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Finding the Strongest Shapes with 3D Printing

Heinrich Jaeger, a professor at the University of Chicago, and his research group examine materials and phenomena that appear simple at the surface, but which reveal great complexity upon close examination. One such phenomenon is jamming, in which aggregates of randomly placed particles transition from fluid-like to solid-like behavior. Jamming lends itself especially to soft robotics. Jaeger and graduate student Marc Miskin have used computer simulations and experiments to analyze how the properties of a jammed material can be tuned by changing the shape of the constituent particles. Miskin employed a computer algorithm and once an optimal shape was identified, he manufactured a large number of copies with the lab's 3D printer for testing in a viselike squeezing apparatus.

Topics:
Automation Computer-Aided Design (CAD) Computers Electronics & Computers Manufacturing & Prototyping Materials Rapid Prototyping & Tooling Robotics Software Test & Measurement

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    Transcript

    00:00:02 [HEINRICH JEAGER] This is our 3D printer. The unique features is that enables us to take a file that would describe the virtual object in a computer and turn it into a real physical object that we can then use to, you know, not only hold in our hand but do a real experiment on. (Music) This is really all about jamming so it's really that fact that when available volume to a bunch of grains gets so small that they get in each other's way they get stuck. [MARC MISKIN] It doesn't sound like a very big question but what shapes would be very stiff, or very soft under compression.

    00:00:36 If you to want to build a house, for instance, you're building on granular material it might be nice to know what shapes would be good to put on that foundation. [JEAGER] This turned into a very important next level of research. How to find optimal shapes. If you had just ordinary spheres in three dimensions. How do they pack? What if you went away from spheres and had different shapes? How would they pack? That's when we realized that we needed to have a way to fabricate

    00:01:06 particles of the essentially arbitrary shape. (Music) MISKIN] So if you take this shape and you pour a bunch of it into a rubber bag and then you pull out all the air. Then you push down on top and you measure how much force the thing is pushing back with. And to give you an idea this machines very sensitive You can measure everything from how much force it takes to pull apart a drop of water, to being able to crush the piece of copper piping

    00:01:33 from the tube into this little pancake. So what you're looking at here this is the total force being applied vs. the distance I went out and then each of the curves is obviously a different sample. If I had a regular solid, like a piece of steel I can just push on it and measure how much it pushes back. For something like a granular aggregate that's going to depend on a lot of things. If I looked at different shapes, they would all have different types of these curves Originally the way the research was getting done was we had an army of undergraduates. Each of them had a shape. You've identified which ones are better or worse but you don't really have a method

    00:02:03 to go through this. [JEAGER] So that's where where Marc developed a fresh approach. [MISKIN] Every optimization, everyone of these evolutionary optimizations is going to start with a random guess. Then as you start to learn where the good results are, it begins to converge. We ran it, we found our optimizer got it dead on. In the case of the softest shapes, amazingly you find it every single time it's rods It's always use take them all, line them all up that forms the softest packing. The stiffest was trickier.

    00:02:31 You formed the stiffest packing shape for a given a number of equal size spheres if you take the most compact shape you can make, and then pull off a sphere. These guys are strain stiffing particles When you start compressing them they generate very little force and they build up more and more force the more you compress them. As far as we know they're pretty unique in that respect. When we look at it as humans I think we're surprised by it because as physicists and engineers are biased towards at looking at designs that are symmetric. The computer and nature aren't. [JEAGER] And in that sense it is not only nice to find high-performance from this but it is also particularly nice because it is

    00:03:10 not something that is symmetric, not something that we would have, as physicists, have come up with. With this new tool that we have, we are discovering particle shapes that nobody had guessed before The stress response, the 3D printing, the simulations makes a complete package for us. That in turn, connects to some of the deepest problems in mathematics, physics and granular physics per se.