Tag Archives: 3-d

Electrical and computer engineering Professor Barry Van Veen wears an electrode net used to monitor brain activity via EEG signals. His research could help untangle what happens in the brain during sleep and dreaming.

Photo Credit: Nick Berard/UW-Madison

Software lets designers exploit the extremely high resolution of 3-D printers.

Designing the microstructure of printed objects

Software lets designers exploit the extremely high resolution of 3-D printers.

By Larry Hardesty

CAMBRIDGE, Mass. – Today’s 3-D printers have a resolution of 600 dots per inch, which means that they could pack a billion tiny cubes of different materials into a volume that measures just 1.67 cubic inches.

Such precise control of printed objects’ microstructure gives designers commensurate control of the objects’ physical properties — such as their density or strength, or the way they deform when subjected to stresses. But evaluating the physical effects of every possible combination of even just two materials, for an object consisting of tens of billions of cubes, would be prohibitively time consuming.

So researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a new design system that catalogues the physical properties of a huge number of tiny cube clusters. These clusters can then serve as building blocks for larger printable objects. The system thus takes advantage of physical measurements at the microscopic scale, while enabling computationally efficient evaluation of macroscopic designs.

“Conventionally, people design 3-D prints manually,” says Bo Zhu, a postdoc at CSAIL and first author on the paper. “But when you want to have some higher-level goal — for example, you want to design a chair with maximum stiffness or design some functional soft [robotic] gripper — then intuition or experience is maybe not enough. Topology optimization, which is the focus of our paper, incorporates the physics and simulation in the design loop. The problem for current topology optimization is that there is a gap between the hardware capabilities and the software. Our algorithm fills that gap.”

Zhu and his MIT colleagues presented their work this week at Siggraph, the premier graphics conference. Joining Zhu on the paper are Wojciech Matusik, an associate professor of electrical engineering and computer science; Mélina Skouras, a postdoc in Matusik’s group; and Desai Chen, a graduate student in electrical engineering and computer science.

Points in space

The MIT researchers begin by defining a space of physical properties, in which any given microstructure will assume a particular location. For instance, there are three standard measures of a material’s stiffness: One describes its deformation in the direction of an applied force, or how far it can be compressed or stretched; one describes its deformation in directions perpendicular to an applied force, or how much its sides bulge when it’s squeezed or contract when it’s stretched; and the third measures its response to shear, or a force that causes different layers of the material to shift relative to each other.

Those three measures define a three-dimensional space, and any particular combination of them defines a point in that space.

In the jargon of 3-D printing, the microscopic cubes from which an object is assembled are called voxels, for volumetric pixels; they’re the three-dimensional analogue of pixels in a digital image. The building blocks from which Zhu and his colleagues assemble larger printable objects are clusters of voxels.

In their experiments, the researchers considered clusters of three different sizes — 16, 32, and 64 voxels to a face. For a given set of printable materials, they randomly generate clusters that combine those materials in different ways: a square of material A at the cluster’s center, a border of vacant voxels around that square, material B at the corners, or the like. The clusters must be printable, however; it wouldn’t be possible to print a cluster that, say, included a cube of vacant voxels with a smaller cube of material floating at its center.

For each new cluster, the researchers evaluate its physical properties using physics simulations, which assign it a particular point in the space of properties.

Gradually, the researchers’ algorithm explores the entire space of properties, through both random generation of new clusters and the principled modification of clusters whose properties are known. The end result is a cloud of points that defines the space of printable clusters.

Establishing boundaries

The next step is to calculate a function called the level set, which describes the shape of the point cloud. This enables the researchers’ system to mathematically determine whether a cluster with a particular combination of properties is printable or not.

The final step is the optimization of the object to be printed, using software custom-developed by the researchers. That process will result in specifications of material properties for tens or even hundreds of thousands of printable clusters. The researchers’ database of evaluated clusters may not contain exact matches for any of those specifications, but it will contain clusters that are extremely good approximations.

The MIT researchers’ work was supported by the U.S. Defense Advanced Research Projects Agency’s SIMPLEX program.

Source: MIT News Office

Microscopic “walkers” find their way across cell surfaces

Technology could provide a way to deliver probes or drugs to cell structures without outside guidance.

By David Chandler


CAMBRIDGE, Mass–Nature has developed a wide variety of methods for guiding particular cells, enzymes, and molecules to specific structures inside the body: White blood cells can find their way to the site of an infection, while scar-forming cells migrate to the site of a wound. But finding ways of guiding artificial materials within the body has proven more difficult.

Now a team of researchers at MIT led by Alfredo Alexander-Katz, the Walter Henry Gale Associate Professor of Materials Science and Engineering, has demonstrated a new target-finding mechanism. The new system allows microscopic devices to autonomously find their way to areas of a cell surface, for example, just by detecting an increase in surface friction in places where more cell receptors are concentrated.

The finding is described this week in a paper in the journal Physical Review Letters, written by Alexander-Katz, graduate student Joshua Steimel, and postdoc Juan Aragones.

“The idea was to find out if we could create a synthetic, active system that could sense gradients in biological receptors,” Alexander-Katz explains. “Currently, we don’t know of anything that can do that.”

Cells have a way of locating areas that bear a specific kind of chemical signature — a process called chemotaxis. That’s the method used by white blood cells, for example, to locate regions where pathogens are attacking body cells.

“Our system is very simple,” Alexander-Katz says — similar to the way in which bacteria locate nutrients they need. The system, without guidance, samples areas on a surface and migrates toward those where friction is greater — which also correspond to areas where receptors are concentrated.

The system uses a pair of linked particles with magnetic properties. In the presence of a magnetic field, the paired particles begin to tumble across a surface, with first one particle and then the other making contact — in effect, “walking” across the surface.

So far, the work has been carried out on a model cell surface, on a functionalized microscope slide, but the effect should work similarly with living cells, Alexander-Katz says. The team’s goal now is to demonstrate the ability of the microscopic walkers to find their way toward concentrations of receptors in actual living tissue.

The method could potentially have a variety of applications, Alexander-Katz says. For example, it could be developed as a method of locating tumor cells within the body by identifying their surface texture, perhaps in combination with other characteristics.

Such magnetic microwalkers, he adds, could be unleashed to locate areas of interest on various kinds of surfaces, based solely on differences in friction. The particles naturally migrate toward high-friction regions, where they could then be induced to interact with a surface by active molecules attached to them.

“It’s a very versatile system,” Alexander-Katz says, that can be functionalized by attaching other kinds of receptors or binding agents to affect or monitor the target area in different ways.

The next step is to test the approach in more complex settings. The initial work was done with flat surfaces; the team now aims to conduct studies in complex 3-D settings to make sure the process works effectively in situations that more closely resemble a real cellular environment.

The research was supported by the U.S. Department of Energy, the MIT Energy Initiative, and the Chang family.