TY - JOUR
T1 - Memory-Efficient Modeling and Slicing of Large-Scale Adaptive Lattice Structures
AU - Liu, Shengjun
AU - Liu, Tao
AU - Zou, Qiang
AU - Wang, Weiming
AU - Doubrovski, Eugeni L.
AU - Wang, Charlie C.L.
N1 - Accepted Author Manuscript
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85105994363&partnerID=8YFLogxK
U2 - 10.1115/1.4050290
DO - 10.1115/1.4050290
M3 - Article
AN - SCOPUS:85105994363
SN - 1530-9827
VL - 21
JO - Journal of Computing and Information Science in Engineering
JF - Journal of Computing and Information Science in Engineering
IS - 6
M1 - 061003
ER -