research-article

Perceptual models of preference in 3D printing direction

Authors:

Xiaoting Zhang,

Athina Panotopoulou,

Charlie C. L. WangAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 34, Issue 6

Article No.: 215, Pages 1 - 12

https://doi.org/10.1145/2816795.2818121

Published: 02 November 2015 Publication History

Abstract

This paper introduces a perceptual model for determining 3D printing orientations. Additive manufacturing methods involving low-cost 3D printers often require robust branching support structures to prevent material collapse at overhangs. Although the designed shape can successfully be made by adding supports, residual material remains at the contact points after the supports have been removed, resulting in unsightly surface artifacts. Moreover, fine surface details on the fabricated model can easily be damaged while removing supports. To prevent the visual impact of these artifacts, we present a method to find printing directions that avoid placing supports in perceptually significant regions. Our model for preference in 3D printing direction is formulated as a combination of metrics including area of support, visual saliency, preferred viewpoint and smoothness preservation. We develop a training-and-learning methodology to obtain a closed-form solution for our perceptual model and perform a large-scale study. We demonstrate the performance of this perceptual model on both natural and man-made objects.

Supplementary Material

ZIP File (a215-zhang.zip)

Supplemental files.

Download
55.35 MB

References

[1]

Alexander, P., Allen, S., and Dutta, D. 1998. Part orientation and build cost determination in layered manufacturing. Computer-Aided Design 2, 3, 343--356.

[2]

Chalasani, K., Jones, L., and Roscoe, L. 1995. Support generation for fused deposition modeling. In Proceedings of 1995 Symposium on Solid Freeform Fabrication, 229--241.

[3]

Chen, Y., Li, K., and Qian, X. 2013. Direct geometry processing for tele-fabrication. ASME Journal of Computing and Information Science in Engineering 13, 4, 041002.

[4]

Cheng, W., Fuh, J., Nee, A., Wong, Y., Loh, H., and Miyazawa, T. 1995. Multi-objective optimization of part-building orientation in stereolithography. Rapid Prototyping Journal 1, 4, 12--23.

[5]

Chorowski, J., Wang, J., and Zurada, J. M. 2014. Review and performance comparison of svm-and elm-based classifiers. Neurocomputing 128, 507--516.

Digital Library

[6]

Cochocki, A., and Unbehauen, R. 1993. Neural Networks for Optimization and Signal Processing. John Wiley & Sons, Inc.

Digital Library

[7]

Dong, Y., Wang, J., Pellacini, F., Tong, X., and Guo, B. 2010. Fabricating spatially-varying subsurface scattering. ACM Trans. Graph. 29, 4, 62:1--62:10.

Digital Library

[8]

Dumas, J., Hergel, J., and Lefebvre, S. 2014. Bridging the gap: Automated steady scaffoldings for 3d printing. ACM Trans. Graph. 33, 4 (July), 98:1--98:10.

Digital Library

[9]

Faure, F., Barbier, S., Allard, J., and Falipou, F. 2008. Image-based collision detection and response between arbitrary volume objects. In Proceedings of the 2008 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, Eurographics Association, Aire-la-Ville, Switzerland, Switzerland, SCA '08, 155--162.

Digital Library

[10]

Hearst, M., Dumais, S., Osman, E., Platt, J., and Scholkopf, B. 1998. Support vector machines. IEEE Intelligent Systems and their Applications 13, 4, 18--28.

Digital Library

[11]

Herholz, P., Matusik, W., and Alexa, M. 2015. Approximating free-form geometry with height fields for manufacturing. Computer Graphics Forum (Proc. of Eurographics) 34, 2, 239--251.

Digital Library

[12]

Hildebrand, K., Bickel, B., and Alexa, M. 2013. Orthogonal slicing for additive manufacturing. Computers & Graphics 37, 6, 669--675.

Digital Library

[13]

Hu, R., Li, H., Zhang, H., and Cohen-Or, D. 2014. Approximate pyramidal shape decomposition. ACM Transactions on Graphics (Proc. of SIGGRAPH Asia 2014) 33, 6, 213:1--213:12.

Digital Library

[14]

Hu, K., Jin, S., and Wang, C. 2015. Support slimming for single material based additive manufacturing. Computer-Aided Design 65, 1--10.

