Often, an expert operator must use manual trial-and-error -- possibly making thousands of prints -- to determine ideal parameters that consistently print a new material effectively. These parameters include printing speed and how much material the printer deposits. MIT researchers have now used artificial intelligence to streamline this procedure. They developed a machine-learning system that uses computer vision to watch the manufacturing process and then correct errors in how it handles the material in real-time. They used simulations to teach a neural network how to adjust printing parameters to minimize error, and then applied that controller to a real 3D printer.

Scientists and engineers are constantly developing new materials with unique properties that can be used for 3D printing, but figuring out how to print with these materials can be a complex, costly conundrum. Often, an expert operator must use manual trial-and-error -- possibly making thousands of prints -- to determine ideal parameters that consistently print a new material effectively. These parameters include printing speed and how much material the printer deposits. MIT researchers have now used artificial intelligence to streamline this procedure. They developed a machine-learning system that uses computer vision to watch the manufacturing process and then correct errors in how it handles the material in real-time.