SustainaPrint: Making the Most of Eco-Friendly Filaments
Name
3746059.3747640.pdf
Size
7.99 MB
Format
Adobe PDF
Checksum (MD5)
c6cd451edbbd3ece97f0527780220e1b
Author(s)
Perroni-Scharf, Maxine
Xiao, Jennifer
Paulin, Cole
Wang, Zhi Ray
Sethapakdi, Ticha
Abdullah, Muhammad
Baudisch, Patrick
Mueller, Stefanie
Date Issued
September 27, 2025
Publisher
ACM|The 38th Annual ACM Symposium on User Interface Software and Technology
Citation
Maxine Perroni-Scharf, Jennifer Xiao, Cole Paulin, Zhi Ray Wang, Ticha Sethapakdi, Muhammad Abdullah, Patrick Baudisch, and Stefanie Mueller. 2025. SustainaPrint: Making the Most of Eco-Friendly Filaments. In Proceedings of the 38th Annual ACM Symposium on User Interface Software and Technology (UIST '25). Association for Computing Machinery, New York, NY, USA, Article 104, 1–13.
Version
Final published version
Abstract
We present SustainaPrint, a system for integrating eco-friendly filaments into 3D printing without compromising structural integrity. While biodegradable and recycled 3D printing filaments offer environmental benefits, there is a trade-off in using them as they may suffer from degraded or unpredictable mechanical properties, which can limit their use in load-bearing applications. SustainaPrint addresses this by strategically assigning eco-friendly and standard filaments to different regions of a multi-material print—reinforcing the areas that are most likely to break with stronger material while maximizing the use of sustainable filament elsewhere. As eco-friendly filaments often do not come with technical datasheets, we also introduce a low-cost, at-home mechanical testing toolkit that enables users to evaluate filament strength before deciding if they want to use that filament in our pipeline. We validate SustainaPrint through real-world fabrication and mechanical testing, demonstrating its effectiveness across a range of functional 3D printing tasks.
Description
UIST ’25, Busan, Republic of Korea
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-ShareAlike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3746059.3747640
