
Adaptive Slicing of Point Cloud Directly with Discrete Interpolable-Area Error Profile in Additive Manufacturing
Journal Article
05-16-02-0014
ISSN: 1946-3979, e-ISSN: 1946-3987
Sector:
Topic:
Citation:
Moodleah, S. and Kirimasthong, K., "Adaptive Slicing of Point Cloud Directly with Discrete Interpolable-Area Error Profile in Additive Manufacturing," SAE Int. J. Mater. Manf. 16(2):175-187, 2023, https://doi.org/10.4271/05-16-02-0014.
Language:
English
Abstract:
Point cloud objects have gained popularity in three-dimensional (3D) printing
recently due to advancements in reverse engineering technology. Fabricating an
object with a fused deposition modeling (FDM) printer requires converting the
object to layered contours, which involves a slicing process. The slicing
process of a point cloud object usually requires reconstructing a 3D object from
a point cloud, which requires users’ deep understanding of 3D modeling software
and a laborious work process. To avoid these problems, the direct slicing of
point cloud objects is gaining more popularity. This research work proposes an
adaptive slicing approach from point cloud objects directly without surface
reconstruction. The adaptive slicing maintains the global geometry error while
requiring a smaller number of fabrication layers and printing time. A new error
profile used in the adaptive slicing approach is introduced. It approximates the
geometry error from the point cloud directly based on the discrete
interpolable-area (DIA) error between two adjacent layers. The interpolable
capability of the DIA error profile allows the adaptive slicing algorithm to
efficiently measure the geometry error of a point cloud. We perform the proposed
algorithm with four point cloud models that represent both symmetrical and
asymmetrical shapes. The adaptive slicing results show that the performance is
increased by 8.05%–32.73% while maintaining accuracy compared to traditional
uniform slicing. Furthermore, the fabrication time and materials used are
reduced by 10.30%–39.10% and 1.01%–13.47%, respectively. Based on these results,
further research can be focused on finding an optimal threshold between the
accuracy of the contour projection and the distance between the layers, which
could further improve fabrication performance.