Digital Library

[15]

Huang, G.-B., Chen, L., and Siew, C.-K. 2006. Universal approximation using incremental constructive feedforward networks with random hidden nodes. Neural Networks, IEEE Transactions on 17, 4, 879--892.

Digital Library

[16]

Huang, G.-B., Zhu, Q.-Y., and Siew, C.-K. 2006. Extreme learning machine: theory and applications. Neurocomputing 70, 1, 489--501.

[17]

Huang, G.-B., Ding, X., and Zhou, H. 2010. Optimization method based extreme learning machine for classification. Neurocomputing 74, 1, 155--163.

Digital Library

[18]

Huang, G.-B., Wang, D. H., and Lan, Y. 2011. Extreme learning machines: a survey. International Journal of Machine Learning and Cybernetics 2, 2, 107--122.

[19]

Huang, G.-B., Zhou, H., Ding, X., and Zhang, R. 2012. Extreme learning machine for regression and multiclass classification. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on 42, 2, 513--529.

Digital Library

[20]

Huang, G., Huang, G.-B., Song, S., and You, K. 2015. Trends in extreme learning machines: A review. Neural Networks 61, 32--48.

Digital Library

[21]

Kulkarni, P., Marsan, A., and Dutta, D. 2000. A review of process planning techniques in layered manufacturing. Rapid Prototyping Journal 6, 1, 18--35.

[22]

Lan, P.-T., Chou, S.-Y., Chen, L.-L., and Gemmill, D. 1997. Determining fabrication orientations for rapid prototyping with stereolithography apparatus. Computer-Aided Design 29, 1, 53--62.

[23]

Lan, Y., Dong, Y., Pellacini, F., and Tong, X. 2013. Bi-scale appearance fabrication. ACM Trans. Graph. 32, 4, 145:1--145:12.

Digital Library

[24]

Lee, C. H., Varshney, A., and Jacobs, D. W. 2005. Mesh saliency. ACM Trans. Graph. 24, 3 (July), 659--666.

Digital Library

[25]

Leung, Y.-S., and Wang, C. 2013. Conservative sampling of solids in image space. IEEE Comput. Graph. Appl. 33, 1 (Jan.), 32--43.

Digital Library

[26]

Levya, G. N., Schindela, R., and Kruth, J. P. 2003. Rapid manufacturing and rapid tooling with layer manufacturing {(LM)} technologies, state of the art and future perspectives. {CIRP} Annals - Manufacturing Technology 52, 2, 589--609.

[27]

Liu, L., Shamir, A., Wang, C., and Whiting, E. 2014. 3d printing oriented design: Geometry and optimization. In SIGGRAPH Asia 2014 Courses, ACM, New York, NY, USA, SA '14.

Digital Library

[28]

Luo, L., Baran, I., Rusinkiewicz, S., and Matusik, W. 2012. Chopper: Partitioning models into 3D-printable parts. ACM Transactions on Graphics (Proc. SIGGRAPH Asia) 31, 6 (Dec.).

Digital Library

[29]

Majhi, J., Janardan, R., Smid, M., and Schwerdt, J. 1998. Multi-criteria geometric optimization problems in layered manufacturing. In Proceedings of the fourteenth annual symposium on Computational geometry, ACM, 19--28.

Digital Library

[30]

Masood, S., and Rattanawong, W. 2002. A generic part orientation system based on volumetric error in rapid prototyping. The International Journal of Advanced Manufacturing Technology 19, 3, 209--216.

[31]

Otsu, N. 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics 9, 1, 62--66.

[32]

Padhye, N., and Deb, K. 2011. Multi-objective optimisation and multi-criteria decision making in SLS using evolutionary approaches. Rapid Prototyping Journal 17, 6, 458--478.

[33]

Papas, M., Regg, C., Jarosz, W., Bickel, B., Jackson, P., Matusik, W., Marschner, S., and Gross, M. 2013. Fabricating translucent materials using continuous pigment mixtures. ACM Trans. Graph. 32, 4, 146:1--146:12.

Digital Library

[34]

Podolak, J., Shilane, P., Golovinskiy, A., Rusinkiewicz, S., and Funkhouser, T. 2006. A planar-reflective symmetry transform for 3d shapes. ACM Trans. Graph. 25, 3, 549--559.

Digital Library

[35]

Rhodes, G., Proffitt, F., Grady, J. M., and Sumich, A. 1998. Facial symmetry and the perception of beauty. Psychonomic Bulletin and Review 5, 4, 659--669.

