6th World Congress on Integrated Computational Materials Engineering (ICME 2022): Applications: Materials Design & Alloy Modification 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: Martis Peak
Location: Hyatt Regency Lake Tahoe
Session Chair: Katherine Sebeck, US Army Ground Vehicle Systems Center
8:00 AM Invited
Accelerated ICME Design and Development of a New Single Crystal Ni Alloy with High Performance and Processibility: Jiadong Gong1; 1QuesTek Innovations LLC
To improve the efficiency of advanced power systems, Integrated Computational Materials Engineering (ICME) tools are being developed at QuesTek Innovations LLC for the design of high-performance alloys for gas turbine. In this work, we detail progress on the design of a low-Re, highly-castable, creep-resistant single-crystal Ni-based superalloy (QTSX). CALPHAD-based indicators for castability and creep resistance as well as many other design criteria were simultaneously employed to predict an optimum alloy composition. Component-level QTSX trail castings have been fabricated and characterization of the castings has demonstrated freckle-free solidification and creep resistance comparable to CMSX4 and ReneN5, validating this accelerated ICME approach. The accelerated development greatly speedy up the qualification process using the modelling results as guidance for targeted experiments, which paves way for higher TRL and great potential for commercialization.
8:30 AM
Alloy Design Using Integrated High-Throughput Additive Manufacturing, Characterization, and Computation: Olivia Dippo; Kevin Kaufmann1; Kenneth Vecchio1; 1University of California, San Diego
Establishing a commercially viable alloy from lab-scale alloy development research typically takes decades, in part due to the typical one-sample-at-a-time approach of sample synthesis, preparation, characterization, and analysis. To accelerate alloy development, we have designed an integrated, high-throughput method focused on parallelizing, miniaturizing, and automating each of these steps. In this method, alloys are selected using a CALPHAD-based alloy design algorithm. Then, test samples are built using laser metal deposition in 15-sample libraries with a unique geometry that facilitates rapid characterization. Samples can then be automatically prepared and characterized by XRD, EDS, and EBSD. These analyses, combined with various material property assessments, are then integrated with machine learning techniques to accelerate future materials analysis and guide subsequent composition and processing decisions. This method is applied to functionally graded metallic materials as well as discrete libraries of materials to better understand alloy development and improve predictive capabilities.
8:50 AM
Accelerated HEA Development and Evaluation via Combined Approach of Additive Manufacturing, Machine Learning, and Thermodynamic Modeling: Phalgun Nelaturu1; Jason Hattrick-Simpers2; Thien Duong3; Michael Moorehead1; Santanu Chauduri3; Adrien Couet1; Dan Thoma1; 1University of Wisconsin; 2National Institute of Standards and Technology; 3Argonne National Laboratory
Additive manufacturing via directed energy deposition was employed as a high-throughput technique to synthesize alloys in the Cr-Fe-Mn-Ni quaternary system. 120 alloy compositions were synthesized in a week, exploring a vast portion of the composition space. Tight compositional control within ±5 at% and <0.2% unmelted powder fraction were achieved. The rapid synthesis combined with rapid heat treatment, characterization, and nano- and micro-hardness measurements enabled high-throughput evaluation of these materials. The large dataset of experimentally measured properties were used to develop a predictive hardening model using active machine learning algorithm coupled with thermodynamic modeling. The overall high-throughput framework being developed for material discovery will be presented along with important insights into advantages of coupling blind machine learning with physics-based modeling.
9:50 AM Break