Computational design will keep pace with machines

April 18, 2018
Rick Nelson,
Executive Editor

We may be on a cusp of the transition to a world of complex, customizable products manufactured on demand by flexible robotic systems, ranging from 3D printers to whole-garment knitting machines. But while the machines stand ready to crank out custom products, what’s missing is a design methodology that can keep up with the machines. “When every product is different, we cannot have a designer work on each one individually,” said Wojciech Matusik, associate professor of electrical engineering and computer science at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT, where he leads the Computational Fabrication Group. “We have to develop a much more automated method to convert specifications to digital files to send to the various fabrication devices.”

What’s needed, said Matusik in a recent phone interview, is the new field of computational design for manufacturing, which will automate the design process to address larger design spaces across multiple domains—electronic, optical, mechanical, and thermal, for example. The field will rely on AI techniques, he said, to make optimal tradeoffs among multiple competing design objectives.

Matusik said it would be beneficial to reuse existing tools to the extent possible, but they’ll be wrapped around a more sophisticated AI computational framework, and completely new tools will be needed as well. Providing an example of how new and old design tools might work together, Matusik coauthored a paper last year describing InstantCAD, a tool that integrates with existing CAD software as a plugin and lets designers interactively edit, improve, and optimize CAD models.

“In a world where 3D printing and industrial robotics are making manufacturing more accessible, we need systems that make the actual design process more accessible, too,” said the paper’s lead author, Adriana Schulz, as quoted in MIT News. “With systems like this that make it easier to customize objects to meet your specific needs, we hope to be paving the way to a new age of personal manufacturing and DIY design.” Schulz, a Ph.D. student in MIT’s Department of Electrical Engineering and Computer Science, presented the paper at last summer’s Siggraph computer-graphics conference in Los Angeles.

In addition to the Siggraph paper on InstantCAD, other recent initiatives suggest how the future of computational design for manufacturing might work. For example, another Siggraph paper coauthored by Matuzik described how designers can gain control of a 3D-printed microstructure’s physical properties such as density or strength. In January, Matusik and coauthors elaborated on this work with a paper in Science Advances describing metamaterials—engineered materials with complex custom internal structures that exhibit a broader range of bulk properties than their base materials. “Although metamaterials with extraordinary properties have many applications, designing them is very difficult and is generally done by hand,” they write. “We propose a computational approach to discover families of microstructures with extremal macroscale properties automatically.”

Matusik will address computational design for manufacturing in a three-day MIT Professional Education course July 16 to July 18 on the MIT campus in Cambridge, MA. The course will conclude with a lab session on integrated design and optimization of custom drones. A 2016 paper coauthored by Matusik provides a preview of what this session might involve, describing a system that lets users design, simulate, and build a drone with their choice of size, shape, structure, payload, flight time, and other factors—avoiding a one-size-fits-all approach to drone design.

Matusik’s course is one of a new wave of MIT Professional Education courses designed to reflect the knowledge needs of the workforce of the future. Others cover topics ranging from the design of fast, efficient deep-learning systems to food innovation and machine learning for healthcare.

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