Tanks of Gel Make 3-D Printing Fast and Easy

Time is a 3-D printer’s worst enemy. The machines work by piping out the printed object's material in thin layers, and have to wait for each layer to dry before adding the next one. If they didn't have to wait for the material to dry, a printer could construct items like furniture in a matter of minutes instead of hours. A team at MIT believes they found a way around this issue using giant tanks of gel.

As Co.Design reports, a new technique called Rapid Liquid Printing doesn’t require any layering. Instead, a needle injects the material (either liquified rubber, foam, or plastic) directly into the gel. The gel supports the hot liquid so the machine is able to move on with the rest of the design without waiting for the structure to harden.

MIT’s Self-Assembly Lab developed the technology after they were approached by the office furniture company Steelcase last year. Yuka Hiyoshi, a senior industrial designer for Steelcase, compared the method they landed on to “calligraphy or drawing” when speaking to Co.Design.

The Self Assembly Lab isn’t the first group to come up with a rapid 3-D printing process. The continuous liquid interface production (CLIP) method from Carbon3D uses oxygen and UV light to liquify and harden materials instantaneously. But while that technology is good for printing small, detailed objects in a few minutes, MIT’s printer is ideal printing larger items in the same time frame.

Rapid Liquid Printing works with any item that fits in a vat. Furniture for Steelcase, specifically tops for coffee tables, has been the first big experiment for the printers. Results have been promising, but the company doesn’t plan to integrate the technology into its commercial products just yet. Manufacturing car and plane parts is another possible application for the machine.

[h/t Co.Design]

Watch an Antarctic Minke Whale Feed in a First-of-Its-Kind Video

New research from the World Wildlife Fund is giving us a rare glimpse into the world of the mysterious minke whale. The WWF worked with Australian Antarctic researchers to tag minke whales with cameras for the first time, watching where and how the animals feed.

The camera attaches to the whale's body with suction cups. In the case of the video below, the camera accidentally slid down the side of the minke whale's body, providing an unexpected look at the way its throat moves as it feeds.

Minke whales are one of the smallest baleen whales, but they're still pretty substantial animals, growing 30 to 35 feet long and weighing up to 20,000 pounds. Unlike other baleen whales, though, they're small enough to maneuver in tight spaces like within sea ice, a helpful adaptation for living in Antarctic waters. They feed by lunging through the sea, gulping huge amounts of water along with krill and small fish, and then filtering the mix through their baleen.

The WWF video shows just how quickly the minke can process this treat-laden water. The whale could lunge, process, and lunge again every 10 seconds. "He was like a Pac-Man continuously feeding," Ari Friedlaender, the lead scientist on the project, described in a press statement.

The video research, conducted under the International Whaling Commission's Southern Ocean Research Partnership, is part of WWF's efforts to protect critical feeding areas for whales in the region.

If that's not enough whale for you, you can also watch the full 13-minute research video below:

AI Could Help Scientists Detect Earthquakes More Effectively

Thanks in part to the rise of hydraulic fracturing, or fracking, earthquakes are becoming more frequent in the U.S. Even though it doesn't fall on a fault line, Oklahoma, where gas and oil drilling activity doubled between 2010 and 2013, is now a major earthquake hot spot. As our landscape shifts (literally), our earthquake-detecting technology must evolve to keep up with it. Now, a team of researchers is changing the game with a new system that uses AI to identify seismic activity, Futurism reports.

The team, led by deep learning researcher Thibaut Perol, published the study detailing their new neural network in the journal Science Advances. Dubbed ConvNetQuake, it uses an algorithm to analyze the measurements of ground movements, a.k.a. seismograms, and determines which are small earthquakes and which are just noise. Seismic noise describes the vibrations that are almost constantly running through the ground, either due to wind, traffic, or other activity at surface level. It's sometimes hard to tell the difference between noise and legitimate quakes, which is why most detection methods focus on medium and large earthquakes instead of smaller ones.

But better understanding natural and manmade earthquakes means studying them at every level. With ConvNetQuake, that could soon become a reality. After testing the system in Oklahoma, the team reports it detected 17 times more earthquakes than what was recorded by the Oklahoma Geological Survey earthquake catalog.

That level of performance is more than just good news for seismologists studying quakes caused by humans. The technology could be built into current earthquake detection methods set up to alert the public to dangerous disasters. California alone is home to 400 seismic stations waiting for "The Big One." On a smaller scale, there's an app that uses a smartphone's accelerometers to detect tremors and alert the user directly. If earthquake detection methods could sense big earthquakes right as they were beginning using AI, that could afford people more potentially life-saving moments to prepare.

[h/t Futurism]


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