Electronics

Video: MIT Develops Algorithm That Bridges the Gap Between 3D Printing Hardware and Software

04 August 2017

The maximum stiffness of a 3D printed gripper could be improved through precise control. Image credit: MITThe maximum stiffness of a 3D printed gripper could be improved through precise control. Image credit: MIT

A new design system has been developed by MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) that catalogs physical properties of a huge number of tiny cube clusters that serve as the building blocks for larger 3D printable objects.

3D printers today have a resolution of 600 dots per inch, packing in up to a billion tiny cubes of different materials into a volume that measures about 1.67 cubic inches. This precise control allows designers to control an object’s physical properties, including density or strength or how they deform when under stress.

MIT’s system takes advantage of physical measurement at a microscopic scale while enabling computationally efficient evaluation of macroscopic designs.

"Conventionally, people design 3-D prints manually," says Bo Zhu, a postdoc at CSAIL. "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 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."

How They Did It

Researchers began 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: Deformation in the direction of an applied force, deformation in directions perpendicular to an applied force and how the material responds to force that causes different layers of the material to shift.

These standards define a 3D space and any particular combination of them defines a point in that space. In terms of 3D printing, the microscopic cubes from which an object is assembled is called voxels, three-dimensional analog pixels in a digital image. These are the building blocks to assemble larger printable objects as clusters of voxels.

Researchers considered clusters of three different sizes—16, 32 and 64 voxels to a face. In printable materials, the clusters combine those materials in different ways. The MIT algorithm explores the entire space of properties through both random generation of new clusters and the principled modification of clusters whose properties are known. This results in a cloud of points that defines the space of printable clusters.

The team then calculated a function called the level set describing the shape of the point cloud. This allowed researchers to mathematically determine whether a cluster with a particular combination of properties is printable or not.

Finally, they optimized the object to be printed using custom-developed software resulting in material properties for tens or even hundreds of thousands of printable clusters.

To read the full research, visit: https://arxiv.org/pdf/1706.03189.pdf

To contact the author of this article, email peter.brown@ieeeglobalspec.com


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