Session: 16-02-01: Microstructure II

Paper Number: 173319

Hierarchical Analysis of Additively Manufactured 316l 

Additive manufacturing is able to manufacture metal alloy components with novel geometries and properties, but under highly complex processing conditions that lead to equally complex microstructural features.  Many of the typical metrics used to analyze microstructure, property, processing relationships are not well equipped to analyze these far-from-equilibrium conditions, where large thermal gradients and residual strains develop on the scale of the microstructure, resulting in microstructures not represented in traditional manufacturing. New methods must be developed for 3D microstructure analysis that were traditionally performed on 2D cross sections of the microstructure. This presents another challenging problem in that with these large 3D data sets, one cannot easily use manual measurements methods, thus requiring highly automated, quantitative, and algorithmic approaches to efficiently analyze microstructural data.
In this talk, we will outline methods for the combined morphological, topological, and crystallographic analysis of microstructure features across the component, grain, and sub-grain length scales.  By introducing and adapting topological analysis techniques, such as persistent homology, for analysis of polycrystalline microstructures, reduced order representations of microstructural features can be used to accelerate analysis and perform automated, algorithmic data processing of complex datasets, providing key information about length-scales and localized morphological crystallographic textures.
Using electron backscattered diffraction (EBSD) maps collected by automated mechanical serial-sectioning of a laser powder bed fusion (LPBF) additively manufactured 316L sample.  By utilizing an automated serial polishing method, a large volume of material can be collected while maintaining a high resolution, yielding a data set that contains over 30,000 grains, ranging in size from a few microns to millimeters in length.  Thus, hierarchical analysis will be used to show the relationships between structural grain properties and subsequent local orientation behavior. Quantitative tools and metrics are being developed to define characteristic signatures of different grain structures in additively manufactured materials, ranging from columnar to branching to small clusters, and search for relationships between those signatures and crystallographic properties, as well as link them to the localized processing conditions.  We will also examine if these methods can be adapted for large grain sizes present in laser hot-wire directed energy deposition (DED) manufactured 718 nickel-based superalloy, where grain sizes exceed multiple millimeters in size, and are on the scale of the component.
In conclusion, new analysis techniques are being developed for the characterization of three-dimensional additively manufactured materials. These methods provide quantitative metrics on grain behavior across multiple length scales and are important for understanding process control and material properties and informing modeling and simulation in additively manufactured materials.

Presenting Author: Simon Mason Naval Research Laboratory

Presenting Author Biography: Simon Mason is a NRC Postdoctoral Associate conducting research at the Naval Research Laboratory in Washington, DC. Simon received his PhD in Materials Science and Engineering from the Ohio State University as a member of the Mesoscale Mechanics and Microstructures research group. His research experience focuses on microstructure quantification and characterization, as well as in analysis of 3 dimensional microstructure datasets from automated serial sectioning and high energy diffraction microscopy.

Authors:

Simon Mason Naval Research Laboratory
David Rowenhorst Naval Research Laboratory