Driving Toward the Intersection of 3D Printing and Machine Learning

Applications of metal additive manufacturing, otherwise known as 3D printing, have primarily been confined to prototyping, but researchers are now pushing closer to developing metal 3D printing as a reliable form of industrial manufacturing. However, major obstacles still need to be addressed, especially in high-risk applications such as aviation components. "One of the biggest hurdles between just making a part that looks good and actually putting it on an aircraft is making sure that the part you're producing doesn't have flaws in it," said Carnegie Mellon University alumnus Luke Scime. Scime, who graduated with a doctorate in mechanical engineering and is now a post-doctoral researcher at Oak Ridge National Laboratory in Tennessee, worked with Mechanical Engineering's Jack Beuth, director of the NextManufacturing Center , to develop a machine learning algorithm that detects anomalies within a part as it's being printed - a practice known as process monitoring. The specific type of printing they worked with, laser powder bed fusion, involves spreading a thin layer of powder - 30 to 60 microns in diameter - and melting it in select areas to form a layer of the printed object. The process is then repeated for the next layer, with each build containing hundreds of thousands of layers. Many errors that can occur during a build are due to the incorrect spreading of the powder layer.