Scientists Fuse Simulations and Machine Learning to Accelerate Novel Additively Manufactured Materials

Researchers at the Johns Hopkins Applied Physics Laboratory have developed a machine learning method that could have a huge impact on understanding how material is formed during the additive manufacturing process.

John Hopkins Applied Physics Laboratory, Laurel, MD

Researchers at the Johns Hopkins Applied Physics Laboratory (APL) in Laurel, Maryland, have demonstrated a novel approach for applying machine learning to predict microstructures produced by a widely used additive manufacturing technique. Their approach promises to dramatically reduce the time and cost of developing materials with tailored physical properties and will soon be implemented on a NASA-funded effort focused on creation of a digital twin.

“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,” said Morgan Trexler, who manages APL's Science of Extreme and Multifunctional Materials program in the Research and Exploratory Development Mission Area.