Memory-Efficient Modeling and Slicing of Large-Scale Adaptive Lattice StructuresSource: Journal of Computing and Information Science in Engineering:;2021:;volume( 021 ):;issue: 006::page 061003-1Author:Liu, Shengjun
,
Liu, Tao
,
Zou, Qiang
,
Wang, Weiming
,
Doubrovski, Eugeni L.
,
Wang, Charlie C. L.
DOI: 10.1115/1.4050290Publisher: The American Society of Mechanical Engineers (ASME)
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 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 100 M) of struts to demonstrate its applicability to large-scale lattice structures.
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contributor author | Liu, Shengjun | |
contributor author | Liu, Tao | |
contributor author | Zou, Qiang | |
contributor author | Wang, Weiming | |
contributor author | Doubrovski, Eugeni L. | |
contributor author | Wang, Charlie C. L. | |
date accessioned | 2022-02-06T05:37:26Z | |
date available | 2022-02-06T05:37:26Z | |
date copyright | 5/13/2021 12:00:00 AM | |
date issued | 2021 | |
identifier issn | 1530-9827 | |
identifier other | jcise_21_6_061003.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4278416 | |
description 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 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 100 M) of struts to demonstrate its applicability to large-scale lattice structures. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Memory-Efficient Modeling and Slicing of Large-Scale Adaptive Lattice Structures | |
type | Journal Paper | |
journal volume | 21 | |
journal issue | 6 | |
journal title | Journal of Computing and Information Science in Engineering | |
identifier doi | 10.1115/1.4050290 | |
journal fristpage | 061003-1 | |
journal lastpage | 061003-16 | |
page | 16 | |
tree | Journal of Computing and Information Science in Engineering:;2021:;volume( 021 ):;issue: 006 | |
contenttype | Fulltext |