Printing the invisible : bridging the gap between data and matter through voxel-based 3D printing

Scientific visualizations are central to the representation and communication of data in ways that are at once efficient and effective. Numerous data types have established unique formats of representation. In the context of three-dimensional (3D) data sets, such information is often presented as a 3D rendering, a video or an interactive application. The purpose of such visualization is often to emulate the physical, three-dimensional world; however, they remain inherently virtual. Recent advancements in additive manufacturing are making it possible to 'physicalize' three-dimensional data through 3D printing. Still, most 3D printing methods are geared towards single material printing workflows devoid of the ability to physically visualize volumetric data with high fidelity matching their virtual origin. As a result, information and detail are compromised. To overcome this limitation, I propose, design and evaluate a workflow to 'physicalize' such data through multi-material 3D printing. The thesis focuses on methods for voxel-based additive fabrication at high spatial resolution of three-dimensional data sets including - but not limited to point clouds, volumes, lines and graphs, and image stacks. This is achieved while maintaining the original data with high fidelity. I demonstrate that various data sets - often visualized through rasterization on screen - can be translated into physical, materially heterogeneous objects, by means of multi-material, voxel-based 3D printing. This workflow - its related tools, techniques and technologies contained herein - enables bridging the gap between digital information presentation and physical material composition. Developed methods are experimentally tested with various data across scales, disciplines and problem contexts - including application domains such as biomedicine, physics and archeology.

Department dc:contributor.department

Program in Media Arts and Sciences (Massachusetts Institute of Technology)

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2017

Author dc:creator

• Kolb, Dominik.

Advisor dc:contributor.advisor

• Neri Oxman.

• Program in Media Arts and Sciences

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/112911

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/112911