Memory-Efficient Modeling and Slicing of Large-Scale Adaptive Lattice Structures

TL;DR

This paper introduces a memory-efficient modeling and slicing method for large-scale adaptive lattice structures using graph representations and convolution surfaces, enabling scalable additive manufacturing with optimized support and stress management.

Contribution

It presents a novel graph-based representation and streaming slicing approach for large lattice structures, reducing memory use and improving blending at strut intersections.

Findings

Validated on structures with up to 100 million struts

Achieved significant memory savings during modeling and slicing

Demonstrated effective stress distribution and blending at intersections

Abstract

Lattice structures have been widely used in various applications of additive manufacturing due to its superior physical properties. If modeled by triangular meshes, a lattice structure with huge number of struts would consume massive memory. This hinders the use of lattice structures in large-scale applications (e.g., to design the interior structure of a solid with spatially graded material properties). To solve this issue, we propose a memory-efficient method for the modeling and slicing of adaptive lattice structures. A lattice structure is represented by a weighted graph where the edge weights store the struts' radii. When slicing the structure, its solid model is locally evaluated through convolution surfaces and in a streaming manner. As such, only limited memory is needed to generate the toolpaths of fabrication. Also, the use of convolution surfaces leads to natural blending at intersections of struts, which can avoid the stress concentration at these regions. We also present a computational framework for optimizing supporting structures and adapting lattice structures with prescribed density distributions. The presented methods have been validated by a series of case studies with large number (up to 100M) of struts to demonstrate its applicability to large-scale lattice structures.