Numerical analysis of damage mechanisms for 3D-printed sandwich structures using a meshless method

Abstract

Finite element analysis is a widely used simulation technique to analyze structural components and the mechanical behavior of materials under different loading conditions. The major steps involved in finite element simulations are the definition of the basic parameters and the discretization in elements of the component, also known as preprocessing, the analysis of the models and the post-processing by the representation and the interpretation of the obtained results. On the particular case of complex geometries obtained by fused deposition modeling, the most time consuming of these three steps is that of preprocessing, because on traditional finite element technology is require that the real geometry of the CAD model is simplified to be meshed and analyzed. Nowadays, different techniques have been proposed and developed with this objective: reduce costs in terms of time and specialized human resources because the workflow is easier and simpler. One of these methods is known as the meshless method, which do not require connection between nodes and are based on the interaction of the points of the geometry with the neighbors, so the processor analyzes the real CAD geometry and no simplifications are needed. The present work uses the meshless method based on the theory of external approximation as an alternative to the classic finite element method. In numerical simulations, the influence of core shape is evaluated on the failure mode of the 3D-printed lightweight structures. Three cell core patterns were numerically evaluated under tensile and three-point bending tests: out-of-plane hexagonal honeycomb, S-shape corrugated, and in-plane hexagonal honeycomb cores. The nucleation and propagation of cracks had more heterogeneous profiles for in-plane hexagonal honeycomb cores, showing greater unpredictability in the susceptible areas to failure. The numerical analysis with the meshless method showed a potential for a fast prediction and a low computational cost analysis.