Numerical characterisation of uncured elastomers by a neural network based approach

Model-free neural network approach captures large strain inelasicity.Inelastic model-free approach represented by recurrent neural network.Adaptive recurrent neural network represents different kinds of inelasticity (viscous, plastic and visco-plastic).Novel combination of neural network based material formulation and micro-sphere framework.Application of novel model-free approach to describe uncured rubber material.Different strategies to identify the parameters of the neural network.Performance demonstrated by numerical characterisation of uncured rubber.Analysis of a rubber forming process. This research paper contributes to the model-free characterisation of elastic and inelastic materials. The introduced material description is based on neural networks for the constitutive stress-strain-relationship and is an alternative to a classical constitutive material description. This approach is an efficient method to represent the material behaviour numerically. The performance of the novel model-free formulation is exemplary shown for uncured natural rubber. One major advantage of the presented approach is the capability to use it for the representation of a wide range of materials.The novel model-free characterisation consists of a neural network that is coupled to the so-called micro-sphere approach. This formulation was developed for the characterisation of rubber like materials and takes the micro-structure of the material into account. As a benefit of this coupling, the neural networks, representing the stress-stretch-dependency, have to be exploited analysed only in onedimensional direction. In the first instance, the derivation is introduced for an Artificial Neural Network to yield a pure elastic description. Subsequently, the model-free approach is expanded in order to represent inelastic material behaviour as well. This extended formulation is obtained via a Recurrent Neural Network.Finally, uncured natural rubber material is described by the derived numerical approach. The model-free characterisation is validated by the finite element simulation of material tests. A complex forming of a rubber block into a mould is basis for a final validation of the model-free description.

[1]  M. Kaliske,et al.  An extended tube-model for rubber elasticity : Statistical-mechanical theory and finite element implementation , 1999 .

[2]  L. Treloar,et al.  Stress-strain data for vulcanised rubber under various types of deformation , 1944 .

[3]  Norbert Hoffmann,et al.  Simulation Neuronaler Netze , 1991 .

[4]  K. Chandrashekhara,et al.  Neural Network Based Constitutive Model for Rubber Material , 2004 .

[5]  B. Yegnanarayana,et al.  Artificial Neural Networks , 2004 .

[6]  D. Krige A statistical approach to some basic mine valuation problems on the Witwatersrand, by D.G. Krige, published in the Journal, December 1951 : introduction by the author , 1951 .

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

[8]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[9]  Jörg F. Unger,et al.  Neural networks in a multiscale approach for concrete , 2009 .

[10]  Christian Miehe,et al.  Superimposed finite elastic–viscoelastic–plastoelastic stress response with damage in filled rubbery polymers. Experiments, modelling and algorithmic implementation , 2000 .

[11]  Mark A. Kramer,et al.  Improvement of the backpropagation algorithm for training neural networks , 1990 .

[12]  Steffen Freitag,et al.  Modeling of materials with fading memory using neural networks , 2009 .

[13]  Ivonne Sgura,et al.  Fitting hyperelastic models to experimental data , 2004 .

[14]  M. Kaliske,et al.  Comparison of approaches to model viscoelasticity based on fractional time derivatives , 2015 .

[15]  Hubert M. James,et al.  Theory of the Elastic Properties of Rubber , 1943 .

[16]  C. Miehe,et al.  Aspects of the formulation and finite element implementation of large strain isotropic elasticity , 1994 .

[17]  Stephan Pannier,et al.  Robust Design with Uncertain Data and Response Surface Approximation , 2010 .

[18]  S. Göktepe Micro-macro approaches to rubbery and glassy polymers : predictive micromechanically-based models and simulations , 2007 .

[19]  O. H. Yeoh,et al.  Characterization of Elastic Properties of Carbon-Black-Filled Rubber Vulcanizates , 1990 .

[20]  Hamid Garmestani,et al.  Prediction of nonlinear viscoelastic behavior of polymeric composites using an artificial neural network , 2006 .