[36]

Sachs, E., Cima, M., Williams, P., Brancazio, D., and Cornie, J. 1992. Three dimensional printing: Rapid tooling and prototypes directly from a CAD model. ASME Journal of Engineering for Industry 114, 4, 481--488.

[37]

Schüller, C., Panozzo, D., and Sorkine-Hornung, O. 2014. Appearance-mimicking surfaces. ACM Trans. Graph. 33, 6, 216:1--216:10.

Digital Library

[38]

Secord, A., Lu, J., Finkelstein, A., Singh, M., and Nealen, A. 2011. Perceptual models of viewpoint preference. ACM Trans. Graph. 30, 5 (Oct.), 109:1--109:12.

Digital Library

[39]

Strano, G., Hao, L., Everson, R., and Evans, K. 2013. A new approach to the design and optimisation of support structures in additive manufacturing. The International Journal of Advanced Manufacturing Technology 66, 9--12, 1247--1254.

[40]

Umetani, N., and Schmidt, R. 2013. Cross-sectional structural analysis for 3d printing optimization. In SIGGRAPH Asia 2013 Technical Briefs, 5:1--5:4.

Digital Library

[41]

Vanek, J., Galicia, J. A. G., and Benes, B. 2014. Clever support: Efficient support structure generation for digital fabrication. Computer Graphics Forum 33, 5, 117--125.

Digital Library

[42]

Vidimče, K., Wang, S.-P., Ragan-Kelley, J., and Matusik, W. 2013. Openfab: A programmable pipeline for multi-material fabrication. ACM Trans. Graph. 32, 4, 136:1--136:12.

Digital Library

[43]

Wang, C. C. L., Leung, Y.-S., and Chen, Y. 2010. Solid modeling of polyhedral objects by layered depth-normal images on the gpu. Comput. Aided Des. 42, 6 (June), 535--544.

Digital Library

[44]

Wang, W., Wang, T. Y., Yang, Z., Liu, L., Tong, X., Tong, W., Deng, J., Chen, F., and Liu, X. 2013. Cost-effective printing of 3d objects with skin-frame structures. ACM Trans. Graph. 32, 6 (Nov.), 177:1--177:10.

Digital Library

[45]

Wang, W., Chao, H., Tong, J., Yang, Z., Tong, X., Li, H., Liu, X., and Liu, L. 2015. Saliency-preserving slicing optimization for effective 3d printing. Computer Graphics Forum.

[46]

Xie, Z., Xu, K., Liu, L., and Xiong, Y. 2014. 3d shape segmentation and labeling via extreme learning machine. Computer Graphics Forum 33, 5, 85--95.

Digital Library

[47]

Xie, Z., Xu, K., Shan, W., Liu, L., Xiong, Y., and Huang, H. 2015. Projective feature learning for 3d shapes with multi-view depth images. Computer Graphics Forum (Proc. of Pacific Graphics 2015) 34, 6, to appear.

Cited By

Kumar SKumar R(2025)A Comprehensive Study on Additive Manufacturing Techniques, Machine Learning Integration, and Internet of Things-Driven Sustainability OpportunitiesJournal of Materials Engineering and Performance10.1007/s11665-025-10757-xOnline publication date: 5-Mar-2025
https://doi.org/10.1007/s11665-025-10757-x
Wang TLi YLi TLiu BLi XZhang X(2025)Machine learning in additive manufacturing: enhancing design, manufacturing and performance prediction intelligenceJournal of Intelligent Manufacturing10.1007/s10845-025-02568-7Online publication date: 27-Jan-2025
https://doi.org/10.1007/s10845-025-02568-7
Vasileska EArgilovski ATomov MJovanoski BGecevska V(2024)Implementation of Machine Learning for Enhancing Lean Manufacturing Practices for Metal Additive ManufacturingIEEE Transactions on Engineering Management10.1109/TEM.2024.345964571(14836-14845)Online publication date: 2024
https://doi.org/10.1109/TEM.2024.3459645
Show More Cited By

Index Terms

Perceptual models of preference in 3D printing direction

Recommendations

Medial axis tree-an internal supporting structure for 3D printing

Saving material and improving strength are two important but conflicting requirements in 3D printing. We propose a novel method for designing the internal supporting frame structures of 3D objects based on their medial axis such that the objects are ...
Cost-effective printing of 3D objects with self-supporting property

