A GPU-based parallel slicer for 3D printing

Xiong, Gang; Shen, Zhen; Zhao, Yiyao; Guo, Chao; Dong, Xisong

doi:10.1109/coase.2017.8256075

Cited by 5 publications

(1 citation statement)

References 16 publications

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“…Leveraging massively parallel architecture, GPU-based methods can provide up to a 30x speedup over similar CPU-based algorithms [11,12,13] and out-of-core slicing algorithms reduce the memory burden for larger meshes by keeping the mesh on the hard drive rather than the memory of the computer [14,15]. However, these outof-core methods have quadratic complexity in the number of triangles.…”

Section: Slicingmentioning

confidence: 99%

Plane-Activated Mapped Microstructure

Youngquist,

Peters,

Sitharam

2020

Preprint

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Querying and interacting with models of massive material micro-structure requires localized on-demand generation of the micro-structure since the full-scale storing and retrieving is cost prohibitive. When the micro-structure is efficiently represented as the image of a canonical structure under a non-linear space deformation to allow it to conform to curved shape, the additional challenge is to relate the query of the mapped micro-structure back to its canonical structure.This paper presents an efficient algorithm to pull back a mapped micro-structure to a partition of the canonical domain structure into boxes and only activates boxes whose image is likely intersected by a plane. The active boxes are organized into a forest whose trees are traversed depth first to generate mapped micro-structure only of the active boxes. The traversal supports, for example, 3D print slice generation in additive manufacturing.

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Section: Slicingmentioning

confidence: 99%