Compared to traditional casting and metal working methods, metal additive manufacturing (AM) (also known as 3D printing) provide several benefits including near-net-shape production, fabrication of complex geometries, construction of parts with dissimilar alloys, and ease of repair. Our research on metal AM is focused on a) development of innovative in-situ quality control methods coupled with machine-learning-assisted feedback for real-time process control, b) fabrication of new alloys such as high entropy alloys for extreme environments, c) development of novel architectures to meet application requirements.
Widespread use of metal additive manufacturing (AM) is contingent on meeting the stringent requirements of quality and repeatability of parts made by this process. The complex transformations that occur in the transient stages of deposition, melt-solidification and cooling in the metal AM process, and the lack of mathematical models to understand these transformations make this a challenging endeavor. In-situ structural analysis has been limited to surface features or texture measured in an exotic setting like the Advanced Photon Source that is not easily transferrable to a commercial AM tool. Ideally, a broad range of sub-surface and bulk microstructural features such as texture, phase assemblage, composition and size of secondary phases should be evaluated in real-time, at the speed of fabrication in an AM tool. The objective of our research is to accomplish this goal by the development of a real-time, comprehensive, in-situ sub-surface and bulk structural analysis of AM parts during fabrication and integration with multi-modal data from various in-situ sensors, that can bridge the critical knowledge gap between process conditions and properties.
Our work is based on a proven in-situ 2D X-ray Diffraction (2D-XRD) method that we have developed for real-time monitoring of the crystallographic texture, phase assemblage, film composition and size of nano-scale secondary phases in roll-to-roll (R2R)-manufactured high temperature superconductor (HTS) tapes. 2D-XRD data obtained on 316 stainless steel AM parts made by Directed Energy Deposition (DED) demonstrate the ability of the 2D-XRD to resolve spatial differences in texture, grain size and phase assemblage of the AM parts.
A close correlation was found between Electron Backscatter Diffraction (EBSD) and 2D-XRD results on the average size distribution and the crystallographic orientation of grains in the sample. This work demonstrates the fast and reliable as-printed crystallography analysis using 2D-XRD compared to the EBSD technique, with potential for in-line integration.
{111} polefigures of DED-printed 316L metallic sample were analyzed at two different sections along the build direction. The {111} peak texture of the sample top section reveals a larger grain size manifested by a lower peak count and stronger intensity compared to the bottom section where smaller grain size with higher grain count and lower intensities are observed.
The grain size of the DED-printed samples was determined from the x-ray illuminated area of the sample andthe total grain count, determined from the number of observed {111} peaks normalized by multiplicity and fraction of reciprocal space scanned.
In a new project funded by the National Institute of Standards and Technology (NIST) Office of Advanced Manufacturing, we are developing real-time, comprehensive, in-situ sub-surface and bulk structural analysis of AM parts using in-situ 2D XRD during fabrication and integration with multi-modal data from various in-situ sensors, that can bridge the critical knowledge gap between process conditions and properties.