1. Bought Materials: wood, tubing, metallic paints, gesso, 1/2″ diameter copper plumbing pipes, pipe cutter (the best cutting tool ever), marbles
2. Building 12″ x 24″ frame, laser-cutting pieces and figuring out the design to make a marble move through the pipes and system.
1. Finalizing Design.
2. Adding an interactive component to it, like a motion or touch sensor to trigger sounds.
3. Painting and piecing it together.
THE PIECE…so far with 1 week left to finish
TITLE: Pep Talk
DESCRIPTION: Non-linear storytelling using video and Eko Studio.
TITLE: Case by Case
DESCRIPTION: Nature of Code final project that distinguishes lowercase letters from uppercase letters for little kids to use. The idea was inspired by a children’s app called Endless Wordplay and from a parent-teacher meeting for my 3-year old. His teacher informed me that he was really good at identifying uppercase letters, but not lower case letters. In preparation towards the next steps of reading, deciphering lowercase letters is something that we needed to work on since most of reading is strings of lowercase letters. And for myself, in effort to grasp the idea of neural networks and machine learning, I decided to work with Shiffman’s neural network of handwritten numbers using the MNIST database and apply letters to his sketch. Eventually, I would like to apply that model to identify letters and numbers in graphic illustrations, photos and different typefaces.
1) In building upon Daniel Shiffman’s Neural Network example from Nature of Code, which was also based on Tariq Rashid’s Make Your Own Neural Network, I want to use a training set of handwritten letters to distinguish between upper and lowercase letters as an initial step. I eventually want to take photos or illustrations of letters and numbers, similar to pieces in 36 Days of Type below, and teach the neural network to identify the letter or number.
2) Converting a test image of a number illustration into a bitmap file to add to the training set. Converted the test image into a 28 x 28 pixel greyscale image in Photoshop and then used Python to extract the pixel values from the photo illustration.
from PIL import Image
im = Image.open('um_000000.png')
pixels = list(im.getdata())
3) Creating the upper and lowercase training set, adding that to the data folder and build…
PROJECT: Mechanical Reproductions
DESCRIPTION: Using Francis Picabia’s collage, Very Rare Picture on the Earth (1915) as a blueprint and building a 3D version using the laser cutters, the CNC router and 3D printers. Also planning on adding a moving mechanical feature to it using gears and marbles or ball bearings.
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STEP #1: The Sketch
I sketched Picabia’s painting in Illustrator keeping in mind that this 2d vector drawing would be laser-cutted and possibly 3D printed for some parts. I wanted to build a drawing of a structure from scratch that symbolizes a working system (ex: filtration of data or the human body), but I found it helpful to replicate and work off Picabia’s design. I will eventually adjust things to make the gears and tubes more functional to create movement of a ball or marble inside.
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STEP #2: Materials
- Base/ Frame: To create the base and frame by layering wood on top of another and cutting it on a laser printer or using the CNC machine.
- Cylindrical Pieces: Using the CNC machine to make the cylindrical pieces.
- Gears and Flat Pieces: Laser cutting them from wood and adding metallic colors of silver and copper to make them more metal-like.
- Metallic Finishes: Going to look into gold leaf or patinas, but bought some metallic paints to also test on the wood.
FINAL DESIGN: 3-piece puzzle of a tree maze (Top Cover, Middle, Bottom)
– 2 player game to race against one another
– Pieces can interlock together and contain metal ball bearings for the game
After we figured out the design for the tree maze, our next steps were to see how our design would translate into the 3D print using the LuxzBot Taz and UltiMaker machines.
1.) INITIAL 3D TEST PRINT: In order to see how the specs would translate to the printed form, we printed our initial design file that we were playing with. We were able to figure out how to take the Tinkercad file, save it as a (.stl) file, load it into Cura to create the (.gcode) file, and then transfer (.gcode) file to an SD card to print on the LuxzBot Taz machine. The total print time for this piece was 2 hours. The main problem from that we ran into with this test run was the depressions printed raised lines, so the extruded parts of the opposite piece could not interlock to close.
2.) MAKING ADJUSTMENTS: We needed to adjust the files to make the depressed line strokes thicker and deeper so the walls of the maze could fit right in. Also from class feedback after showing our sketch in class, one suggestion was to make the maze more in-tune with the concept of a ‘tree’, so Jenn made the lines inside the maze more branch and leaf-like.
NEW TREE DESIGN
CURA VIEW – 3 pieces (bottom, middle, top)
3.) 3D PRINTING PROBLEMS: We made the pieces smaller hoping to lesson the print time, which for this job was estimated at 3 hours. The problem with this was that the pieces were lifting off from the bed and moving around. We tried printing these files a couple times by making the file bigger and waiting for the machines to cool down. During these trials, we noticed that the different color plastics also affected how hard and soft the 3D piece was. We had more success printing with the opaque white plastic, as opposed to the clear and black plastics that were more flimsier.
Prints lifting and moving from the bed
Many failed 3D printing jobs
4.) RAFTS AND BRIMS: While trying to solve the problem with the plastic lifting from the bed, we tried adding a raft and a brim in Cura to help keep the piece down as it printed. It still didn’t work well on the LuxzBot Taz, but when we used the UltiMaker machine, the print was able to finish without lifting off from the bed.
Printing on Ultimaker with raft around design
For this week’s assignment, I decided to think of a dataset that I would like to use with a supervised learning machine learning algorithm and want to concentrate on finding patterns and similarity within images. At first I looked at the Instagram API because I wanted to try to find ‘magazine covers’ from the feed without hashtags or geotags, but since the Instagram users own their images, there seems to be a lot of restrictions. So I remembered from another class that the Smithsonian API could also be used to explore the images and through that I found the Cooper Hewitt API, which seems to format the data as JSON files. Below is an example of how they used colors to classify the pieces in the museum.
Image Recognition & Visual Arts
DATA ART #3: Place & Space
Paths in Space – Melissa’s Week on the Moon
Melissa and I exchanged OpenPaths data and used it to create our own interpretation of each other’s week in outer space. Below is Melissa’s week on the moon visualized.
MoonPath from annemgal on Vimeo.
MoonPathAbstract from annemgal on Vimeo.