Johns Hopkins APL Looks at Machine Learning for LPBF

Researchers at The Johns Hopkins University Applied Physics Laboratory (APL) have developed a new approach that integrates 3D printing with machine learning, specifically for laser powder bed fusion (LPBF). Essentially, the model allows them to create simulations useful for verifying the production of materials created using LPBF. Machine learning is part of a branch of artificial intelligence and can be applied in various fields, from medical to aerospace.

The technique devised by APL makes it possible to predict what microstructure will be formed on the printing surface thanks to measurements made on the single layer of powder. To do this, researchers used computational modeling and simulation. These predictions of the object that will be created allow for early intervention in case of errors. This thus not only saves time, materials and costs, but also could exponentially increase the output of materials manufactured through LPBF technology.

This image shows the microstructure prediction process, which is achieved by analyzing the impact of cooling rate and temperature gradient on grain orientation and size.

This study is part of a larger body of work being conducted at The Johns Hopkins University Applied Physics Laboratory focused on using artificial intelligence to accelerate the discovery of new materials for extreme environments. Morgan Trexler, the liaison for APL’s Science of Extreme and Multifunctional Materials program in the Exploratory Research and Development Mission Area, commented, “We anticipate that this new approach will be extremely impactful in helping design and understand material formation during additive manufacturing processes, and this fits into our overarching strategy focused on accelerating materials development for national security.”