The fused deposition modeling (FDM) printer is a simple, affordable and widely used device in the 3D printing society. However, the high price of printing materials is one of major restrictive factors for its further application. Based on the self-...
CeraMetal: A New Approach to Low-Cost Metal 3D Printing with Bronze Clay
CHI '24: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems

This paper introduces CeraMetal, a low-cost and robust approach to desktop metal 3D printing based on a custom "metal clay". We present three recipes for 3D printable bronze clay along with a workflow that includes print parameters and a sintering ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 34, Issue 6

November 2015

944 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/2816795

Issue’s Table of Contents

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 November 2015

Published in TOG Volume 34, Issue 6

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Science Foundation
HKSAR RGC General Research Fund (GRF)

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

112
Total Citations
View Citations
1,640
Total Downloads

Downloads (Last 12 months)94
Downloads (Last 6 weeks)8

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Kumar SKumar R(2025)A Comprehensive Study on Additive Manufacturing Techniques, Machine Learning Integration, and Internet of Things-Driven Sustainability OpportunitiesJournal of Materials Engineering and Performance10.1007/s11665-025-10757-xOnline publication date: 5-Mar-2025
https://doi.org/10.1007/s11665-025-10757-x
Wang TLi YLi TLiu BLi XZhang X(2025)Machine learning in additive manufacturing: enhancing design, manufacturing and performance prediction intelligenceJournal of Intelligent Manufacturing10.1007/s10845-025-02568-7Online publication date: 27-Jan-2025
https://doi.org/10.1007/s10845-025-02568-7
Vasileska EArgilovski ATomov MJovanoski BGecevska V(2024)Implementation of Machine Learning for Enhancing Lean Manufacturing Practices for Metal Additive ManufacturingIEEE Transactions on Engineering Management10.1109/TEM.2024.345964571(14836-14845)Online publication date: 2024
https://doi.org/10.1109/TEM.2024.3459645
Jin LZhai XWang KZhang KWu DNazir AJiang JLiao W(2024)Big data, machine learning, and digital twin assisted additive manufacturing: A reviewMaterials & Design10.1016/j.matdes.2024.113086244(113086)Online publication date: Aug-2024
https://doi.org/10.1016/j.matdes.2024.113086
LI CLI YYUAN SLI SZHU JZHANG W(2024)Strength-based concurrent design for structural topology and building orientation of additively manufactured lattice structureChinese Journal of Aeronautics10.1016/j.cja.2024.103373(103373)Online publication date: Dec-2024
https://doi.org/10.1016/j.cja.2024.103373
Hamrani AAgarwal AAllouhi AMcDaniel D(2024)Applying machine learning to wire arc additive manufacturing: a systematic data-driven literature reviewJournal of Intelligent Manufacturing10.1007/s10845-023-02171-835:6(2407-2439)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s10845-023-02171-8
Li HQi G(2024)Product packaging design for hybrid manufacturing process based on 3D printing and thermal environment sensorsThe International Journal of Advanced Manufacturing Technology10.1007/s00170-024-14759-2Online publication date: 1-Nov-2024
https://doi.org/10.1007/s00170-024-14759-2
Bacciaglia ALiverani ACeruti A(2024)Efficient part orientation algorithm for additive manufacturing in industrial applicationsThe International Journal of Advanced Manufacturing Technology10.1007/s00170-024-14039-z133:11-12(5443-5462)Online publication date: 2-Jul-2024
https://doi.org/10.1007/s00170-024-14039-z
Latorre EWhiting E(2023)Laser Polishing: A Postprocessing Protocol for Fused Deposition Modeling 3D Printed Parts Using Existing ToolingProceedings of the 8th ACM Symposium on Computational Fabrication10.1145/3623263.3629154(1-3)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3623263.3629154
Ibhadode OZhang ZSixt JNsiempba KOrakwe JMartinez-Marchese AEro OShahabad SBonakdar AToyserkani E(2023)Topology optimization for metal additive manufacturing: current trends, challenges, and future outlookVirtual and Physical Prototyping10.1080/17452759.2023.218119218:1Online publication date: 9-Mar-2023
https://doi.org/10.1080/17452759.2023.2181192
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Issue’s Table of Contents