6th World Congress on Integrated Computational Materials Engineering (ICME 2022): Applications: Advanced Manufacturing – Additive Manufacturing III
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 3:50 PM
April 26, 2022
Room: Regency Ballroom AB
Location: Hyatt Regency Lake Tahoe

Session Chair: Ji-Cheng Zhao, University of Maryland

3:50 PM
Design by Fluid Flow Science for Multi-Metals Additive Manufacturing: Chinnapat Panwisawas¹; Junji Shinjo²; ¹University of Leicester; ²Shimane University
    Multi-metals additive manufacturing (AM) is an emerging research underpinning numerous technologies including AM repair of dissimilar metals and in-situ alloying. Understand the fluid dynamics of multi-metals powders and laser using powder-bed AM as a function of AM process parameters through a high-fidelity multi-physics modelling rationalises the multi-metals thermal-solutal-fluid flow and allows the link between composition and flow science to be constructed. The results provide insights into elemental powder AM and in-situ alloying of NiAl, NiTi and TiAl binary metals. The melt flow dynamics is characterised by dimensionless Reynolds (Re) and Péclet (Pe) numbers. The mass loss rate has been found to be governed by the Re with a simple proportional correlation linked with AM process behaviour. In in-situ binary alloying, convective mixing further complicates the mass loss and flow dynamics. The novel materials-process design developed will lead to AM design of optimum final product composition and mitigation of defect formation.

4:10 PM
Towards to an ICME Approach for the Discovery of Lightweight High Entropy Alloys: Shengyen Li¹; Jianliang Lin; John Macha; Mirella Vargas; Michael Miller; ¹Southwest Research Institute
    This presentation will discuss the feasibility of integrating high throughput experiments (HTE) with computational approaches to discover composition spaces for future developments of lightweight high entropy alloys (LHEAs). The objectives are to reduce density by 25% while the mechanical properties comparable to Ni-based superalloys for high temperature applications. To explore the composition space cost-effectively, the first iteration of the material discovery focuses on data gathering, knowledge managing, and design of experiments. A Python tool is developed to parse, analyze, and save data from literatures and experiments. The statistical functions and machine learning algorithms follow the data gathering to clean-up and map-out high dimensional information for the development of the alloy-structure-properties relationships effectively. This informatics system also integrates with a preliminary modeling hierarchy and Monte Carlo tool to select the potential composition space for the subsequent high throughput experimentations. The outcomes guide the iterative experiments to achieve the goal of discovery.

4:30 PM
The ExaAM AM Process-Aware Material Model: James Belak¹; John Turner²; ExaAM Team³; ¹Lawrence Livermore National Laboratory; ²Oak Ridge National Laboratory; ³LLNL, LANL, ORNL
    The Exascale Additive Manufacturing (AM) project (ExaAM) is focused on metal AM process modeling at the fidelity of the microstructure. To date, ExaAM has instantiated exascale-ready continuum simulations of the AM build and melt-resolidification process during the laser scan. These continuum simulations are used to drive microstructure development simulations from which properties are calculated. Using the AMBench-01 problem as a test case, we generate an ensemble of microstructures and properties from which a continuum material model is determined. While potentially unique to the AMBench-01 build, this process-aware material model enables build simulations closer to AM conditions and part-scale qualification simulations in as-built material conditions. Comparison will be made to experiments of microstructure, properties and build. *Work performed under the auspices of the U.S. DOE by LLNL, LANL and ORNL under contracts DE-AC52-07NA27344, DE-AC52-06NA25396, DE-AC05-00OR22725, and supported by ECP (17-SC-20-SC), a collaborative effort of U.S. DOE Office of Science and NNSA.