Improved And Open Source: Non-Planar Infill For FDM

April 23, 2025 by Heidi Ulrich 13 Comments

Strenghtening FDM prints has been discussed in detail over the last years. Solutions and results vary as each one’s desires differ. Now [TenTech] shares his latest improvements on his post-processing script that he first created around January. This script literally bends your G-code to its will – using non-planar, interlocking sine wave deformations in both infill and walls. It’s now open-source, and plugs right into your slicer of choice: PrusaSlicer, OrcaSlicer, or Bambu Studio. If you’re into pushing your print strength past the limits of layer adhesion, but his former solution wasn’t quite the fit for your printer, try this improvement.

Traditional Fused Deposition Modeling (FDM) prints break along layer lines. What makes this script exciting is that it lets you introduce alternating sine wave paths between wall loops, removing clean break points and encouraging interlayer grip. Think of it as organic layer interlocking – without switching to resin or fiber reinforcement. You can tweak amplitude, frequency, and direction per feature. In fact, the deformation even fades between solid layers, allowing smoother transitions. Structural tinkering at its finest, not just a cosmetic gimmick.

This thing comes without needing a custom slicer. No firmware mods. Just Python, a little G-code, and a lot of curious minds. [TenTech] is still looking for real-world strength tests, so if you’ve got a test rig and some engineering curiosity, this is your call to arms.

The script can be found in his Github. View his full video here , get the script and let us know your mileage!

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Writing A GPS Receiver From Scratch

March 18, 2025 by Bryan Cockfield 21 Comments

GPS is an incredible piece of modern technology. Not only does it allow for locating objects precisely anywhere on the planet, but it also enables the turn-by-turn directions we take for granted these days — all without needing anything more than a radio receiver and some software to decode the signals constantly being sent down from space. [Chris] took that last bit bit as somewhat of a challenge and set off to write a software-defined GPS receiver from the ground up.

As GPS started as a military technology, the level of precision needed for things like turn-by-turn navigation wasn’t always available to civilians. The “coarse” positioning is only capable of accuracy within a few hundred meters so this legacy capability is the first thing that [Chris] tackles here. It is pretty fast, though, with the system able to resolve a location in 24 seconds from cold start and then displaying its information in a browser window. Everything in this build is done in Python as well, meaning that it’s a great starting point for investigating how GPS works and for building other projects from there.

The other thing that makes this project accessible is that the only other hardware needed besides a computer that runs Python is an RTL-SDR dongle. These inexpensive TV dongles ushered in a software-defined radio revolution about a decade ago when it was found that they could receive a wide array of radio signals beyond just TV.

Import GPU: Python Programming With CUDA

February 25, 2025 by Bryan Cockfield 17 Comments

Every few years or so, a development in computing results in a sea change and a need for specialized workers to take advantage of the new technology. Whether that’s COBOL in the 60s and 70s, HTML in the 90s, or SQL in the past decade or so, there’s always something new to learn in the computing world. The introduction of graphics processing units (GPUs) for general-purpose computing is perhaps the most important recent development for computing, and if you want to develop some new Python skills to take advantage of the modern technology take a look at this introduction to CUDA which allows developers to use Nvidia GPUs for general-purpose computing.

Of course CUDA is a proprietary platform and requires one of Nvidia’s supported graphics cards to run, but assuming that barrier to entry is met it’s not too much more effort to use it for non-graphics tasks. The guide takes a closer look at the open-source library PyTorch which allows a Python developer to quickly get up-to-speed with the features of CUDA that make it so appealing to researchers and developers in artificial intelligence, machine learning, big data, and other frontiers in computer science. The guide describes how threads are created, how they travel along within the GPU and work together with other threads, how memory can be managed both on the CPU and GPU, creating CUDA kernels, and managing everything else involved largely through the lens of Python.

Getting started with something like this is almost a requirement to stay relevant in the fast-paced realm of computer science, as machine learning has taken center stage with almost everything related to computers these days. It’s worth noting that strictly speaking, an Nvidia GPU is not required for GPU programming like this; AMD has a GPU computing platform called ROCm but despite it being open-source is still behind Nvidia in adoption rates and arguably in performance as well. Some other learning tools for GPU programming we’ve seen in the past include this puzzle-based tool which illustrates some of the specific problems GPUs excel at.

Cyanotype Prints On A Resin 3D Printer

February 22, 2025 by Tom Nardi 18 Comments

Not that it’s the kind of thing that pops into your head often, but if you ever do think of a cyanotype print, it probably doesn’t conjure up thoughts of modern technology. For good reason — the monochromatic technique was introduced in the 1840s, and was always something of a niche technology compared to more traditional photographic methods.

