Hello everyone, welcome to our virtual presentation on the unique digital fabricaiton workflows that we have been studying on #PlotterTwitter.
As digital fabrication tools grow in popularity, practitioners develop and share workflows for using these tools.
Traditional manufacturing workflows comprise Computer Aided Design (CAD), Computer aided manufacturing (CAM), and execution (CNC).
We term these steps the "canonical workflow."
In the canonical workflow, the software tools for defining geometry are distinct from the programs that tell the machine how to create that geometry.
This abstraction has been widely adopted by HCI fabrication researchers.
However, in non-traditional fabrication contexts this abstraction does not always fit.
In online communities such as #PlotterTwitter, artists create bespoke tools and share hardware and software “hacks.”
Practitioners engage in activities that cannot accurately be described as CAD or CAM, thus challenging the normative categories of digital fabrication.
Critically examining where practitioners are creating their own software and hardware tools would allow HCI researchers to understand how makers and artists are pushing the boundaries of what can be produced with these tools.
Our research examines the unique digital fabrication workflows displayed on #PlotterTwitter.
Our first research question is: How can we conceptualize the space of workflows on #PlotterTwitter beyond the canonical workflow?
And second: How does work from #PlotterTwitter question assumptions of who uses digital fabrication tools, and how?
To answer our research questions, we investigated how the canonical workflow steps could be applied to plotting workflows and identified areas where they failed to capture the activities happening on #Plottertwitter.
We developed a coding scheme to categorize workflow steps, and applied this coding scheme to tweets we collected using the Twitter streaming API.
#Plottertwitter artists display an incredibly diverse set of workflows for engaging with digital fabrication tools.
We observed these workflows to be much more complex and dynamic than the canonical cad/cam/cnc pipeline.
Artists navigated often unclear relationships between digital and material forms. For example, matching appropriate pens and pencils to different image colors required iteration and testing.
There are no “out of the box” programs for accomplishing this, so artists developed their own lightweight and flexible tools to aid in this process.
We found plottertwitter artists particularly interested in extremely niche workflows, like this one for creating plotted Magic the Gathering cards.
Despite plotters being exceptionally good at drawing vector graphics, we saw almost no use of traditional vector graphics programs such as Adobe Illustrator.
Instead, artists would create their own programs to generate graphics and string these tools together
They would also integrate tools such as image generators created by the community into their workflows.
The online community is extremely supportive of the spread of bespoke tools, and we found the hashtag full of questions and answers, recommendations, and resources.
On #PlotterTwitter, the workflow itself has become a medium of exploration and creation.
#PlotterTwitter shows that post-anthropocentric fabrication practice is thriving.
We call on HCI researchers to adopt a post-anthropocentric view of making by conceptualizing personal fabrication as more than a series of steps.
It is fundamentally more complex than that.
Reducing the complexity of a personal fabrication workflow to the vision where “data becomes things” leaves out the nuanced interactions between human expertise, machine idiosyncrasies, material properties, and code.
Abstracting a workflow to one where a human maintains seamless control over their tools is ultimately not how personal fabrication works in practice.
All of this leads us to an important concession.
As researchers and tool-builders, we should not assume that we can predict how people will use digital fabrication technology when they are exploring niche areas and developing novel workflows.
Instead, we should instead leave room for, and perhaps even encourage, niche and case-specific interpretations.
As HCI researchers, this takes the form of studying practitioners, developing flexible tools, and perhaps most importantly, refraining from assumptions about who produces and how.
Thank you for your attention, and we would like to thank all of the #plottertwitter community members whose work appeared in this paper.