Aug 8, 2017 | By David

Knowing the exact physical properties of a 3D print job before it is carried out would be incredibly useful for designers and manufacturers making use of 3D printing technology, enabling them to know exactly which materials to choose to optimize their 3D printed object. A group of researchers at MIT have recently developed an ingenious new software system that will allow these properties to be calculated in a hugely reduced time frame, making use of an advanced physical simulation.

The system was designed by Bo Zhu, a postdoc at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), along with 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. Their work was supported by the U.S. Defense Advanced Research Projects Agency’s SIMPLEX program, and they presented a paper on it recently at Siggraph, the major international graphics conference.

“Conventionally, people design 3-D prints manually,” says Bo Zhu. “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.”

The average 3D printer’s resolution is around 600 dots per inch, which means that a billion tiny cubes of material can be packed into 1.67 cubic inches of volume. This means that it would be prohibitively time-consuming to calculate the physical effects of every combination of just 2 materials. The innovation of Zhu and his team was to model these effects by simulating clusters of cubes instead.

For a given set of materials, the team’s software randomly generates clusters of different sizes- 16, 32, or 64 voxels (3D pixels). It gradually builds up a database of thousands of possible structures by evaluating the physical properties of each of these clusters, which are used as virtual building blocks for the 3D printed object. The software can then specify the best possible materials for the object to be 3D printed, by consulting this database. This software has a huge range of potential applications, and should optimize the 3D printing process further.

“The design and discovery of structures to produce materials and objects with exactly specified functional properties is central for a large number of applications where mechanical properties are important, such as in the automotive or aerospace industries,” says Bernd Bickel, an assistant professor of computer science at the Institute of Science and Technology Austria and head of the institute’s Computer Graphics and Digital Fabrication group. “Due to the complexity of these structures, which, in the case of 3-D printing, can consist of more than a trillion material droplets, exploring them manually is absolutely intractable.”

“The solution presented by Bo and colleagues addresses this problem in a very clever way, by reformulating it,” he says. “Instead of working directly on the scale of individual droplets, they first precompute the behavior of small structures and put it in a database. Leveraging this knowledge, they can perform the actual optimization on a coarser level, allowing them to very efficiently generate high-resolution printable structures with more than a trillion elements, even with just a regular computer. This opens up exciting new avenues for designing and optimizing structures at a resolution that was out of reach so far.”

 

 

Posted in 3D Software

 

 

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