Abstract & Overview
Additive manufacturing (AM) is gaining momentum from being considered a rapid prototyping tool to final part fabrications. Common uses of AM largely include the fabrication of lattice structures to reduce the mass of components. Thus, it is very important to determine structure design information beforehand using lattice structures in various industry applications. This research aims to propose a design optimization framework for determining 3D printed lattice structures to satisfy functional requirements using the design of experiments (DOE) and multi-objective optimization. In the proposed framework, a lattice topology is used to determine the design parameters of the selected lattice based on static compression simulations. The design parameters of the selected lattice are investigated based on the predefined design requirements. DOE is conducted to understand the relationship between the design variables and the responses. A genetic algorithm is applied to obtain a set of solutions from a compromised final design. To validate the proposed framework, 3 different lattice design parameters such as surface lattice thickness, volume lattice cell size, and skin thickness of Hexagon are selected to compare to the simulation results using Polyamide 11 material via the selective laser sintering (SLS) technology.