Jackson Pollock Drip Painting Technique Harnessed for 3D Printing

The artist Jackson Pollock used a now-famous technique of dripping and splashing paint onto a canvas to create his world-renowned paintings. Now researchers at Harvard University have harnessed a similar technique in a new 3D-printing process for fabricating complex and unique shapes, even writing in cursive with the technique.

A team of scientists from Harvard's John A. Paulson School of Engineering and Applied Sciences (SEAS) developed an algorithmic approach to controlling a nozzle to reproduce the same effect as Pollock's in a 3D-printing process. The technique combines the physics of coiling with deep reinforcement learning that can improve the process iteratively.

“I wanted to know, can one replicate Jackson Pollock, and reverse engineer what he did,” explained L. Mahadevan, a professor of applied mathematics at SEAS as well as a professor of organismic and evolutionary biology and of physics.

It turns out that he indeed could, by creating a technique that can quickly create complex patterns—even replicating a segment of a Pollock painting—by leveraging the same natural fluid instability that the artist used.

The method deviates from the traditional printing techniques that are currently used in 3D printers, noted Gaurav Chaudhary, a former postdoctoral fellow at SEAS who also worked on the project.

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“If you look at traditional 3D printers, you supply them a path from point A to point B and the nozzle deposits ink along that specified path,” he explained.

Dynamic Instability

Pollock’s approach of throwing paint from a height meant that even if his hand was moving in a specific trajectory, the paint didn’t follow that trajectory because of the acceleration gained from gravity, Chaudhary said. Therefore, even a small motion could result in a large splatter of paint. 

"Using this technique, you can print larger lengths than you can move because you gain this free acceleration from gravity," he said.

To achieve this in printing, the researchers needed to harness physics, as liquid inks are bound by the rules of fluid dynamics. That means if they fall from a height, they become unstable, folding and coiling in on themselves. 

Mahadevan had already provided a simple physical explanation of this process two decades ago and later suggested how Pollock could have intuitively used these ideas to paint from a distance. However, 3D printing as a general rule avoids this type of instability by placing the print nozzle millimeters from the surface. This eliminates the dynamic instability that Pollock used to his advantage.

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Embracing Physics for 3D Printing

To create their technique, then, the researchers had to go against this general rule embrace the natural physics of fluid dynamics, Chaudhary said. “We wanted to develop a technique that could take advantage of the folding and coiling instabilities, rather than avoid them,” he said.

What appeared to be a lack of control in his process seemed to be what Pollock embraced with his drip method, creating great swirls on the canvas that eventually made their own type of sense. However, the Harvard researchers knew that the key to using the technique in printing, would be in learning how to control it. 

This is where team used techniques developed by Petros Koumoutsakos, a professor of computer science and engineering at SEAS, to apply a model that can learn from its mistakes and get more and more accurate with each trial, which is called deep reinforcement learning, Chaudhary said. 

Using this technique, the researchers printed a series of complex shapes, painting like Pollock and even decorating a cookie with chocolate syrup. They also were able to print the word "Cambridge"—the town where Harvard is located—in silicone oil in cursive "handwriting."

The team published a paper on their work in the journal Soft Matter. While the researchers simple fluids for this research, they said their approach can be expanded to include more complex fluids, including liquid polymers, pastes, and even various types of foods. 

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“Harnessing physical processes for functional outcomes is both a hallmark of intelligent behavior and at the heart of engineering design," Mahadevan said. "This little example suggests, once again, that understanding the evolution of the first might help us be better at the second.”