Development of an Elastic Material Model for BCC Lattice Cell Structures Using Finite Element Analysis and Neural Networks Approaches

Tahseen Alwattar,Ahsan Mian

doi:10.3390/jcs3020033

Abstract

Lattice cell structures (LCS) are being investigated for applications in sandwich composites. To obtain an optimized design, finite element analysis (FEA) -based computational approach can be used for detailed analyses of such structures, sometime at full scale. However, developing a large-scale model for a lattice-based structure is computationally expensive. If an equivalent solid FEA model can be developed using the equivalent solid mechanical properties of a lattice structure, the computational time will be greatly reduced. The main idea of this research is to develop a material model which is equivalent to the mechanical response of a lattice structure. In this study, the mechanical behavior of a body centered cubic (BCC) configuration under compression and within elastic limit is considered. First, the FEA approach and theoretical calculations are used on a single unit cell BCC for several cases (different strut diameters and cell sizes) to predict equivalent solid properties. The results are then used to develop a neural network (NN) model so that the equivalent solid properties of a BCC lattice of any configuration can be predicted. The input data of NN are bulk material properties and output data are equivalent solid mechanical properties. Two separate FEA models are then developed for samples under compression: one with 5 × 5 × 4 cell BCC and one completely solid with equivalent solid properties obtained from NN. In addition, 5 × 5 × 4 cell BCC LCS specimens are fabricated on a Fused Deposition Modeling uPrint SEplus 3D printer using Acrylonitrile Butadiene Styrene (ABS) and tested under compression. Experimental load-displacement behavior and the results obtained from both the FEA models are in good agreement within the elastic limit.

Highlights

Lattice cell structures (LCS) are a kind of engineered structure having periodic cell made of struts at different orientations
Due to the advent of new additive manufacturing (AM) technologies, applications of LCS are currently being explored in several industries including gas storage, filtering, thermal science, and aerospace [1,2,3,4]
As the equivalent mechanical properties are obtained after training of neural network (NN) using the outcomes of unit cell finite element analysis (FEA) models, the results of the NN model are supposed to approach that of FEA

Summary

Introduction

Lattice cell structures (LCS) are a kind of engineered structure having periodic cell made of struts at different orientations. To obtain an optimized LCS design for any particular application, finite element analysis (FEA) tools are usually used to model and numerically analyze many lattice configurations. The FEA involves a massive number of degrees of freedom to resolve for each strut in structures, which increases the computational time extremely [5]. A body centered cubic BCC unit cell with a strut diameter of 0.7 mm and dimensions of 5 mm × 5 mm × 5 mm in x, y, and z direction respectively under tension has been simulated using finite element analysis with a computational time of 10 h [5]. It is beneficial to develop a solid FEA model with a fewer number of degrees of freedom that represents the complex LCS. The main idea of an equivalent mechanical properties is to replace and represent the lattice structure model with an equivalent compact solid material

Methods

Results

Conclusion