6th International Congress on 3D Materials Science (3DMS 2022): Microstructure Characterization
Program Organizers: Dorte Juul Jensen, Technical University of Denmark; Marie Charpagne, University of Illinois; Keith Knipling, Naval Research Laboratory; Klaus-Dieter Liss, University of Wollongong; Matthew Miller, Cornell University; David Rowenhorst, Naval Research Laboratory
Monday 9:30 AM
June 27, 2022
Room: Capitol A
Location: Hyatt Regency Washington on Capitol Hill
Session Chair: Karsten Kunze, ETH Zurich, ScopeM
9:30 AM Invited
Porosity in L-PBF Additively Manufactured 316L: Richard Fonda1; Aeriel Leonard2; David Rowenhorst1; 1Naval Research Laboratory; 2NRL / OSU
We will present an analysis of the sizes, morphologies, and 3D distributions present within laser-powder bed fusion additively manufactured builds of 316L stainless steel. Porosity is a ubiquitous characteristic of structures built by additive manufacturing, and the amount and distribution of that porosity can have detrimental impacts on the performance of such materials. We will discuss the different morphologies and sizes of porosity in the build, the increased density of pores that form in the near-surface regions, and observed variations in porosity along the build direction, as well as the origins of these characteristics. In addition, we will discuss the impact that porosity can have on the mechanical and corrosion performance of L-PBF additively manufactured 316L stainless steel.
10:00 AM
Tribeam Tomography of a Novel CoNi-superalloy for Laser Additive Processing: James Lamb1; Andrew Polonsky2; McLean Echlin2; Kira Pusch2; Aurelien Botman3; Steven Randolph4; Remco Geurts3; Jorge Filevich3; Tresa Pollock2; 1Sandia National Laboratories; 2University of California, Santa Barbara; 3Thermo Fisher Scientific; 4Oak Ridge National Laboratory
The geometric flexibility of additive manufacturing offers the possibility of advanced component designs for materials in extreme environments. However, many of the advanced gamma prime-containing nickel superalloys used for critical components in the hot section of turbine engines are not easily processed via additive technologies due to their tendency to crack on solidification in welding conditions. Here we present a 3D characterization of a novel gamma prime containing CoNi-superalloy with inherent oxidation resistance that is amenable to the high solidification velocities and large thermal gradients generated during laser additive processing. The role of edge effects, both with and without a contour scan applied, on as-deposited microstructure occurring at the part-powder interface will be discussed. Using electron backscatter diffraction (EBSD) data collected in 3D, the accumulation of misorientation and microstructural dependence on scan strategy will also be discussed.
10:20 AM
Defect Interrogation in Additive Manufactured Inconel 738 Turbine Blades: Paul Brackman1; Curtis Frederick1; Andres Marquez-Rossy2; Michael Kirka2; 1Carl Zeiss; 2Oak Ridge National Laboratory
Development of Inconel 738 turbine blades produced by additive manufacturing (AM) involved mitigation of porosity, cracks, and contamination. Multi-length scale microscopy & microanalysis by X-Ray CT and SEM/EDS/EBSD were required to improve the AM processing parameters. Determination of the effects of hot isostatic pressure (HIP), specifically which defects were healed and root cause of large cracks found in early blades, were two questions addressed by this characterization protocol. In one example, a 0.75mm pore was located and tracked with X-ray CT before and after a HIP cycle. EDS and EBSD was used to determine grain orientation and elemental distribution near the pore. In the second case, a sub-surface crack initiated by contamination in a 2mm pore was identified by X-ray CT. By extracting this region and using SEM/EDS, the contaminate was identified. The combination of X-ray CT and SEM/EDS/EBSD was essential in improving the quality of AM produced turbine blades.
10:40 AM Break
11:10 AM
Noise, Sampling, and Phase: Understanding Spurious X-ray Signal: Matthew Andrew1; Stephen Kelly1; Andriy Andreyev2; Ravikumar Sanapala2; Robin White1; William Harris1; Hrishikesh Bale1; William Fadgen1; 1Carl Zeiss RMS; 2Carl Zeiss X-ray Microscopy
We introduce two new technologies which significantly improve X-ray microscopy image quality and contrast through the removal of spurious or undesired reconstructed signal typical for traditional reconstruction algorithms. DeepRecon Pro is the first technique for the fully automated training of deep learning networks for X-ray reconstruction. The resulting networks can be used to remove or greatly reduce two major contributors to spurious reconstructed image signal; random noise and sparse sampling artefacts. We also introduce a technique which allows for the complete removal of propagation phase contrast artefacts in the volume domain, which can become the dominant contrast mechanism when imaging at high resolution or at low kV. The removal of this effect reveals the inherent reconstructed material contrast which can then be much more effectively denoised, segmented or analyzed. We demonstrate the utility of these technologies on samples including low-contrast features like graphite anode of a commercial lithium-ion battery.
11:30 AM
A Novel Approach for High-resolution Phase Analysis using Hybrid Machine Learning Segmentation of Multi-modal FIB-SEM Datasets and its Application for Analysis of Next Generation Wear-resistant Coatings: Jiri Dluhos1; Hana Tesařová1; Vendulka Bertschová1; Frédéric Voisard2; Alexandre Migneault2; Nadi Braidy2; 1TESCAN ORSAY HOLDING, a.s.; 2Université de Sherbrooke
Acquisition of complete analytical information about material microstructure for understanding its relation to mechanical properties often requires a multi-scale or multi-modal characterization approach involving multiple analytical techniques. The scanning electron microscope (SEM) is a technology that allows materials characterization from few nanometers up to millimeter range. Besides high-resolution imaging the analytical potential of the system also allows corelative analysis by multiple micro/nano analytical techniques like EDS, EBSD or TOF-SIMS. With addition of focused ion beam (FIB) this extends also the capability into serial-sectiong 3D volume analysis. However, the segmentation of the large multi-modal FIB-SEM datasets is still challenging. The approach presented in this work tries to overcome some of the limitations of traditional techniques by hybrid machine learning segmentation on the multi-modal dataset followed by phase identification from spectral data. A high-resolution 3D analysis used in this work shows the application for development of next generation WC-Fe3Al wear resistant coatings.