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.
Accepted:
Published online:
Miguel Vaz 1; Marco A. Luersen 2; Pablo A. Muñoz-Rojas 1; Robson G. Trentin 3
@article{CRMECA_2016__344_4-5_319_0, 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}, }
TY - JOUR AU - Miguel Vaz AU - Marco A. Luersen AU - Pablo A. Muñoz-Rojas AU - Robson G. Trentin TI - Identification of inelastic parameters based on deep drawing forming operations using a global–local hybrid Particle Swarm approach JO - Comptes Rendus. Mécanique PY - 2016 SP - 319 EP - 334 VL - 344 IS - 4-5 PB - Elsevier DO - 10.1016/j.crme.2015.07.015 LA - en ID - CRMECA_2016__344_4-5_319_0 ER -
%0 Journal Article %A Miguel Vaz %A Marco A. Luersen %A Pablo A. Muñoz-Rojas %A Robson G. Trentin %T Identification of inelastic parameters based on deep drawing forming operations using a global–local hybrid Particle Swarm approach %J Comptes Rendus. Mécanique %D 2016 %P 319-334 %V 344 %N 4-5 %I Elsevier %R 10.1016/j.crme.2015.07.015 %G en %F CRMECA_2016__344_4-5_319_0
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/
[1] Computational Methods for Plasticity: Theory and Applications, Wiley, Chichester, UK, 2008
[2] Identification of material parameters of the Gurson–Tvergaard–Needleman model by combined experimental and numerical techniques, Comput. Mater. Sci., Volume 33 (2005) no. 4, pp. 501-509
[3] Identification of the model describing viscoplastic behaviour of high strength metals, Inverse Probl. Sci. Eng., Volume 19 (2011) no. 1, pp. 17-30
[4] A mixed optimization approach for parameter identification applied to the Gurson damage model (M. Vaz; E.A. de Souza Neto; P.A. Muñoz-Rojas, eds.), Advanced Computational Materials Modeling: From Classical to Multi-Scale Techniques, Wiley–VCH, Weinheim, Germany, 2011, pp. 165-204
[5] A cascade optimization methodology for automatic parameter identification and shape/process optimization in metal forming simulation, Comput. Methods Appl. Mech. Eng., Volume 195 (2006) no. 41–43, pp. 5472-5508
[6] An algorithm for incremental elastoplastic analysis using equality constrained sequential quadratic programming, Comput. Struct., Volume 102–103 (2012), pp. 97-107
[7] An efficient and practical approach to obtain a better optimum solution for structural optimization, Eng. Optim., Volume 45 (2013) no. 8, pp. 1005-1026
[8] NLPQLP: a new Fortran implementation of a sequential quadratic programming algorithm for parallel computing, University of Bayreuth, Bayreuth, Germany, 2001 (Technical report)
[9] Engineering Optimization. Theory and Practice, Wiley, Hoboken, NJ, USA, 2009
[10] A modified finite difference sensitivity analysis method allowing remeshing in large strain path-dependent problems, Int. J. Numer. Methods Eng., Volume 61 (2004) no. 7, pp. 1049-1071
[11] Identification paramétrique et optimisation des procédés de mise à forme par problèmes inverses, University of Liège, Liège, Belgium, 2000 (Ph.D. thesis)
[12] A simplex method for function minimization, Comput. J., Volume 7 (1965) no. 4, pp. 308-313
[13] Sequential application of simplex designs in optimisation and evolutionary operation, Technometrics, Volume 4 (1962) no. 4, pp. 441-461
[14] Convergence properties of the Nelder–Mead simplex method in low dimensions, SIAM J. Optim., Volume 9 (1998) no. 1, pp. 112-147
[15] Non-quadratic yield criterion for orthotropic sheet metals under plane-stress conditions, Int. J. Mech. Sci., Volume 45 (2003) no. 4, pp. 797-811
[16] Identification of elasto-plastic constitutive parameters from statically undetermined tests using the virtual fields method, Exp. Mech., Volume 46 (2006) no. 6, pp. 735-755
[17] Numerical solution to identification problems of material parameters in geotechnical engineering, Proc. Eng., Volume 28 (2012), pp. 61-65
[18] Continuum mechanical investigations of the intervertebral disc, ETH Zurich, Zurich, Switzerland, 2011 (Ph.D. thesis, Diss. ETH Nr. 19545)
[19] On convergence of the Nelder–Mead simplex algorithm for unconstrained stochastic optimization, The Pennsylvania State University, University Park, PA, USA, 1995 (Ph.D. thesis)
[20] Identification of constitutive parameters – optimization strategies and applications, Mat.-wiss. Werkstofftech., Volume 46 (2015) no. 4–5, pp. 477-491
[21] Globalized Nelder–Mead method for engineering optimization, Comput. Struct., Volume 82 (2004) no. 23–26, pp. 2251-2260
[22] A new optimizer using particle swarm theory, Proceedings of the 6th International Symposium on Micro Machine and Human Science, IEEE Press, Piscataway, NJ, USA, 1995, pp. 39-43
