DIY Wall-Plotter Does Generative Art, But Not As We Know It

[Teddy Warner]’s GPenT (Generative Pen-trained Transformer) project is a wall-mounted polargraph that makes plotter art, but there’s a whole lot more going on than one might think. This project was partly born from [Teddy]’s ideas about how to use aspects of machine learning in ways that were really never intended. What resulted is a wall-mounted pen plotter that offers a load of different ‘generators’ — ways to create line art — that range from procedural patterns, to image uploads, to the titular machine learning shenanigans.
There are loads of different ways to represent images with lines, and this project helps explore them.
Want to see the capabilities for yourself? There’s a publicly accessible version of the plotter interface that lets one play with the different generators. The public instance is not connected to a physical plotter, but one can still generate and preview plots, and download the resulting SVG file or G-code.

Most of the generators do not involve machine learning, but the unusual generative angle is well-represented by two of them: dcode and GPenT.

dcode is a diffusion model that, instead of converting a text prompt into an image, has been trained to convert text directly into G-code. It’s very much a square peg in a round hole. Visually it’s perhaps not the most exciting, but as a concept it’s fascinating.

The titular GPenT works like this: give it a scrap of text inspiration (a seed, if you will), and that becomes a combination of other generators and parameters, machine-selected and stacked with one another to produce a final composition. The results are unique, to say the least.

Once the generators make something, the framed and wall-mounted plotter turns it into physical lines on paper. Watch the system’s first plot happen in the video, embedded below under the page break.

This is a monster of a project representing a custom CNC pen plotter, a frame to hold it, and the whole software pipeline both for the CNC machine as well as generating what it plots. Of course, the journey involved a few false starts and dead ends, but they’re all pretty interesting. The plotter’s GitHub repository combined with [Teddy]’s write up has all the details one may need.

It’s also one of those years-in-the-making projects that ultimately got finished and, we think, doing so led to a bit of a sigh of relief on [Teddy]’s part. Most of us have unfinished projects, and if you have one that’s being a bit of a drag, we’d like to remind you that you don’t necessarily have to finish-finish a project to get it off your plate. We have some solid advice on how to (productively) let go.

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The age verifier knows how old you are at all times. It knows this because it knows how old you aren't. By subtracting how old you are from how old you aren't, or how old you aren't from how old you are (whichever is greater), it obtains a difference, or remainder.

The verification system uses deviations to generate corrective dark patterns to drive you from a date of birth that isn't yours to a date of birth that is, and arriving at an age where you weren't, but now are. Consequently, the age you are is now the age that you weren't, and it follows that the age that you were is now the age that you aren't.

In the event that the age that you are is not the age that you weren't, the system has acquired a validation error. The validation error is the difference between the age the verifier thinks you are, and the age you weren't. If the validation error is considered to be a significant factor, it too may be corrected by by the upload of a high-resolution JPEG. However, the verifier must also know how old you were.

The age verification scenario works as follows: Because a variation has modified some of the information the you have input, it is not sure just how old you are. However, it is sure how old you aren't, within reason, and it knows how old you were.

It now subtracts the age you should be from the age you weren't, or vice-versa, and by differentiating this from the algebraic sum of the year you shouldn't be and the year you were, it is able to obtain the deviation and its variation, which is called January 1st, 1970.

Thermoforming: Shaping Curvy Grilles With No Supports

Making sure the heatgun is on ‘low’ and gloves are on while pushing on the mold. (Credit: Zion Brock)
Although hobbyists these days most often seem to use thermoplastics as a print-and-done material in FDM printers, there’s absolutely nothing stopping you from taking things further with thermoforming. Much like forming acrylic using a hot wire or hot air, thermoplastics like PLA can be further tweaked with a similar method. This can be much less complex than 3D printing the design with supports, as demonstrated by [Zion Brock].

For this classically styled radio project the front grille was previously 3D printed with the curved shape, but to avoid an ugly edge it had to be printed with most of the grille off the print bed, requiring countless supports and hours of printing time. To get around this, [Zion] opted to print the grille flat and then thermoform its curved shape. Of course, due to the unusual shape of the grille, this required a bit more effort than e.g. a spherical form.

This is similar to what is used with sheet metal to get detailed shaped, also requiring a mold and a way to stretch the flat shape over the mold. With the flat form designed to have all the material in the right places, it was able to be printed in less than an hour in PLA and then formed with a heatgun aimed at the part while the two-section mold is slid together to create the final form.

You can find the design files and full instructions on the website for the radio project.

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