Software lets designers exploit high resolution of 3D printers

Today's 3D 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 27.37 cu cm.

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 the Computer Science & Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology (MIT) in the US 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 3D prints manually," says Bo Zhu, a postdoctoral student 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 optimisation, which is the focus of our paper, incorporates the physics and simulation in the design loop. The problem for current topology optimisation is that there is a gap between the hardware capabilities and the software. Our algorithm fills that gap."

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 is squeezed or contract when it is 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 3D printing, the microscopic cubes from which an object is assembled are called voxels, for volumetric pixels; they are 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 centre, a border of vacant voxels around that square, material B at the corners, or the like. The clusters must be printable, however; it would not be possible to print a cluster that, say, included a cube of vacant voxels with a smaller cube of material floating at its centre.

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.

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 optimisation 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.


MIT researchers 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. Image: Computational Fabrication Group at MIT

Three-layer technique for secure additive manufacturing

Additive manufacturing, also known as 3D printing, is replacing conventional fabrication processes in critical areas ranging from aerospace components to medical implants. But because the process relies on software to control the 3D printer, additive manufacturing could become a target for malicious attacks as well as unscrupulous operators who may cut corners.

Researchers from the Georgia Institute of Technology (GeorgiaTech) and Rutgers University in the US have developed a three-layer system to verify that components produced using additive manufacturing have not been compromised. Their system uses acoustic and other physical techniques to confirm that the printer is operating as expected, and non-destructive inspection techniques to verify the correct location of tiny gold nanorods buried in the parts. The validation technique is independent of printer firmware and software in the controlling computer.

"These 3D printed components will be going into people, aircraft and critical infrastructure systems," said Prof Raheem Beyah, the Motorola Foundation Professor and associate chair in GeorgiaTech's School of Electrical & Computer Engineering. "Malicious software installed in the printer or control computer could compromise the production process. We need to make sure that these components are produced to specification and not affected by malicious actors or unscrupulous producers."

The three components of the new system include:

1. Acoustic measurement of the 3D printer in operation. When compared to a reference recording of a correct print, this acoustic monitoring - done with an inexpensive microphone and filtering software - can detect changes in the printer's sound that may indicate installation of malicious software.

2. Physical tracking of printer components. To create the desired object, the printer's extruder and other components should follow a consistent mechanical path that can be observed with inexpensive sensors. Variations from the expected path could indicate an attack.

3. Detection of nanorods in finished components. Using Raman spectroscopy and computed tomography, the researchers were able to detect the location of gold nanorods that had been mixed with the filament material used in the 3D printer. Variations from the expected location of those particles could indicate a quality problem with the component. The variations could result from malicious activity, or from efforts to conserve printer materials.

The researchers tested their technique on three different types of 3D printers and a computer numerical control machine using a polyethylene tibial knee prosthesis as a test case. Beyond detecting malicious activity or quality problems, the technique could stop inadvertent production problems, reducing materials waste.

In their technique to detect flaws in 3D printed components, the researchers were inspired to apply the same kind of contrast agents used in medical imaging techniques for detecting tumours, said Dr Mehdi Javanmard, assistant professor in the Department of Electrical & Computer Engineering at Rutgers University.

The gold contrast materials were tested to make sure they would not compromise the structural integrity of the printed components.

Now that they have demonstrated the feasibility of the techniques, the researchers plan to improve the validation methods and move them closer to application. "Our focus now will be on testing the resilience of this technology and its resistance to intrusion and malicious attacks," Dr Javanmard said.

Among the challenges ahead will be obtaining good acoustic data in the noisy environments in which 3D printers typically operate. In the research reported by the researchers, operation of other 3D printers near the one being observed cut the accuracy significantly, but Prof Beyah believes that challenge can be addressed with additional signal processing. The technique will also be applied to additional types of printers and different materials.

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