 10.1016/j.crme.2015.07.015

Mots clés : Deep drawing, Parameter identification, SQP, PSO, Nelder–Mead

Affiliations des auteurs :

Miguel Vaz ¹ ; Marco A. Luersen ² ; Pablo A. Muñoz-Rojas ¹ ; Robson G. Trentin ³

¹ Department of Mechanical Engineering, State University of Santa Catarina, Campus Universitario, 89219-710 Joinville, Brazil
² Department of Mechanical Engineering, Federal University of Technology – Parana, Av. Sete de Setembro, 80230-901 Curitiba, Brazil
³ Department of Mechanical Engineering, Federal University of Technology – Parana, Via do Conhecimento, 85503-390 Pato Branco, Brazil

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     author = {Miguel Vaz and Marco A. Luersen and Pablo A. Mu\~noz-Rojas and Robson G. Trentin},
     title = {Identification of inelastic parameters based on deep drawing forming operations using a global{\textendash}local hybrid {Particle} {Swarm} approach},
     journal = {Comptes Rendus. M\'ecanique},
     pages = {319--334},
     publisher = {Elsevier},
     volume = {344},
     number = {4-5},
     year = {2016},
     doi = {10.1016/j.crme.2015.07.015},
     language = {en},
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Miguel Vaz; Marco A. Luersen; Pablo A. Muñoz-Rojas; Robson G. Trentin. Identification of inelastic parameters based on deep drawing forming operations using a global–local hybrid Particle Swarm approach. Comptes Rendus. Mécanique, Volume 344 (2016) no. 4-5, pp. 319-334. doi : 10.1016/j.crme.2015.07.015. https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.1016/j.crme.2015.07.015/

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