Adaptive Slicing of Point Cloud Directly with Discrete Interpolable-Area Error Profile in Additive Manufacturing

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.