Design of a Graduated Lattice Structure Based on Topology Optimization for Component Design Obtained by Additive Manufacturing

EasyChair Preprint 10425

Abstract

Topology optimization techniques and generative lattice design strategy are two increasingly common approaches for an advanced structural design that can achieve the optimal structural configuration of additively manufactured parts, which seem to be the focus of ongoing research in the additive manufacturing community. In this paper, a new methodology for designing and constructing graded lattice structures based on the results obtained from topological optimization suitable for additive fabrication is proposed in order to generate a lightweight lattice structure design with predictable yield performance. This method was developed using a combination of the two programs ANSYS and MATLAB, where the ANSYS software is relied upon in the process of designing and creating structures, and algorithms for topology optimization and deriving lattice structures were developed in MATLAB software. The effectiveness of the proposed method was demonstrated by solving the simply supported beam reference problem to verify the superior rigidity properties of the improved graded lattice structure compared to the basic design with uniform interested structures. The paper also examines the use of minimum triply periodic surfaces as a unique class of lattice structures that can be used for a wide range of applications due to their low volume fractions and smooth curvature.

Keyphrases: Additive Manufacturing, Graded lattice, lattice structure, topology optimization

@booklet{EasyChair:10425,
  author    = {Issam El Khadiri and Mohamed Abouelmajd and Maria Zemzami and Nabil Hmina and Manuel Lagache and Bandar Almangour and Ahmed Bahlaoui and Ismail Arroub and Soufiane Belhouideg},
  title     = {Design of a Graduated Lattice Structure Based on Topology Optimization for Component Design Obtained by Additive Manufacturing},
  howpublished = {EasyChair Preprint 10425},
  year      = {EasyChair, 2023}}