Experimental Identification of Fatigue Damage Model for Short Glass Fibre Reinforced Thermoplastic Composites

A polycyclic fatigue damage model for short glass fibre reinforced thermoplastics is developed and implemented into ABAQUS FE code using UMAT subroutine. The MNL model is introduced here in terms of damage rates: ( ) ) ( 2 0 1 2 0 2 1 2 1 1 ) ( ) ( N ij ij ij ij ij ij ij ij ij ij ij e E E N d d λ β ε γ ε β β α − −       +       + = ∂ ∂ = & (1) Where N is the number of cycles. αij, βij, λij and γij are the model parameters. Equation (1) consists of the sum of two terms: the first one is issued from Ladevèze’s model. The second one is based on an exponential term describing the first stage of damage specific to short glass fibre reinforced thermoplastics. In this work, the MNL model is performed to attempt a prediction of the fatigue damaged behaviour of a short glass fibre reinforced polyamide (PA 6-6) and polypropylene. To identify all the set of parameters, two different strategies are adopted. First, homogeneous tensile fatigue tests have been performed in longitudinal direction. Three strain controlled fatigue tests at different levels have been carried-out to identify the four parameters α11, β11, λ11 and γ11 , governing the evolution of d11, These fatigue tests were performed at 50% of εfailure, 60% of εfailure and 70% of εfailure. εfailure = 0.02, 0 11 E = 7500 MPa and R=0.3 (R=fatigue ratio). The identified parameters for the damage variable d11 in a short glass fibre reinforced polypropylene (PP-GFL40) are reported in table 1. The fatigue damage evolution curve predicted from the identified parameters is represented in figure 1 with respect to the experimental one. Table 1: Identification of the model parameters α11 β11 λ11 γ11 3.8E-8 1.5 0.00125 0.3 The second approach developed in this work is currently ongoing. It is based on the use of optical whole-field displacement/strain measurements by digital image correlation coupled to an inverse method from one single coupon [5,6]. It is worth noting, that the mechanical test must give rise to heterogeneous stress/strain fields (figure 2). Indeed, in this case, the constitutive parameters are expected to be all involved in the response of the specimen. Further results about this strategy will be presented in the final paper. Fig. 1: Typical Fatigue Damage Evolution in Fig. 2. Example of numerical results computed using Short Fibre Reinforced Thermoplastics the MNL implemented model at an increment (PP-GFL40, 50%εmax).corresponding to 2000 cycles. REFERENCES1. Sedrakian A., Ben Zineb T. and Billoet J.L., 2002 “Contribution of industrial composite parts to fatiguebehaviour simulation”, International Journal of Fatigue, pp 307-318, 1992.2. W. Van Paepegem, J. Degrieck. “A new coupled approach of residual stiffness and strength for fatigueof fibre-reinforced composites”, International Journal of Fatigue 24 (2002) 747–762.3. Ladeveze P. “Sur la mécanique de l’endommagement des composites”,Compte Rendu des JNC 1986;5.4. Nouri H., Meraghni F., Lory P. “A new thermodynamical fatigue damage model for short glass fibrereinforced thermoplastic composites”. 16 International Conference on composite materials, Kyoto July2007.5. Cooreman S.,Lecompte D.,Sol H.,Vantomme J., Debruyne D., “Elasto-plastic material parameteridentification by inverse methods: Calculation of the sensitivity matrix”, International journal of solidsand stuctures, November 2006.6. Claire D., Hild F. and Roux S. “A finite element formulation to identify damage fields: the equilibriumgap method”. International journal for numerical methods in engineering, 61: pp 189-208, 2004.ε12