To 3D print concrete, several parameters must be met. It must be able to be extruded through a nozzle, hold its shape once deposited, and also be …
Metamaterials, which can morph according to their environment, make up a new class of 3D printable, engineered surfaces which can perform nature-defying tasks, like making holograms and shaping sound. Recently, a collaborative team led by researchers from the USC Viterbi School of Engineering created new 3D printed acoustic metamaterials that are able to be remotely switched on and off, using a magnetic field, between active control and passive states.
This makes it possible to control vibration and sound, which other researchers have been trying unsuccessfully to do with abnormal property-exhibiting structures. The difference is that those metamaterials are built in fixed geometries, so their abilities will also remain fixed.
“When you fabricate a structure, the geometry cannot be changed, which means the property is fixed,” explained Qiming Wang, USC Viterbi Assistant Professor of Civil and Environmental Engineering. “The idea here is, we can design something very flexible so that you can change it using external controls.”
Wang, together with USC Viterbi PhD student Kun-Hao Yu, University of Missouri Professor Guoliang Huang, and MIT Professor Nicholas X. Fang, whose work with 3D metamaterials we’re familiar with, have developed 3D printed metamaterials that can block both mechanical vibrations and sound waves. This opens up applications in vibration control, noise cancellation, and sonic cloaking (used to hide objects from acoustic waves), because, unlike current metamaterials, these can be controlled remotely with a magnetic field.
Yu said, “Traditional engineering materials may only shield from acoustics and vibrations, but few of them can switch between on and off.”
Yu, Fang, Huang, and Wang, whose research was funded by the National Science Foundation and the Air Force Office of Scientific Research Young Investigator Program, recently published a paper, titled “Magnetoactive Acoustic Metamaterials,” in the Advanced Materials journal.
The abstract reads, “In conventional acoustic metamaterials, the negative constitutive parameters are engineered via tailored structures with fixed geometries; therefore, the relationships between constitutive parameters and acoustic frequencies are typically fixed to form a 2D phase space once the structures are fabricated. Here, by means of a model system of magnetoactive lattice structures, stimuli‐responsive acoustic metamaterials are demonstrated to be able to extend the 2D phase space to 3D through rapidly and repeatedly switching signs of constitutive parameters with remote magnetic fields. It is shown for the first time that effective modulus can be reversibly switched between positive and negative within controlled frequency regimes through lattice buckling modulated by theoretically predicted magnetic fields.”
Metamaterials can manipulate wave phenomena, like light, radar, and sound, which helps create technology like cloaking devices. Environmental sounds and structural vibrations, which have similar waveforms, can now be controlled by the team’s unique metamaterials. These can be compressed, but not constrained, with a magnetic field by 3D printing a deformable material, which contains iron particles, in a lattice structure. So, when a mechanical or acoustic wave makes contact with the 3D printed metamaterial, it disturbs it, which then produces the properties that can block certain frequencies of mechanical vibrations and sound waves.
“You can apply an external magnetic force to deform the structure and change the architecture and the geometry inside it,” said Wang. “Once you change the architecture, you change the property. We wanted to achieve this kind of freedom to switch between states. Using magnetic fields, the switch is reversible and very rapid.”
In order to work, the mechanism needs the negative modulus and density of the metamaterials; these are both positive in regular materials. An object will typically push back against you if you push it, but objects with a negative modulus pull you forward as you push; objects with negative density move toward you when you push them.
Yu explained, “Material with a negative modulus or negative density can trap sounds or vibrations within the structure through local resonances so that they cannot transfer through it.”
Just one negative property, be it density or modulus, is able to independently block vibrations and noise within certain frequencies, but these can pass through if the two negative properties work together. By switching the magnetic field, the researchers have versatile control and can switch the metamaterial between double-positive (sound passing), single-negative (sound blocking), and double-negative (sound passing again).
Wang said, “This is the first time researchers have demonstrated reversible switching among these three phases using remote stimuli.”
The team’s current system can only 3D print metamaterials with beam diameters between one micron and one millimeter, so it either needs to grow or shrink. Larger beams would affect lower frequency waves, while smaller ones would control waves of higher frequencies.
“There are indeed a number of possible applications for smartly controlling acoustics and vibrations. Traditional engineering materials may only shield from acoustics and vibrations, but few of them can switch between on and off,” Yu said.
