Parameter identification for a low-density-foam material model using numerical optimisation procedures

Purpose – In the event of a crash involving a car, its seats, together with their backrests and head supports, ensure the safety of the passengers. The filling material used for such a car seat is normally made of polyurethane foam. To simulate the behaviour of the seat assembly during a crash, the material characteristics of the seat-filling foam should be appropriately modelled. The purpose of this paper is to present a method, with which the proper parameter values of the selected material model for the seat-filling foam can be easily determined. Design/methodology/approach – In the study, an experiment with the specimen from seat-filling foam was carried out. The results from this experiment were the basis for the determination of the parameter values of the low-density-foam material model, which is often used in crash-test simulations. Two different numerical optimisation algorithms – a genetic algorithm and a gradient-descent algorithm – were coupled with LS-DYNA explicit simulations to identify the...

[1]  S Forrest,et al.  Genetic algorithms , 1996, CSUR.

[2]  George Lindfield,et al.  Numerical Methods Using MATLAB , 1998 .

[3]  G. Belingardi,et al.  Characterization of polymeric structural foams under compressive impact loading by means of energy-absorption diagram , 2001 .

[4]  G. Piero,et al.  Strain localization in open-cell polyurethane foams: Experiments and theoretical model , 2008 .

[5]  S. Mizrahi,et al.  Mechanical properties and behavior of open cell foams used as cushioning materials , 1990 .

[6]  James A. Sherwood,et al.  Constitutive modeling and simulation of energy absorbing polyurethane foam under impact loading , 1992 .

[7]  Lothar M. Schmitt.  Optimization with genetic algorithms in multispecies environments , 2003, Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003.

[8]  Jan A Snyman,et al.  Practical Mathematical Optimization: An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms , 2005 .

[9]  Simon Ouellet,et al.  Compressive response of polymeric foams under quasi-static, medium and high strain rate conditions , 2006 .

[10]  Madhukar Vable.  Intermediate Mechanics of Materials , 2007 .

[11]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[12]  Strainlocalizationandcyclicdamageofpolyurethanefoam cylinders: experimental tests and theoretical model , 2010 .

[13]  A. Iusem,et al.  Full convergence of the steepest descent method with inexact line searches , 1995 .

[14]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[15]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[16]  Ya-Xiang Yuan,et al.  Optimization theory and methods , 2006 .