6th World Congress on Integrated Computational Materials Engineering (ICME 2022): Microstructure Evolution and Analysis I
Program Organizers: William Joost; Kester Clarke, Los Alamos National Laboratory; Danielle Cote, Worcester Polytechnic Institute; Javier Llorca, IMDEA Materials Institute & Technical University of Madrid; Heather Murdoch, U.S. Army Research Laboratory; Satyam Sahay, John Deere; Michael Sangid, Purdue University

Tuesday 8:00 AM
April 26, 2022
Room: Regency Ballroom DE
Location: Hyatt Regency Lake Tahoe


8:00 AM  Invited
Measurement of Flow Stresses at High Strain Rates and Temperatures for Improved Simulation of Friction Stir Welding: John Prymak1; Kennen Brooks1; Michael Miles1; Tracy Nelson1; 1Brigham Young University
    The modeling of friction stir welding has long been challenged by a lack of accurate flow stress data over the large range of temperatures and strain rates that are typical of the process. Most often, hot compression or hot torsion tests are used to measure flow stresses, but the strain rates and deformation modes associated with these tests do not emulate the high strain rate shearing that occurs near the friction stir welding tool. An alternate method of measuring flow stresses for the modeling of FSW is proposed, where a flat tool configuration is employed to shear AA 6061-T6 specimens at different rotational velocities and vertical loads. An analytical model of the shear layer is employed to estimate material viscosity and flow stresses for each test condition. FSW simulations using the measured flow stresses were compared to experiments as partial validation of the approach.

8:30 AM  
Generation of Large-Scale Three-Dimensional Microstructures from Surface Images: Application to Additive Manufacturing: Iman Javaheri1; Veera Sundararaghavan2; 1NASA Langley Research Center; 2University of Michigan
    The generation of large-scale microstructural models is essential for understanding the process-structure-property relationships of metals. We present a software LEGOMAT, applicable for large-scale real-time integration of microstructural models with millions of grains within an engineering CAD model. We display the use of LEGOMAT software for an additively-manufactured (i.e., laser engineered net shaping) part by embedding microstructures along user-specified laser paths while accounting for hatch widths, layer thicknesses, and scan directions. Here, the surface micrographs are obtained from location-specific EBSD images, which are converted to three-dimensional (3D) representative microstructures using the Markov random field technique. These 3D microstructures are seamlessly merged into an engineering-scale CAD model, using a patch-based optimization process that maps every finite element within the CAD model to a synthetic microstructural domain. Cross-validation of morphological features (e.g., grain size, shape, orientation, two-point correlation), as well as mechanical properties are carried out to validate our reconstruction strategy.

8:50 AM  
Object-Oriented Finite-Elements for Materials Science: Andrew Reid1; 1National Institute of Standards and Technology
     The NIST-developed open-source Object-Oriented Finite Element code (OOF) is a long-standing project to deliver high-quality mathematics and computational capabilities to a materials-science audience. The code features tools to easily construct finite-element meshes which match real 3D microstructures, derived from micrographs or models, as well as a scheme for the addition of custom constitutive rules. The result allows materials science domain experts to conduct sophisticated structure-property explorations. The tool has recently had a crystal-plasticity capability added to it, which posed several development challenges, in that the plasticity property has strong history-dependence, whereas the initial design for the software primarily anticipated essentially PDE divergence equations.An on-going need for the OOF tool is better integration with materials analysis and design workflows. This is an additional challenge not well-anticipated by the initial application design, but with obvious value to the materials community

9:10 AM  
Large Volume Grain Statistics with Laboratory Diffraction Contrast Tomography: Erik Lauridsen1; Jette Oddershede1; Jun Sun1; Hrishikesh Bale2; Florian Bachmann1; 1Xnovo Technology; 2Carl Zeiss X-ray Microscopy Inc
    Integrated microstructural modeling approaches, ensuring the handshake between modeling and experimentation, relies on adequate experimental statistics. Furthermore, recent improvements in computing power and characterization techniques have opened up new possibilities in 4D analysis, beyond established 2D microstructural methods. Here we present the latest innovations in laboratory X-ray diffraction contrast tomography (LabDCT) which allow for recording and reconstructing of very large representative volumes seamlessly. The LabDCT technique can produce 3D grain maps containing more than 10.000 grains in less than a day. We will present and discuss different acquisition strategies with emphasis on how to approach a given acquisition problem inherent to the sample and provide examples of how such experimental data can be used for either validation or as input volumes for simulation. Moreover, with its nondestructive nature, LabDCT enables time resolved studies of the response of the materials when exposed to external stimuli such as e.g. thermo-mechanical processing.

9:30 AM  
Templated Product-Phase Microstructure via Directed Solid-State Synthesis: A Combined Mesoscale Modeling and Machine-Learning Approach: Connor Mcnamara1; Helen Chan1; Jeffrey Rickman1; 1Lehigh University
    Solid-state, single-crystal synthesis is used in our laboratory to produce patterned product-phase microstructures in systems having an entropy-stabilized intermediate phase. We seek to model the underlying physics dictating these product-phase microstructures and establish correlations with the initial, patterned duplex structures. For this purpose, we utilize numerical solutions of reaction-diffusion equations combined with a supervised machine learning strategy. In this talk, we will discuss the impact of relevant physical parameters (e.g., diffusion rates, reaction rates, template geometry) on the product-phase microstructural features, as obtained from our analysis. Moreover, we will describe how our analysis enables high-throughput microstructural design via this novel synthesis procedure.

9:50 AM  
Modeling Magnetic Field Influence on Iron Alloy Phase Transformations: Heather Murdoch1; Efrain Hernandez1; Matthew Guziewski1; Anit Giri1; Daniel Field1; 1U.S. Army Research Laboratory
    In order to take full advantage of magnetic field as a processing parameter it is necessary to be able to predict the change of material behavior (such as phase transformation temperatures) as a function of applied magnetic field, processing temperature, and composition. Such methodology will aid in determining which materials/alloys will be most responsive to applied magnetic field. Here we focus on Fe-X and Fe-X-C alloys (where X= Co, Ni, Mn, etc.) and their austenite/ferrite and martensitic transformations. We have also collated the necessary magnetic properties of these alloys, namely magnetic moment and Curie temperature as a function of alloy content. The application of alternate magnetization models (Kuz’min, Arrott) to the typically used Weiss Molecular Field Theory allows for the improved prediction of magnetic field assisted phase transformations in addition to a larger compositional and temperature range than previously studied.

10:10 AM Break