The original method is simple enough: put an object or negative between the sun and a UV-sensitive medium, and the exposed areas will turn blue and produce a print. This modernized concept created by [Gabe] works the same way, except both the sun and the negative have been replaced by a lightly modified resin 3D printer.

A good chunk of the effort here is in the software, as [Gabe] had to write some code that would take an image and turn it into something the printer would understand. His proof of concept was a clever bit of Python code that produced an OpenSCAD script, which ultimately converted each grayscale picture to a rectangular “pixel” of variable height. The resulting STL files could be run through the slicer to produce the necessary files to load into the printer. This was eventually replaced with a new Python script capable of converting images to native printer files through UVtools.

On the hardware side, all [Gabe] had to do was remove the vat that would usually hold the resin, and replace that with a wooden lid to both hold the UV-sensitized paper in place and protect the user’s eyes. [Gabe] says there’s still some room for improvement, but you wouldn’t know it by looking at some of the gorgeous prints he’s produced already.

No word yet on whether or not future versions of the project will support direct-to-potato imaging.

Could Non-Planar Infill Improve The Strength Of Your 3D Prints?

February 1, 2025 by Lewin Day 24 Comments

When you’re spitting out G-Code for a 3D print, you can pick all kinds of infill settings. You can choose the pattern, and the percentage… but the vast majority of slicers all have one thing in common. They all print layer by layer, infill and all. What if there was another way?

There’s been a lot of chatter in the 3D printing world about the potential of non-planar prints. Following this theme, [TenTech] has developed a system for non-planar infill. This is where the infill design is modulated with sinusoidal waves in the Z axis, such that it forms a somewhat continuous bond between what would otherwise be totally seperate layers of the print. This is intended to create a part that is stronger in the Z direction—historically a weakness of layer-by-layer FDM parts.

Files are on Github for the curious, and currently, it only works with Prusaslicer. Ultimately, it’s interesting work, and we can’t wait to see where it goes next. What we really need is a comprehensive and scientific test regime on the tensile strength of parts printed using this technique. We’ve featured some other neat work in this space before, too. Video after the break.

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Robot Air Hockey Player Predicts Your Next Move

December 16, 2024 by Lewin Day 21 Comments

Air hockey is a fun game, but it’s one you can’t play by yourself. That is, unless you have a smart robot hockey player to act as your rival. [Zeroshot] built exactly that.

The build is based around a small 27-inch air hockey table—not exactly arcade-spec, but big enough to demonstrate the concepts at play. The robot player moves its mallet in the X and Y axes using a pair of NEMA17 stepper motors and an H-belt configuration. To analyze the game state, there’s a Raspberry Pi 3B fitted with a camera, and it has a top-down view of the board. The Pi gives the stepper motors commands on how to move the mallet via an Arduino that communicates with the stepper drivers. The Pi doesn’t just aim for the puck itself, either. With Python and OpenCV, it tries to predict your own moves by tracking your mallet, and the puck, too. It predicts the very-predictable path of the puck, and moves itself to the right position for effective defence.

Believe it or not, we’ve featured quite a few projects in this vein before. They’ve all got their similarities, and their own unique quirks. Video after the break.
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Lasers, Galvos, Action: A Quest For Laser Mastery

November 22, 2024 by Heidi Ulrich 1 Comment

If you’re into hacking hardware and bending light to your will, [Shoaib Mustafa]’s latest project is bound to spike your curiosity. Combining lasers to project multi-colored beams onto a screen is ambitious enough, but doing it with a galvanomirror, STM32 microcontroller, and mostly scratch-built components? That’s next-level tinkering. This project isn’t just a feast for the eyes—it’s a adventure of control algorithms, hardware hacks, and the occasional ‘oops, that didn’t work.’ You can follow [Shoaib]’s build log and join the journey here.

The nitty-gritty is where it gets fascinating. Shoaib digs into STM32 Timers, explaining how modes like Timer, Counter, and PWM are leveraged for precise control. From adjusting laser intensity to syncing galvos for projection, every component is tuned for maximum flexibility. Need lasers aligned? Enter spectrometry and optical diffusers for precision wavelength management. Want real-time tweaks? A Python-controlled GUI handles the instruments while keeping the setup minimalist. This isn’t just a DIY build—it’s a work of art in problem-solving, with successes like a working simulation and implemented algorithms along the way.

If laser projection or STM32 wizardry excites you, this build will inspire. We featured a similar project by [Ben] back in September, and if you dig deep into our archives, you can eat your heart out on decades of laser projector projects. Explore Shoaib’s complete log on Hackaday.io. It is—literally—hacking at its most brilliant.