Now, Wang thinks the team could get their metamaterial to demonstrate another unique property – negative refraction, or “anti-physics,” where a wave goes through the material and comes back in at an unnatural angle. Once the researchers manage to 3D print larger structures, they’ll focus more on studying this phenomenon.
“We want to scale down or scale up our fabrication system. This would give us more opportunity to work on a larger range of wavelengths,” Wang said.
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[Source/Images: USC Viterbi]
The Ames Laboratory, a national laboratory with the US Department of Energy’s Office of Science and operated by Iowa State University, works to solve our world’s pressing issues with innovative energy materials, solutions, and technologies. While the laboratory spent a lot of time in 2017 focusing on its metal 3D printing powder technology and 3D printed chemically active catalytic objects, now researchers are taking a closer look at magnetocaloric cooling.
Scientists have been working to develop new technologies, such as solid-state systems with up to 30% more energy efficiency, to replace gas compression refrigeration technology that’s 100 years old. The magnetocaloric effect is a phenomenon in which a suitable material goes through a temperature change caused by exposure to a changing magnetic field.
Researchers at Ames Laboratory designed and built an advanced model system, with the help of 3D printing technology, that can successfully achieve refrigeration-level cooling using very small quantities of magnetocaloric materials.
The system is called CaloriSMART, or Small Modular Advanced Research-scale Test-station, and is a research thrust of CaloriCool – The Caloric Materials Consortium.
“Despite predictions we would fail because of anticipated inefficiencies and losses, we always believed it would work, but we were pleasantly surprised by just how well it worked,” said CaloriCool project director and Ames Laboratory scientist Vitalij Pecharsky. “It’s a remarkable system and it performs exceptionally well. Magnetic refrigeration near room temperature has been broadly researched for 20 years, but this is one of the best systems that has been developed.”
“But the main reason we conceived and built CaloriSMART is to accelerate design and development of caloric materials so they can be moved into the manufacturing space at least two to three times faster compared to the 20 or so years it typically takes today.”
Caloric cooling is a different way of looking at refrigeration technology, and is the science behind CaloriCool, which is sponsored by the DOE’s Office of Energy Efficiency and Renewable Energy through its Advanced Manufacturing Office. The research collaboration is led by the Ames Laboratory and was established as part of the Energy Materials Network.
The CaloriSMART system, which took about five months to build, was specifically developed to enable rapid evaluation of materials in regenerators (regenerative heat exchangers) without having to invest a lot of manufacturing or time.
Pecharsky, also an Anston Marston Distinguished Professor in the university’s Department of Materials Science and Engineering, credits project scientist Julie Slaughter and her team for the system’s design, which includes a custom 3D printed manifold that holds gadolinium samples and circulates the actual fluid that, according to the laboratory, “harnesses the system’s cooling power.”
“We only need 2-5 cubic centimeters of sample material – in most cases about 15-25 grams. We are setting the benchmark with gadolinium and we know there are other materials that will perform even better. And our system should be scalable (for commercial cooling) in the future,” Slaughter explained.
Gadolinium is a malleable and ductile rare earth metal, found in nature only in oxidized form. The first test of the CaloriSMART system administered a sample of three cubic centimeters of gadolinium to sequential magnetic fields, which caused it to switch back and forth between cooling down and heating up. During these cycles, the system used well-timed pumps to circulate water, which allowed it to demonstrate a sustained cooling power of 10 watts and a 15°C gradient between the hot and cold ends.
Customized neodymium-iron-boron magnets are also included in the CaloriSMART system, and are able to send a concentrated 1.4 Tesla magnetic field right to its circulating, precision in-line pumping system, and to the sample itself.
Now that CaloriSmart has achieved successful magnetocaloric testing, the research team plans to upgrade the system with electrocaloric materials, which reversibly heat up and cool down when subjected to a changing electric field, as well as elastocaloric materials, which behave in a similar fashion but when cyclic tension or compression is administered. The CaloriSmart system will even be able to operate in an innovative combined-field mode, which allows for the simultaneous use of a combination of techniques.
“There are a handful of places studying elastocaloric and electrocaloric materials, but nobody has all three in one place and our system now gives us that capability,” said Pecharsky.
3D printing technology has the ability to achieve unique, custom shapes at a faster rate of time, which is why we’ve often seen 3D printed manifolds put to work before in cars, vintage fire engines, and even ventilators. Now, we can add a magnetocaloric cooling system to the list.
