MultiFab: a machine vision assisted platform for multi-material 3D printing
Name
Matusik_MultiFab.pdf
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24.78 MB
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Adobe PDF
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Author(s)
Sitthi-Amorn, Pitchaya
Ramos, Javier E.
Wangy, Yuwang
Kwan, Joyce
Lan, Justin
Wang, Wenshou
Matusik, Wojciech
Date Issued
August 2015
Journal
ACM Transactions on Graphics
Publisher
Association for Computing Machinery (ACM)
Citation
Pitchaya Sitthi-Amorn, Javier E. Ramos, Yuwang Wangy, Joyce Kwan, Justin Lan, Wenshou Wang, and Wojciech Matusik. 2015. MultiFab: a machine vision assisted platform for multi-material 3D printing. ACM Trans. Graph. 34, 4, Article 129 (July 2015), 11 pages.
Version
Author's final manuscript
Abstract
We have developed a multi-material 3D printing platform that is high-resolution, low-cost, and extensible. The key part of our platform is an integrated machine vision system. This system allows for self-calibration of printheads, 3D scanning, and a closed-feedback loop to enable print corrections. The integration of machine vision with 3D printing simplifies the overall platform design and enables new applications such as 3D printing over auxiliary parts. Furthermore, our platform dramatically expands the range of parts that can be 3D printed by simultaneously supporting up to 10 different materials that can interact optically and mechanically. The platform achieves a resolution of at least 40 μm by utilizing piezoelectric inkjet printheads adapted for 3D printing. The hardware is low cost (less than $7,000) since it is built exclusively from off-the-shelf components. The architecture is extensible and modular -- adding, removing, and exchanging printing modules can be done quickly. We provide a detailed analysis of the system's performance. We also demonstrate a variety of fabricated multi-material objects.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1145/2766962
