Harvard Researchers Develop Faster 3D-Printing Method Inspired by Jackson Pollock's Paint-Dripping Technique

New 3D-printing method leverages machine learning and fluid dynamics to emulate Pollock's unique painting style - significant speed increases demonstrated in laboratory experiments.

• Harvard researchers, led by L. Mahadevan, have harnessed the painting techniques of Jackson Pollock to develop a faster method for 3D printing. They have used physics and machine learning to create complex physical patterns and replicate segments of Pollock's art.
• Traditional 3D and 4D printing techniques work by placing the print nozzle close to the surface, avoiding the dynamic instability of the liquid ink. However, this team has decided to embrace the physics, using the folding and coiling instabilities to their advantage, increasing the speed of printing process.
• Mahadevan and his team used deep reinforcement learning, an algorithmic approach to improve performance iteratively, and the physics of coiling to control fluid coiling and print at a distance, a technique used by Jackson Pollock in his painting style.
• The team's research demonstrated that implementing this method could print larger lengths and handle rough surfaces better than traditional printing methods. They believe this approach could be expanded to include more complex fluids like liquid polymers, pastes, and even food.
• The implications of this research could move beyond manufacturing and art, offering potential advancements in tissue engineering and creating structures that better mimic those found in the body.

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