This paper presents a multi-scale modeling approach involving interfacial interactions to predict the elastic properties and mechanical behavior of single-layer graphene-reinforced nanocomposites under tension load. A multi-scale model was developed using the finite element method of the tripartite structure consisting of graphene in epoxy, the interfacial region and their Van Der Walls interactions. The effect of graphene chirality was investigated by proposing a methodology of graphene-Van Der Walls interactions-polymer with determined geometric dimensions. Parametric modeling was performed to model the interactions between Van Der Walls, graphene and interface material atoms using the finite element method with molecular mechanics approach. Numerical analysis of graphene nanoparticles by embedding them in an epoxy with their real dimensions is not an appropriate task today. In particular, it is not possible to analyze these real graphene nanoparticles as multiple by randomly dispersing them in the epoxy polymer. Therefore, in this research, a model was developed to overcome this problem and to investigate the effect of molecular interactions on loads in different axes. The results show that graphene nanocomposites in armchair geometry give higher stress values and behave more rigidly. As the volume ratio increases, the mechanical performances increase. It is seen that the graphene direction is much stronger than the thickness direction. It is clear that the volume ratio effect in the thickness direction has a slight effect on the tensile behavior.

Abstract. The study investigated the tensile behavior of Sandwich Functionally Graded Material (SFGM) fabricated using Additive Manufacturing (AM) technology. SFGMs are characterized by a gradual variation in composition and structure with respect to the forming volume from the lower and upper surfaces of the structure towards the center, resulting in a corresponding change in material properties. Fused Filament Fabrication (FFF), a widely used AM process, was used in the present work to fabricate the thermoplastic polymer-based SFGM specimens. SFGM were produced by the FFF method using ABS and PLA materials and subjected to tensile tests according to ASTM D638.

Abstract. In this paper, using molecular and micromechanics methods, a new approach for the prediction of the stiffness of randomly distributed CNT/polymer nanocomposites with the van der walls interactions is presented. A multi-scale modeling technique was designed for CNT nanoparticles randomly embedded in the polymer using AMBER force field. This multi-scale model constitutes a representative volume element. The representative volume element consists of polymer, CNT nanoparticle, CNT-polymer interfacial region and van der waals bonds. A programming code was developed that randomly distributes nanoparticles according to the desired volume fraction. Python scripting language was used for the modeling technique performed in a finite element environment. By modeling the interfacial regions around randomly distributed CNTs, van der Waals bonds are modeled stochastically. In this study, the subject of interest is the number of CNTs positioned in the RVE according to the volume ratio. These numbers were determined at the level allowed by finite element equations and computational solvers and their effects were investigated by calculated stiffness behavior.

In this study, a new algorithm was developed for the random distribution of the nanomaterials in the polymer matrix to model realistic behavior of polymer nanocomposites. The study focused on the development of this algorithm rather than the modeling of nanocomposites as a finite element method. The multi-scale method with a representative volume element (RVE) is generally used for numerical modeling of nanomaterials and polymer nanocomposites. The researchers investigate the effect of the reinforcement material and the reinforcement mechanism has not been fully explained both numerically and experimentally. The success of numerical studies is also very important to specify the effect of reinforcement mechanism in experimental studies. For this reason, an algorithm was developed to model the realistic distribution of nanomaterials in the polymer matrix and adapted to numerical studies. The algorithm provided that materials of desired geometric dimensions were randomly positioned within a control volume and did not intersect with each other and the control volume. The algorithm was developed using the Python programming language and the positions of the nanomaterials were transferred to the ABAQUS finite element program using scripting language. Graphene was used as a nanomaterial and epoxy was used as a polymer matrix. Randomly distributed RVE models gave more successful results than single element RVE models. It shows a good agreement with experimental results.