Identification of material parameters from punch stretch test

Abstract To accurately describe the mechanical properties of aluminium alloy sheet during deformation, an inverse identification was presented to deal with material parameters from the popular punch stretch test. In the identification procedure, the optimization strategy combines finite element method (FEM), Latin hypercube sampling (LHS), Kriging model and multi-island genetic algorithm (MIGA). The proposed approach is used on material parameter identification of aluminium alloy sheet 2D12. The anisotropic yield criterion Hill'90 is discussed. The results show that the Hill'90 anisotropic yield criterion with identified anisotropic material parameters has a good potential in describing the anisotropic behaviours. It provides a way to obtain the material parameters for FE simulations of sheet metal forming.

[1]  Jean-Philippe Ponthot,et al.  Parameter identification and shape/process optimization in metal forming simulation , 2003 .

[2]  Jerome Sacks,et al.  Designs for Computer Experiments , 1989 .

[3]  Mihaela Banu,et al.  Towards an accurate description of the anisotropic behaviour of sheet metals under large plastic deformations: Modelling, numerical analysis and identification , 2006 .

[4]  Pierre Vacher,et al.  Heterogeneous tensile test on elastoplastic metallic sheets: Comparison between FEM simulations and full-field strain measurements , 2008 .

[5]  I. A. Burchitz,et al.  Numerical product design: Springback prediction, compensation and optimization , 2008 .

[6]  Luís Menezes,et al.  Optimization of the Phenomenological Constitutive Models Parameters Using Genetic Algorithms , 2007 .

[7]  Abdelwaheb Dogui,et al.  Anisotropic parameter identification using inhomogeneous tensile test , 2002 .

[8]  Dihui Liu.  IDENTIFICATION OF MATERIAL PARAMETERS OF BARLAT 1991 MODEL BY INVERSE METHOD , 2006 .

[9]  Jd Jan Janssen,et al.  Determination of the elasto-plastic properties of aluminium using a mixed numerical–experimental method , 1998 .

[10]  Hedi Belhadjsalah,et al.  Parameter identification of an elasto-plastic behaviour using artificial neural networks–genetic algorithm method , 2011 .

[11]  J. Gelin,et al.  Identification of material parameters directly from metal forming processes , 1998 .

[12]  Dimitri Debruyne,et al.  Elasto-plastic material parameter identification by inverse methods: Calculation of the sensitivity matrix , 2007 .

[13]  R. Hill.  Constitutive modelling of orthotropic plasticity in sheet metals , 1990 .

[14]  M. Martiny,et al.  Identification of sheet metal plastic anisotropy using heterogeneous biaxial tensile tests , 2010 .

[15]  H. Aguir,et al.  Identification of Constitutive Parameters using Hybrid ANN multi-objective optimization procedure , 2008 .

[16]  Xinwei Deng,et al.  Design for computer experiments with qualitative and quantitative factors , 2015 .

[17]  Jean-Claude Gelin,et al.  A finite element-based identification method for complex metallic material behaviours , 2001 .

[18]  Thomas J. Santner,et al.  The Design and Analysis of Computer Experiments , 2003, Springer Series in Statistics.

[19]  Lihui Lang,et al.  Optimized constitutive equation of material property based on inverse modeling for aluminum alloy hydroforming simulation , 2006 .

[20]  Zhong Zhi-hua.  IDENTIFICATION OF MATERIAL PARAMETERS OF BARLAT 1991 MODEL BY INVERSE METHOD , 2006 .

[21]  J. Ponthot,et al.  A cascade optimization methodology for automatic parameter identification and shape/process optimization in metal forming simulation , 2006 .

[22]  A. Leacock.  A mathematical description of orthotropy in sheet metals , 2006 .

[23]  J. Ding,et al.  A modified form of Hill's orientationdashdependent yield criterion for orthotropic sheet metals , 1996 .