Nonlinear constitutive models for FRP composites using artificial neural networks

This paper presents a new approach to generate nonlinear and multi-axial constitutive models for fiber reinforced polymeric (FRP) composites using artificial neural networks (ANNs). The new nonlinear ANN constitutive models are complete and have been integrated with displacement-based FE software for the nonlinear analysis of composite structures. The proposed ANN constitutive models are trained with experimental data obtained from off-axis tension/compression and pure shear (Arcan) tests. The proposed ANN constitutive model is generated for plane–stress states with assumed functional response in some parts of the multi-axial stress space with no experimental data. The ability of the trained ANN models to predict material response is examined directly and through FE analysis of a notched composite plate. The experimental part of this study involved coupon testing of thick-section pultruded FRP E-glass/polyester material. Nonlinear response was pronounced including in the fiber direction due to the relatively low overall fiber volume fraction (FVF). Notched composite plates were also tested to verify the FE, with ANN material models, to predict general nonhomogeneous responses at the structural level. 2007 Elsevier Ltd. All rights reserved.

[1]  Klaus Friedrich,et al.  Dynamic mechanical properties of PTFE based short carbon fibre reinforced composites: experiment and artificial neural network prediction , 2002 .

[2]  Huajian Gao,et al.  Identification of elastic-plastic material parameters from pyramidal indentation of thin films , 2002, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[3]  Mathew J. Palakal,et al.  Material model for composites using neural networks , 1993 .

[4]  A. Saxena,et al.  Artificial neural network and finite element modeling of nanoindentation tests , 2002 .

[5]  Philip D. Wasserman,et al.  Neural computing - theory and practice , 1989 .

[6]  Rami Haj-Ali,et al.  Numerical finite element formulation of the Schapery non‐linear viscoelastic material model , 2004 .

[7]  A. C. Okafor,et al.  Delamination Prediction in Composite Beams with Built-In Piezoelectric Devices Using Modal Analysis and Neural Network , 1996 .

[8]  Debabrata Chakraborty,et al.  Artificial neural network based delamination prediction in laminated composites , 2005 .

[9]  A. Muliana,et al.  A micromechanical constitutive framework for the nonlinear viscoelastic behavior of pultruded composite materials , 2003 .

[10]  Hakan Kilic,et al.  Nonlinear behavior of pultruded FRP composites , 2002 .

[11]  George Z. Voyiadjis,et al.  SIMULATED MICROMECHANICAL MODELS USING ARTIFICIAL NEURAL NETWORKS , 2001 .

[12]  Yoshihiro Ootao,et al.  Optimization of material composition of FGM hollow circular cylinder under thermal loading: a neural network approach , 1999 .

[13]  Jamshid Ghaboussi,et al.  Autoprogressive training of neural network constitutive models , 1998 .

[14]  Hakan Kilic,et al.  Progressive damage and nonlinear analysis of pultruded composite structures , 2003 .

[15]  Rami Haj-Ali,et al.  In-plane shear testing of thick-section pultruded FRP composites using a modified Arcan fixture , 2004 .