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Machine settings and tuning are critical for digital fabrication outcomes. However, exploring these parameters is non-trivial. We seek to enable exploration of the full design space of digital fabrication. To identify where we might intervene, we studied how practitioners approach 3D printing. We found that beyond using CAD/CAM, they create bespoke routines and workflows to explore interdependent material and machine settings. We seek to provide a system that supports this workflow development. We identified design goals around material exploration, fine-tuned control, and iteration. Based on these, we present p5.fab, a system for controlling digital fabrication machines from the creative coding environment p5.js. We demonstrate p5.fab with examples of 3D prints that cannot be made with traditional 3D printing software. We evaluate p5.fab in workshops and find that it encourages novel printing workflows and artifacts. Finally, we discuss implications for future digital fabrication systems.
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Tools, Tricks, and Hacks: Exploring Novel Digital Fabrication Workflows on #PlotterTwitter
As digital fabrication machines become widespread, online communities have provided space for diverse practitioners to share their work, troubleshoot, and socialize. These communities pioneer increasingly novel fabrication workflows, and it is critical that we understand and conceptualize these workflows beyond traditional manufacturing models. To this end, we conduct a qualitative study of #PlotterTwitter, an online community developing custom hardware and software tools to create artwork with computer-controlled drawing machines known as plotters. We documented and analyzed emergent themes where the traditional interpretation of digital fabrication workflows fails to capture important nuances and nascent directions. We find that #PlotterTwitter makers champion creative exploration of interwoven digital and physical materials over a predictable series of steps. We discuss how this challenges long-running views of digital fabrication and propose design implications for future frameworks and toolkits to account for this breadth of practice.
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- PAR ID:
- 10273544
- Date Published:
- Journal Name:
- Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
- Page Range / eLocation ID:
- 1 to 15
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3411764.3445653
