Prediction of optimal 3D printing orientation using vertically sparse voxelization and modified support structure tomography

Abstract

PurposeIncreasingly 3D printing is used for parts of garments or for making whole garments due to their flexibility and comfort and for functionalizing or enhancing the aesthetics of the final garment and hence adding value. Many of these applications rely on complex programming of the 3D printer and are usually provided by the vendor company. This paper introduces a simpler, easier platform for designing 3D-printed textiles, garments and other artifacts, by predicting the optimal orientation of the target objects to minimize the use of plastic filaments.Design/methodology/approachThe main idea is based on the shadow-casting analogy, which assumes that the volume of the support structure is similar to that of the shadow from virtual sunlight. The triangular elements of the target object are converted into 3D pixels with integer-based normal vectors and real-numbered coordinates via vertically sparse voxelization. The pixels are classified into several groups and their noise is suppressed using a specially designed noise-filtering algorithm called slot pairing. The final support structure volume information was rendered as a two-dimensional (2D) figure, similar to a medical X-ray image. Thus, the authors named their method modified support structure tomography.FindingsThe study algorithm showed an error range of no more than 1.6% with exact volumes and 6.8% with slicing software. Moreover, the calculation time is only several minutes for tens of thousands of mesh triangles. The algorithm was verified for several meshes, including the cone, sphere, Stanford bunny and human manikin.Originality/valueSimple hardware, such as a CPU, embedded system, Arduino or Raspberry Pi, can be used. This requires much less computational resources compared with the conventional g-code generation. Also, the global and local support structure is represented both quantitatively and graphically via tomographs.