Scientists and engineers are continuously creating new materials with unique qualities that can be utilised for 3D printing, but figuring out how to print with these materials can be a difficult and expensive task. MIT researchers identified this crucial problem and to solve it, they are using artificial intelligence for 3D printing with new materials.
Often, a skilled operator must rely on manual trial-and-error — potentially hundreds of prints — to discover perfect conditions for printing a novel material effectively. These parameters include printing speed and the amount of material deposited by the printer.
Mike Foshey, a mechanical engineer and project manager at the CDFG, and Michal Piovarci, a postdoc at Austria’s Institute of Science and Technology, are the study’s co-lead authors. Jie Xu, an electrical engineering and computer science graduate student at MIT, and Timothy Erps, a former CDFG technical associate, are among the co-authors of the research.
Artificial Intelligence for 3D Printing with New Materials
This method has now been streamlined by MIT researchers using artificial intelligence. They created a machine-learning system that uses computer vision to monitor the manufacturing process and fix faults in how the material is handled in real time.
They utilised simulations to train a neural network to modify printing parameters to reduce error, and then they applied that controller to a real 3D printer. Their system created items more precisely than any other 3D printing controller they tested.
Above: Closed-loop control of direct ink writing via reinforced learning/Source: Michal Piovarči
The research avoids the excessively expensive procedure of printing thousands or millions of real-world objects in order to train the neural network. It may also make it easier for engineers to include novel materials into their prints, allowing them to create objects with unique electrical or chemical properties. It could also assist technicians in making on-the-fly changes to the printing process if material or ambient circumstances change unexpectedly.
Senior author Wojciech Matusik, professor of electrical engineering and computer science at MIT who leads the Computational Design and Fabrication Group (CDFG) within the Computer Science and Artificial Intelligence Laboratory (CSAIL) said, “This project is really the first demonstration of building a manufacturing system that uses machine learning to learn a complex control policy. If you have manufacturing machines that are more intelligent, they can adapt to the changing environment in the workplace in real-time, to improve the yields or the accuracy of the system. You can squeeze more out of the machine.”
Picking parameters
Determining the ideal parameters of a digital manufacturing process can be one of the most expensive parts of the process because so much trial-and-error is required.
They created a machine-vision system with two cameras targeted at the 3D printer’s nozzle for this purpose. The technology beams light at the material as it is deposited and estimates the thickness based on how much light passes through.
The controller would then analyse the images received from the vision system and, based on any errors detected, modify the feed rate and printer direction.
“You can think of the vision system as a set of eyes watching the process in real-time,” Foshey said.
However, training a neural network-based controller to understand this manufacturing process is time-consuming and would necessitate millions of prints. Instead, the researchers created a simulator.
Successful simulation
They used a process known as reinforcement learning to train their controller, in which the model learns through trial-and-error with a reward. The model was tasked with selecting printing parameters that would create a specific object in a simulated environment, and it was rewarded when the parameters minimised the error between the print and the expected result.
An “error” in this case means that the model either dispensed too much material, filling in areas that should have been left open, or did not dispense enough, leaving open spots that should be filled in. The researchers were aware that in the real world, conditions frequently change as a result of minor variations or noise in the printing process. As a result, the researchers developed a numerical model that approximates 3D printer noise. This model was used to add noise to the simulation, resulting in more realistic results.
Foshey added, “The interesting thing we found was that, by implementing this noise model, we were able to transfer the control policy that was purely trained in simulation onto hardware without training with any physical experimentation. We didn’t need to do any fine-tuning on the actual equipment afterwards.”
When they tested the controller, they discovered that it printed objects more accurately than any other control method they tried.
What Next?
The researchers want to develop controllers for other manufacturing processes now that they have demonstrated the effectiveness of this technique for 3D printing. They’d also like to see how the approach can be modified for scenarios involving multiple layers of material or the printing of multiple materials at the same time. Furthermore, they assumed that each material has a fixed viscosity (“syrupiness”), but a future iteration could use AI to recognise and adjust for viscosity in real-time.
Vahid Babaei, who leads the Max Planck Institute’s Artificial Intelligence Aided Design and Manufacturing Group; Piotr Didyk, associate professor at the University of Lugano in Switzerland; Szymon Rusinkiewicz, the David M. Siegel ’83 Professor of computer science at Princeton University; and Bernd Bickel, professor at the Institute of Science and Technology in Austria, are also co-authors on this paper.
The research was funded in part by the FWF Lise-Meitner programme, a European Research Council starting grant, and the National Science Foundation of the United States.
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