Investigating the Value of In-Situ Monitoring in the Laser Powder Bed Fusion of Nickel-Based Superalloys

Laser Powder Bed Fusion (L-PBF) is an additive manufacturing method, in which 2D patterns are selectively melted into successive layers of metal powder by a high power laser, resulting in the creation of a 3D component. L-PBF offers a range of advantages over traditional manufacturing techniques: otherwise-impossible geometries, such as internal cooling channels; the ability to build complex systems as a single part; and rapid design-to-production timelines.

Several problems must be overcome before L-PBF technology can achieve widespread adoption, however. Rapid thermal cycling causes unpredictability during solidification, resulting in geometric distortions, anisotropy, porosity, and undesirable microstructures in as-built components.

In-situ monitoring methods, including real-time imaging of the melt pool with high-speed cameras, are a vital tool in solving these problems. Should engineers be able to detect and correct for defects during the build, a significant step forward will have been made towards mainstream L-PBF usage.

The aim of this project was to add to the existing literature on the use of in-situ monitoring in L-PBF, specifically in terms of the identification of pore formation and the prediction of microstructure, in nickel-based superalloys. Various builds were completed, ranging from powder-free, single line scans, to geometrically complex components. Infrared camera data was collected, alongside ex-situ data from SEM, EBSD, and XCT. These datasets were compiled and cross-eferenced, to discover correlations and trends between melt pool images, and the resulting build.

The strengths and limitations of using melt pool images to predict pore and microstructure formation were revealed. While it was found to be challenging, with the equipment tested, to identify the creation of specific pores, it was found that distinct processing regions exist, in which the likelihood of pore formation was greater.

This research builds upon previous work by other authors, advancing L-PBF towards the predictability required for widespread adoption by industry.