A study of interlaminar properties for a unidirectional glass fiber reinforced epoxy composite

Abstract

In this work, a numerical-experimental study of the interlaminar zone for an unidirectional glass fiber reinforced epoxy composite is carried out in order to predict the load-displacement curves of a double cantilever beam test. First, an experimental mechanical characterization of the laminated composite was made through quasi-static in-plane tensile and bending tests and out-of-plane delamination tests (i.e. double cantilever beam tests or opening mode I). The main results have shown that the elastic module in the fiber direction is E 1 ¼ 32.1 GPa and the fracture process is characterized by a critical energy release rate G IC ¼ 1466 N/m. In order to be able to predict delamination using finite element analysis, a bilinear cohesive zone model is adopted. This law has two parameters-the initial stiffnessK and the critical traction t n0-to be fit for reducing the distance between numerical and experimental double cantilever beam load-displacement curves. That means an optimization problem, which is here solved by proposing a very simple and cheap three-step procedure, avoiding expensive three-dimensional simulations: (i) the numerical double cantilever beam test is coded in the OCTAVE software using one-dimensional beam elements and the bilinear cohesive zone model; (ii) several onedimensional simulations are performed varying at the cohesive law's parameters in order to build the objective function; and (iii) a genetic algorithm from the Scilab optimization toolbox is then used to determine the interface's parameters minimizing the objective function. This method has proposedK ¼ 2.74 Â 10 14 N/m 3 and t n0 ¼ 1.99 MPa as the best parameters fitting the curves. Finally, the identified bilinear cohesive law's parameters are used to carry out a full three-dimensional finite element analysis simulation in ANSYS, showing the same response than the simplified beam code, but with the possibility to obtain more accurate information about the fracture process (e.g. the shape of the process zone) or to use more complicated geometries or boundary conditions. In this work, we show the ability to predict the material behavior through different methods, using the best characteristics of each one.

Determination of Compression Stress and Volumetric Weight of Lightened Concrete Blocks, with the Use of Recycled Polymers and Nanoadditives

“…According to Krasna et al [17], the resistance of the blocks with 25% plastic is suitable for these masonry to be used in the elaboration of exterior and interior partition walls. As Pincheira et al [43] indicated, recycled PET can be used to manufacture concrete with superior mechanical properties. With the compression results obtained in the current study it can be defined that by introducing different percentages of PET plastic and a certain amount of nanoadditive the resistance exceeds that of traditional blocks.…”

Optimizing the search directions of a mixed DDM applied on cracks

“…The optimization of Search Directions is addressed with metaheuristics and optimization algorithms, which have been applied widely in the mechanical context, such as the optimization of the design of a composite riser using an elitist real-coded genetic algorithm in order to reduce the structural weight (Wang et al 2014), and determinate the interface parameters to reduce the distance between numerical and experimental load-displacement curve of a reinforced glass fiber epoxy composite using genetic algorithms (Pincheira et al 2018). Another examples can be found in (Corriveau et al 2010;Saitou et al 2005).…”

Simulation of Mode I Interlaminar Damage of a GFRP Composite Using Cohesive Laws in the Framework of the Equivalent LEFM R-Curve and an Optimised Algorithm

“…Although these techniques require very high resolution equipment to capture the CTOD or FPZ, which can cost a lot of money and be difficult to implement in specimens tested in a controlled environmental. Another option is to use inverse methods for minimising the residual between experimental and numerical curves [ 44 , 63 , 64 , 65 , 66 ]. However, despite their simplicity, they are very time-consuming because it is necessary several virtual tests to evaluate different values of each parameter of the softening function .…”