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  • richardmitnick 9:35 am on July 16, 2019 Permalink | Reply
    Tags: "Automated system generates robotic parts for novel tasks", 3-D printing, , , Navigating the “combinatorial explosion”, The researchers adopted a computer graphics technique called “ray-tracing” which simulates the path of light interacting with objects.   

    From MIT News: “Automated system generates robotic parts for novel tasks” 

    MIT News

    From MIT News

    July 12, 2019
    Rob Matheson

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    A new MIT-invented system automatically designs and 3-D prints complex robotic actuators optimized according to an enormous number of specifications, such as appearance and flexibility. To demonstrate the system, the researchers fabricated floating water lilies with petals equipped with arrays of actuators and hinges that fold up in response to magnetic fields run through conductive fluids. Credit: Subramanian Sundaram

    When designing actuators involves too many variables for humans to test by hand, this system can step in.

    An automated system developed by MIT researchers designs and 3-D prints complex robotic parts called actuators that are optimized according to an enormous number of specifications. In short, the system does automatically what is virtually impossible for humans to do by hand.

    In a paper published today in Science Advances, the researchers demonstrate the system by fabricating actuators — devices that mechanically control robotic systems in response to electrical signals — that show different black-and-white images at different angles. One actuator, for instance, portrays a Vincent van Gogh portrait when laid flat. Tilted an angle when it’s activated, however, it portrays the famous Edvard Munch painting “The Scream.” The researchers also 3-D printed floating water lilies with petals equipped with arrays of actuators and hinges that fold up in response to magnetic fields run through conductive fluids.

    The actuators are made from a patchwork of three different materials, each with a different light or dark color and a property — such as flexibility and magnetization — that controls the actuator’s angle in response to a control signal. Software first breaks down the actuator design into millions of three-dimensional pixels, or “voxels,” that can each be filled with any of the materials. Then, it runs millions of simulations, filling different voxels with different materials. Eventually, it lands on the optimal placement of each material in each voxel to generate two different images at two different angles. A custom 3-D printer then fabricates the actuator by dropping the right material into the right voxel, layer by layer.

    “Our ultimate goal is to automatically find an optimal design for any problem, and then use the output of our optimized design to fabricate it,” says first author Subramanian Sundaram PhD ’18, a former graduate student in the Computer Science and Artificial Intelligence Laboratory (CSAIL). “We go from selecting the printing materials, to finding the optimal design, to fabricating the final product in almost a completely automated way.”

    The shifting images demonstrates what the system can do. But actuators optimized for appearance and function could also be used for biomimicry in robotics. For instance, other researchers are designing underwater robotic skins with actuator arrays meant to mimic denticles on shark skin. Denticles collectively deform to decrease drag for faster, quieter swimming. “You can imagine underwater robots having whole arrays of actuators coating the surface of their skins, which can be optimized for drag and turning efficiently, and so on,” Sundaram says.

    Joining Sundaram on the paper are: Melina Skouras, a former MIT postdoc; David S. Kim, a former researcher in the Computational Fabrication Group; Louise van den Heuvel ’14, SM ’16; and Wojciech Matusik, an MIT associate professor in electrical engineering and computer science and head of the Computational Fabrication Group.

    Navigating the “combinatorial explosion”

    Robotic actuators today are becoming increasingly complex. Depending on the application, they must be optimized for weight, efficiency, appearance, flexibility, power consumption, and various other functions and performance metrics. Generally, experts manually calculate all those parameters to find an optimal design.

    Adding to that complexity, new 3-D-printing techniques can now use multiple materials to create one product. That means the design’s dimensionality becomes incredibly high. “What you’re left with is what’s called a ‘combinatorial explosion,’ where you essentially have so many combinations of materials and properties that you don’t have a chance to evaluate every combination to create an optimal structure,” Sundaram says.

    In their work, the researchers first customized three polymer materials with specific properties they needed to build their actuators: color, magnetization, and rigidity. In the end, they produced a near-transparent rigid material, an opaque flexible material used as a hinge, and a brown nanoparticle material that responds to a magnetic signal. They plugged all that characterization data into a property library.

    The system takes as input grayscale image examples — such as the flat actuator that displays the Van Gogh portrait but tilts at an exact angle to show “The Scream.” It basically executes a complex form of trial and error that’s somewhat like rearranging a Rubik’s Cube, but in this case around 5.5 million voxels are iteratively reconfigured to match an image and meet a measured angle.

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    Initially, the system draws from the property library to randomly assign different materials to different voxels. Then, it runs a simulation to see if that arrangement portrays the two target images, straight on and at an angle. If not, it gets an error signal. That signal lets it know which voxels are on the mark and which should be changed. Adding, removing, and shifting around brown magnetic voxels, for instance, will change the actuator’s angle when a magnetic field is applied. But, the system also has to consider how aligning those brown voxels will affect the image.

    Voxel by voxel

    To compute the actuator’s appearances at each iteration, the researchers adopted a computer graphics technique called “ray-tracing,” which simulates the path of light interacting with objects. Simulated light beams shoot through the actuator at each column of voxels. Actuators can be fabricated with more than 100 voxel layers. Columns can contain more than 100 voxels, with different sequences of the materials that radiate a different shade of gray when flat or at an angle.

