About this Abstract |
| Meeting |
2021 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2021)
|
| Symposium
|
Special Session
|
| Presentation Title |
Adaptive Voxelization for Computed Axial Lithography |
| Author(s) |
Kevin P. Coulson, Joseph T. Toombs, Magnus Gu, Hayden K Taylor |
| On-Site Speaker (Planned) |
Kevin P. Coulson |
| Abstract Scope |
Computed axial lithography (CAL) is a tomographic additive manufacturing technology that offers exceptionally fast printing in a wide range of materials. CAL involves pre-computing a sequence of light patterns to be projected into a photopolymer. For a uniform spatial discretization of the target geometry, computational time scales inversely with the cube of the discretization pitch, which makes it challenging to exploit the full space-bandwidth product of available spatial light modulators. This work introduces an adaptive voxelization approach to reduce computational expense. Using one of several proposed mesh-based complexity analyses, a CAD model is recursively subdivided into stacked sub-meshes of varying voxel resolution. These complexity methods can be tailored to emphasize complexity in particular regions. Each sub-mesh is then independently voxelized before projections are generated and optimized in parallel. On a four-core CPU, this method results in a 2-3x speedup with applications in high-precision CAL and other voxel-based additive manufacturing computations. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |