Researchers Develop Multimaterial Voxel-3D Printing Method For More Direct Data to Object Translation


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Volumetric 3D printed high-resolution data objects. [Image: The Mediated Matter Group]

Modern approaches to physical visualization and data representations still mostly use a 2D display of 3D data sets on planar computer screens, which doesn’t always present the best results. A specific research area, known as data or physical visualization, has been growing to achieve the representation of data sets in a physical form through digital fabrication.

What if, instead of being forced to rely on geometric representations of the objects you’re 3D printing, you could use data sets instead, like what you’d get from an MRI machine? This outcome is exactly what a team of researchers from MIT Media Lab’s Mediated Matter Group and Harvard University has been working to achieve.

To fabricate an item on conventional 3D printers, one must make calculations regarding the object’s digital description, and then convert the resulting numeric description to geometric shapes which can be used to 3D print it. But the research team has developed a new technique to 3D print multimaterial data sets as physical objects, which requires less pre-processing to create a more direct data-to-object translation, and builds a bridge between the physical and the digital.

The team published a paper, titled “Making data matter: Voxel printing for the digital fabrication of data across scales and domains,” on the open access Science Advances site. In it, they describe their new technique, which converts data describing a digitized image to voxels – making it possible to 3D print incredibly precise voxels, instead of shapes, at a resolution of 2.3 million per cubic centimeter.

The abstract reads, “We present a multimaterial voxel-printing method that enables the physical visualization of data sets commonly associated with scientific imaging. Leveraging voxel-based control of multimaterial three-dimensional (3D) printing, our method enables additive manufacturing of discontinuous data types such as point cloud data, curve and graph data, image-based data, and volumetric data. By converting data sets into dithered material deposition descriptions, through modifications to rasterization processes, we demonstrate that data sets frequently visualized on screen can be converted into physical, materially heterogeneous objects. Our approach alleviates the need to postprocess data sets to boundary representations, preventing alteration of data and loss of information in the produced physicalizations. Therefore, it bridges the gap between digital information representation and physical material composition. We evaluate the visual characteristics and features of our method, assess its relevance and applicability in the production of physical visualizations, and detail the conversion of data sets for multimaterial 3D printing. We conclude with exemplary 3D-printed data sets produced by our method pointing toward potential applications across scales, disciplines, and problem domains.”

General workflow for the conversion of data sets to 3D printed data physicalizations.

The results of the team’s new method to 3D print data sets as physical objects identical to their on-screen visualizations are akin to switching to a laser printer from a dot-matrix. The technique actually negates the need to create an intermediate boundary representation of an object, which can end in information loss or data alteration for less ideal physical results.

According to the paper, “The STL file format represents objects through a closed regular surface, which is described by a list of triangles, defined through their vertices. During the 3D printing process, each surface is considered a solid object, where space inside the triangle boundary representation is occupied by a single material. Unfortunately, these design and additive manufacturing workflows do not think ‘beyond the shell’ of objects, despite the fact that commercially available 3D printers can print up to seven materials simultaneously. This means that to 3D print any data set, especially those that are not naturally representable as surfaces, all data first must be converted into a boundary representation. Specifically for scientific data, this conversion process is problematic, as, in many cases, it introduces computational overhead, alteration of data, and even loss of information.”

Vespers. Series 2 Mask 2. Side View. Designed by Neri Oxman and members of the Mediated Matter Group for The New Ancient Collection curated and 3D printed by Stratasys, 2016. [Image: Yoram Reshef]

Just like pixels, individual voxels have a color code that can help recreate an object’s actual color by mixing white, clear, black, cyan, magenta, and yellow. This results in an object that closely resembles the original, such as an artifact or human heart. This new technique uses multimaterial 3D printing to improve the current data physicalization workflows, and the team explained that it can also be used to design, then 3D print, new objects from the ground up on a 3D modeling computer.

To demonstrate this, they designed and 3D printed some incredible pieces, like the intricate death masks with subtle color changes by Stratasys and Neri Oxman, who runs the Mediated Matter research group, that were featured in the hauntingly beautiful Vespers series for part of the company’s The New Ancient 3D printed art and design collection.

The researchers believe that their new multimaterial 3D printing method could have applications in several fields, including education, surgical planning, conservation, and cultural artifact preservation.

“By using recent advances in multimaterial 3D printing technologies in combination with voxel printing, the presented process allows less preprocessing (such as segmentation and hole filling) of the used data sets compared to methods using boundary representations,” the paper reads.

Co-authors include Christoph Bader and Dominik Kolb from MIT, James C. Weaver from Harvard’s Wyss Institute for Biologically Inspired Engineering, MIT’s Sunanda Sharma, Ahmed Hosny from Harvard Medical School’s Dana-Farber Cancer Institute, and MIT’s João Costa and Oxman.

Discuss this research and other 3D printing topics at or share your thoughts below. 



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