    When the actuator is flat, for instance, the light beam may shine down on a column containing many brown voxels, producing a dark tone. But when the actuator tilts, the beam will shine on misaligned voxels. Brown voxels may shift away from the beam, while more clear voxels may shift into the beam, producing a lighter tone. The system uses that technique to align dark and light voxel columns where they need to be in the flat and angled image. After 100 million or more iterations, and anywhere from a few to dozens of hours, the system will find an arrangement that fits the target images.

    “We’re comparing what that [voxel column] looks like when it’s flat or when it’s titled, to match the target images,” Sundaram says. “If not, you can swap, say, a clear voxel with a brown one. If that’s an improvement, we keep this new suggestion and make other changes over and over again.”

    To fabricate the actuators, the researchers built a custom 3-D printer that uses a technique called “drop-on-demand.” Tubs of the three materials are connected to print heads with hundreds of nozzles that can be individually controlled. The printer fires a 30-micron-sized droplet of the designated material into its respective voxel location. Once the droplet lands on the substrate, it’s solidified. In that way, the printer builds an object, layer by layer.

    The work could be used as a stepping stone for designing larger structures, such as airplane wings, Sundaram says. Researchers, for instance, have similarly started breaking down airplane wings into smaller voxel-like blocks to optimize their designs for weight and lift, and other metrics. “We’re not yet able to print wings or anything on that scale, or with those materials. But I think this is a first step toward that goal,” Sundaram says.

    See the full article here .


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  • richardmitnick 4:46 pm on July 25, 2016 Permalink | Reply
    Tags: 3-D printing, , , ,   

    From U Wisconsin: “Tiny 3-D models may yield big insights into ovarian cancer” 

    U Wisconsin

    University of Wisconsin

    July 25, 2016
    Will Cushman
    perspective@engr.wisc.edu

    With a unique approach that draws on 3-D printing technologies, a team of University of Wisconsin–Madison researchers is developing new tools for understanding how ovarian cancer develops in women.

    About 1.5 percent of American women will be diagnosed with ovarian cancer, but most of them will not be diagnosed until late in the disease’s progression — after the cancer has spread to other parts of the body. This is reflected in the grim outlook for most women: The five-year survival rate for ovarian cancer is about 25 percent.

    Paul Campagnola, a professor of biomedical engineering and medical physics at UW–Madison, leads a group of researchers aiming to improve that outlook by understanding how ovarian cancer cells interact with nearby body tissue, and by developing new tools for imaging and detecting the disease. With a $2 million grant from the National Institutes of Health, they will use technology they’ve developed on the UW–Madison campus to develop images of tissues from surgical patients. The first target is collagen, a common protein that gives much of the body structure by holding bones, ligaments and muscles together.

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    A normal ovarian epithelial cell clings to a tiny model of an ovarian cancer tumor made with a 3-D printer. The tumor models will help scientists study ovarian cancer in mice, which do not naturally develop the disease. Image courtesy of Paul Campagnola

    “In most cancers, including ovarian, there are large changes in the collagen structure that goes along with the disease,” Campagnola says. “It might happen first. It might be later. It’s actually not known.”

    Campagnola and his colleagues, including Kevin Eliceiri, director of UW–Madison’s Laboratory for Optical and Computational Instrumentation, and Manish Patankar, associate professor of obstetrics and gynecology, hope to eliminate that unknown by printing tiny, 3-D models of the collagen samples.

    The models will be biomimetic — synthetic, but mimicking biological materials, as Velcro mimics the burs of a plant — and extremely small. Because, after seeding the models with ovarian cancer cells, the researchers will implant them into mice.

    Why not simply inject the mice with cancer cells and skip the painstaking imaging and 3-D printing process? Mice don’t get ovarian cancer — a partial answer for why we still don’t understand ovarian cancer as well as many other cancers.

    “The current way that people study ovarian cancer in a mouse is very poor,” Campagnola explains. “They just take human cell lines and then inject them into a mouse. Then some of them will form into a tumor, but most do not.”

    By implanting a 3-D tissue model seeded with ovarian cancer into mice, Campagnola hopes to mimic more closely the conditions of metastatic ovarian cancer in humans.

    “What’s different is our tissues will already be 3-D structured,” Campagnola says. “One problem when people study cancer sometimes is that they put cells in a dish. Cells in a dish don’t act like cells in tissue. So we’re trying to give them the tissue structure that cancer cells would have in a native environment.”

    From there, they’ll study how the implanted tumors grow inside the mice, and hopefully begin to learn more about the cues and processes involved in the disease’s progression and spread.

    It’s an approach that no one has ever attempted, one that will also help improve the way doctors make images of ovaries inside the body.

    “It’s an integrated approach to improving our imaging capabilities, but then also using our imaging capabilities to make these models so we can study the biology,” Campagnola says.

    Ultimately, the team’s long-term goal is to improve screening, diagnosis and treatment of ovarian cancer. One of the most effective ways to improve the outlook for women with ovarian cancer is to develop a straightforward method for screening women at higher risk for the disease. Women with a mutation in a gene called BRCA — a mutation also implicated in a higher risk for breast cancer — have a 40 percent chance of developing ovarian cancer in their lifetime.

    “Those are the women we really want to follow,” Campagnola says. “You could imagine — we’re a long way off from this — screening those women every few years with a minimally invasive device through a laparoscope or through the fallopian tubes.”

    But to get to that point, Campagnola says, researchers need to know a lot more about how ovarian cancer works.

    “You have to know what you’re looking for,” he says. “That’s why we have all this more basic work to do to get to that point. That’s why we need better imaging tools and we need better models to understand the biology of the disease.”

    See the full article here .

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