[21]  M. Kaliske,et al.  Experimental Characterization and Constitutive Modeling of the Mechanical Properties of Uncured Rubber , 2010 .

[22]  Lothar Gaul,et al.  Finite Element Formulation of Viscoelastic Constitutive Equations Using Fractional Time Derivatives , 2002 .

[23]  R. Ogden Large deformation isotropic elasticity – on the correlation of theory and experiment for incompressible rubberlike solids , 1972, Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences.

[24]  P. Bazant,et al.  Efficient Numerical Integration on the Surface of a Sphere , 1986 .

[25]  M. Mooney A Theory of Large Elastic Deformation , 1940 .

[26]  Youssef M A Hashash,et al.  Numerical implementation of a neural network based material model in finite element analysis , 2004 .

[27]  Genki Yagawa,et al.  Implicit constitutive modelling for viscoplasticity using neural networks , 1998 .

[28]  Timothy W. Simpson,et al.  Metamodels for Computer-based Engineering Design: Survey and recommendations , 2001, Engineering with Computers.

[29]  M. Boyce,et al.  A three-dimensional constitutive model for the large stretch behavior of rubber elastic materials , 1993 .

[30]  H. Rothert,et al.  Formulation and implementation of three-dimensional viscoelasticity at small and finite strains , 1997 .

[31]  A. Tekkaya,et al.  Modeling and finite element simulation of loading-path-dependent hardening in sheet metals during forming , 2014 .

[32]  Martin D. Buhmann,et al.  Radial Basis Functions , 2021, Encyclopedia of Mathematical Geosciences.

[33]  Wolfgang Graf,et al.  Recurrent Neural Networks for Uncertain Time‐Dependent Structural Behavior , 2010, Comput. Aided Civ. Infrastructure Eng..

[34]  M. Wang,et al.  Statistical Theory of Networks of Non‐Gaussian Flexible Chains , 1952 .

[35]  Kwok-wing Chau,et al.  Particle Swarm Optimization Training Algorithm for ANNs in Stage Prediction of Shing Mun River , 2006 .

[36]  Serdar Göktepe,et al.  A micro-macro approach to rubber-like materials—Part I: the non-affine micro-sphere model of rubber elasticity , 2004 .

[37]  A. Lion,et al.  The Payne effect in finite viscoelasticity: constitutive modelling based on fractional derivatives and intrinsic time scales , 2004 .

[38]  P. Flory,et al.  Statistical Mechanics of Cross‐Linked Polymer Networks I. Rubberlike Elasticity , 1943 .

[39]  Pol D. Spanos,et al.  Neural network based Monte Carlo simulation of random processes , 2005 .

[40]  Martin D. Buhmann,et al.  Radial Basis Functions: Theory and Implementations: Preface , 2003 .

[41]  Marcus Frean,et al.  The Upstart Algorithm: A Method for Constructing and Training Feedforward Neural Networks , 1990, Neural Computation.

[42]  Michael Kaliske,et al.  Bergström–Boyce model for nonlinear finite rubber viscoelasticity: theoretical aspects and algorithmic treatment for the FE method , 2009 .

[43]  L. Treloar,et al.  The elasticity of a network of long-chain molecules.—III , 1943 .

[44]  Douglas C. Montgomery,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[45]  Markus Kästner,et al.  On the numerical handling of fractional viscoelastic material models in a FE analysis , 2013 .

[46]  Wolfgang Graf,et al.  A material description based on recurrent neural networks for fuzzy data and its application within the finite element method , 2013 .

[47]  S. Reese,et al.  A theory of finite viscoelasticity and numerical aspects , 1998 .

[48]  Michael Kaliske,et al.  An endochronic plasticity formulation for filled rubber , 2010 .

[49]  Michael Kaliske,et al.  Neural network based material description of uncured rubber for use in finite element simulation , 2011 .