[23] Particle swarm optimization, Proceedings of the IEEE International Conference on Neural Networks, IEEE Press, Piscataway, NJ, USA, 1995, pp. 1942-1948
[24] Particle swarm optimization methods, taxonomy and applications, Int. J. Comput. Theory Eng., Volume 1 (2009) no. 5, pp. 486-502
[25] A study of global optimization using particle swarms, J. Glob. Optim., Volume 31 (2005) no. 1, pp. 93-108
[26] Swarm intelligence in optimization (C. Blum; D. Merkle, eds.), Swarm Intelligence – Introduction and Applications, Springer, Heidelberg, Germany, 2008, pp. 43-85
[27] Quantum-behaved particle swarm optimization with ring topology and its application in estimating temperature-dependent thermal conductivity, Numer. Heat Transf., Part B, Fundam., Volume 60 (2011) no. 2, pp. 73-95
[28] Particle swarm optimization-based algorithms for solving inverse heat conduction problems of estimating surface heat flux, Int. J. Heat Mass Transf., Volume 55 (2012) no. 7–8, pp. 2062-2068
[29] Low cost surrogate model based evolutionary optimization solvers for inverse heat conduction problem, Int. J. Heat Mass Transf., Volume 56 (2013) no. 1–2, pp. 263-273
[30] Hybrid charged system search and particle swarm optimization for engineering design problems, Eng. Comput., Volume 28 (2011) no. 4, pp. 423-440
[31] Modal parameter based inverse approach for structural joint damage assessment using unified particle swarm optimization, Appl. Math. Comput., Volume 242 (2014), pp. 407-422
[32] Particle swarm-based structural optimization of laminated composite hydrokinetic turbine blades, Eng. Optim., Volume 47 (2015) no. 9, pp. 1191-1207
[33] Identification of visco-elastic models for rocks using genetic programming coupled with the modified particle swarm optimization algorithm, Int. J. Rock Mech. Min. Sci., Volume 43 (2006) no. 5, pp. 789-801
[34] A viscoelastic viscoplastic constitutive model including mechanical degradation: uniaxial transient finite element formulation at finite strains and application to space truss structures, Appl. Math. Model., Volume 39 (2015) no. 5–6, pp. 1725-1739
[35] Performance of heuristic optimisation methods in the characterisation of the dynamic properties of sandwich composite materials, Int. J. Acoust. Vib., Volume 12 (2007) no. 1, pp. 60-68
[36] Functionally graded materials optimization using particle-swarm based algorithms, J. Therm. Stresses, Volume 35 (2012) no. 4, pp. 377-392
[37] Particle swarm optimization and identification of inelastic material parameters, Eng. Comput., Volume 30 (2013) no. 7, pp. 936-960
[38] Material parameters identification: gradient-based, genetic and hybrid optimization algorithms, Comput. Mater. Sci., Volume 44 (2008) no. 2, pp. 339-346
[39] Parameter identification of damage models using genetic algorithms, Exp. Mech., Volume 50 (2010) no. 5, pp. 627-634
[40] Optimization strategies for non-linear material parameters identification in metal forming problems, Comput. Struct., Volume 89 (2011) no. 1–2, pp. 246-255
[41] A Nelder–Mead PSO based approach to optimal capacitor placement in radial distribution system (B.K. Panigrahi; P.N. Suganthan; S. Das; S.C. Satapathy, eds.), Swarm, Evolutionary, and Memetic Computing, Lecture Notes in Computer Science, vol. 7076, Springer-Verlag, Heidelberg, Germany, 2011, pp. 143-150
[42] PSOLVER: a new hybrid particle swarm optimization algorithm for solving continuous optimization problems, Expert Syst. Appl., Volume 37 (2010) no. 10, pp. 6798-6808
[43] A hybrid Nelder–Mead simplex and PSO approach on economic and economic-statistical designs of MEWMA control charts, Int. J. Adv. Manuf. Technol., Volume 65 (2013) no. 9–12, pp. 1339-1348
[44] Considerations on parameter identification and material response for Gurson-type and Lemaitre-type constitutive models, Int. J. Mech. Sci., Volume 106 (2016), pp. 254-265
[45] A benchmark study on identification of inelastic parameters based on deep drawing processes using PSO Nelder Mead hybrid approach (E. Oñate; D.R.J. Owen; D. Peric; B. Suárez, eds.), Computational Plasticity XII – Fundamentals and Applications, CIMNE, Barcelona, Spain, 2013, pp. 153-163
[46] Sheet Metal Forming Process and Die Design, Industrial Press, New York, NY, USA, 2004
[47] Empirical study of particle swarm optimization, Proceedings of the IEEE Congress on Evolutionary Computation, IEEE Press, Piscataway, NJ, USA, 1999, pp. 1945-1950
[48] Dynamically tuning the population size in particle swarm optimization, Proceedings of the ACM Symposium on Applied Computing, ACM Press, New York, NY, USA, 2008, pp. 1782-1787
Cited by Sources:
Comments - Policy