Discuss this and other 3D topics at 3DPrintBoard.com or share your thoughts below.
[Source/Images: Ames Laboratory]
Nov 2, 2017 | By Benedict
Researchers at MIT have carried out an investigation into “adversarial examples,” objects that can fool AI vision into thinking an object is something completely different. The researchers made a 3D printed turtle that fooled Google’s Inception-v3 into thinking it was a gun, even from multiple angles.
Take a look at the 3D printed turtle above, and you’ll be hard pressed to find anything particularly threatening about it. Perhaps the 3D printing filament used to make it was slightly toxic, but ultimately, it’s just a plastic turtle.
That’s not how Google’s Inception-v3 AI image classifier sees it though. Through the eyes of the artificial intelligence system, that innocent-looking 3D printed sea creature looks just like a rifle.
The 3D printed prop is what is known as an adversarial example—something designed to trick an artificial intelligence system into thinking it’s something else entirely. In this instance, MIT researchers engineered the plastic turtle to make Google’s AI see it as a dangerous weapon.
It’s obviously quite funny on some level: who knows how many millions of dollars are being pumped into image classification systems, yet some still think a plastic toy is a rifle. It’s the same impressive yet amusing quality that made those nightmarish Google DeepDream pictures so mesmerizing.
But adversarial objects—or adversarial images in the 2D world—are actually highly significant, and potentially very troublesome.
AI neural network systems like Google’s Inception-v3 are, of course, incredibly smart. But they work on complex, human-made algorithms, not common sense. And if you’re familiar with the precise rules and logic behind an AI system, you can potentially exploit it.
Because Google’s Inception-v3 is open source, the MIT researchers—Anish Athalye, Logan Engstrom, Andrew Ilyas, and Kevin Kwok, who are together known as “labsix”—were in the perfect position to exploit it, by looking at the exact criteria for “rifle” recognition and trying to somehow squeeze those characteristics into something not very rifle-like at all: a turtle.
The MIT researchers aren’t the first to create adversarial objects, of course. People can use certain tricks to fool facial recognition systems into misidentifying a person—something that border security services, for example, are currently trying to curtail.
For most adversarial objects or images, however, the “trick” only works from certain angles. You might fool a neural network into thinking a bag of chips is a face from a certain angle, but move it around slightly and the AI will likely correct its mistake.
But the 3D printed turtle, as well as the MIT researchers’ other 3D examples, are different. They actually fool the Google AI from multiple angles, rather than just one, making them far more devastating than your typical adversarial object.
In addition to the turtle that seems like a rifle, labsix also 3D printed a baseball that gets recognized as espresso. They also made digital models of a barrel that gets interpreted as a guillotine, a baseball that can appear like a green lizard, a dog that the AI thinks is a bittern, and several other examples.
The researchers were able to easily make more examples of these objects after creating an algorithm for “reliably producing physical 3D objects that are adversarial from every viewpoint,” working at almost 100 per cent accuracy. They call this algorithm “Expectation Over Transformation” (EOT).
In a sense, the team is pleased with its achievements, but it’s also worried by how easily it managed to pull it off.
“[EOT] shouldn’t be able to take an image, slightly tweak the pixels, and completely confuse the network,” Athalye told Quartz. “Neural networks blow all previous techniques out of the water in terms of performance, but given the existence of these adversarial examples, it shows we really don’t understand what’s going on.”
Of course, this isn’t just a bit of fun for the MIT researchers. They believe that their research proves beyond doubt that “adversarial examples are a practical concern for real-world systems.”
If, say, hackers were able to ascertain the complex algorithms behind a non-open AI system—the “eyes” of a self-driving car, for example—they might be able to cause real damage by manipulating real-world objects into making the car behave in erroneous ways.
This might all seem like a remote possibility—after all, Google’s open Inception-v3 isn’t used for any critical applications—but the MIT research certainly makes a strong point about the fallibility of visual AI systems.
The team even plans to look further into creating adversarial objects that challenge AI systems whose mechanics are hidden.
The MIT group’s research paper, “Synthesizing Robust Adversarial Examples,” will be presented at ICLR 2018, the sixth International Conference on Learning Representations. It can be read here.
Posted in 3D Printing Application
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