journal article·2026-02-01

Fast Prediction of 3D Printing Optimal Orientation Using General-Purpose Graphic Processor Unit Calculation

Jae Ryoung Kim, In Hwan Sul

Publisher page

Abstract

This article presents a significant advancement in the field of three-dimensional (3D) printing. We have developed a fast parallel computation algorithm that predicts the optimal orientation of 3D printing using a general-purpose graphic processor unit (GPU). Initially designed for the central processing unit (CPU) version support structure tomography, our algorithm has been successfully adapted for NVIDIA graphic processors and the CUDA toolkit. Despite encountering several challenges, we have achieved a remarkable improvement in calculation speed. Two CPUs and four GPUs of various prices and performances were used for the speed comparison. The high-end GPU showed a surprising multiprocessing performance; a maximum of 16.2 times for Dragon mesh data and 11.2 times on average than the high-end CPU. The proposed method assumes that the input triangle should have a bigger size than the voxel size and a relatively smaller number of triangles, about tens of thousands. Nevertheless, the algorithm has the potential to significantly enhance the efficiency and quality of 3D printing, addressing some constraints and expanding its practical applications.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Additive Manufacturing and 3D Printing Technologies · Manufacturing Process and Optimization · Augmented Reality Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Materials Design Engineering, Kumoh National Institute of Technology, Gumi, Republic of Korea.

References (30)

Show source-provided references
1. ISO A. ISO/ASTM 52900. 2015 Additive Manufacturing–General Principles–Terminology. 2015.
2. 10.1080/17452759.2015.111151910.1080/17452759.2015.1111519 ↗
3. 10.1080/17452759.2016.120986710.1080/17452759.2016.1209867 ↗
4. 10.1617/s11527-015-0571-010.1617/s11527-015-0571-0 ↗
5. 10.12924/of2015.0101001910.12924/of2015.01010019 ↗
6. 10.1111/1541-4337.1244310.1111/1541-4337.12443 ↗
7. 10.21037/atm.2016.12.2410.21037/atm.2016.12.24 ↗
8. 10.1007/s11548-010-0476-x10.1007/s11548-010-0476-x ↗
9. 10.1038/nbt.295810.1038/nbt.2958 ↗
10. 10.1177/168781401882288010.1177/1687814018822880 ↗
11. 10.1007/s00170-012-4558-510.1007/s00170-012-4558-5 ↗
12. 10.1145/3197517.320134210.1145/3197517.3201342 ↗
13. 10.1016/j.cad.2010.02.00110.1016/j.cad.2010.02.001 ↗
14. 10.1080/16864360.2017.130807410.1080/16864360.2017.1308074 ↗
15. Xu Q, , Jeon H, , Annavaram M. Graph processing on GPUs: Where are the bottlenecks? IEEE: 2014.
16. 10.1016/j.cag.2015.05.00910.1016/j.cag.2015.05.009 ↗
17. 10.1186/s40691-022-00290-z10.1186/s40691-022-00290-z ↗
18. 10.1109/JAS.2023.12361810.1109/JAS.2023.123618 ↗
19. Zhang C, , Zhang C, , Zheng S, et al. A complete survey on generative ai (aigc): Is chatgpt from gpt-4 to gpt-5 all you need? arXiv Preprint arXiv, 2023:230311717; doi: 10.13140/RG.2.2.29980.16001
20. Nurvitadhi E, , Sheffield D, , Sim J, et al. Accelerating binarized neural networks: Comparison of FPGA, CPU, GPU, and ASIC. IEEE: 2016.
21. Buck I. Gpu computing with nvidia cuda. 2007.10.1145/1281500.1281647 ↗
22. Jung JY, , Sul IH. Prediction of 3D printing bottom plate amount using open source software and support structure tomography. Text Sci Eng, 2023; 60(4):249–255; doi: 10.12772/TSE.2023.60.249
23. Jung JY, , Sul IH. Prediction of filament usage in human manikin 3d printing using voxel approximation-based shadow projection. Text Sci Eng, 2022; 59(2):79–87; doi: 10.12772/TSE.2022.59.079
24. 10.1016/j.matpr.2022.02.31010.1016/j.matpr.2022.02.310 ↗
25. 10.1016/j.jpdc.2018.11.01210.1016/j.jpdc.2018.11.012 ↗
26. 10.1145/301799410.1145/3017994 ↗
27. He B Govindaraju NK Luo Q et al. Efficient gather and scatter operations on graphics processors. 2007.10.1145/1362622.1362684 ↗
28. 10.1016/B978-0-12-385963-1.00003-410.1016/B978-0-12-385963-1.00003-4 ↗
29. Slotwinski JA Moylan S. Applicability of existing materials testing standards for additive manufacturing materials. US Department of Commerce National Institute of Standards and Technology: 2014.10.6028/NIST.IR.8005 ↗
30. Curless B Levoy M. A volumetric method for building complex models from range images. 1996.10.1145/237170.237269 ↗