Many industries already rely on the process to rapidly build parts and components. Rocket engine nozzles; pistons for high-performance cars, and custom orthopedic implants are all made using additive manufacturing, a process that involves building parts layer-by-layer using a 3D printer.
However, structural defects that form during the building process is one of the reasons why this approach has not become more widely adopted.
Now, a research team led by Argonne and the University of Virginia (UVA) have developed various imaging and machine-learning techniques to detect and predict the formation of pores in 3D-printed metals in real time with near-perfect accuracy.
The metal samples used in the study were created using a process called laser powder bed fusion, in which metal powder is heated by a laser and then melted into the proper shape. This approach often leads to the formation of pores that can compromise a part’s performance.
Many additive manufacturing...