ICME 2023: App: Alloy Des. II
Program Organizers: Charles Ward, AFRL/RXM; Heather Murdoch, U.S. Army Research Laboratory
Thursday 9:00 AM
May 25, 2023
Room: Caribbean IV
Location: Caribe Royale
Session Chair: Katherine Sebeck, US Army Ground Vehicle Systems Center
9:00 AM Invited
Alloys-by-Design: Accelerating the Discovery and Deployment of Alloys to Address Future Demands for Increased Performance and Sustainability: David Crudden1; Rory Rose2; Farsad Forghani2; Shohreh Khorsand2; Sajjad Amirkhanlou2; 1Alloyed Inc.; 2Alloyed, Inc.
There is a constant demand for materials and manufacturing technologies which increase performance and efficiency in engineering applications. As many industries go through significant transformations, for example, electrification in the automotive industry, digital transformation in the manufacturing sector or decarbonization in metal processing, rapid discovery of new materials to enable this transformation is critical. Alloyed is addressing the need for rapid materials discovery using its Alloys-by-Design (ABDŽ) computational platform. We will present application of our platform to selected materials design problems with an emphasis on decarbonization and digital manufacture. Impact on decarbonization in metal processing is demonstrated through design of an aluminium casting alloy that does not require heat-treatment, resulting in reduced CO2 emissions and processing costs without compromise in performance. Alloy design for digital manufacturing will be discussed with reference to computational optimization of alloys for additive manufacturing. The role data-science and artificial intelligence methodologies may have in extending current capabilities to revolutionize the way we design and qualify materials in the future is also considered.
9:30 AM
Material And Process Parameter Optimization for Additive Manufacturing Using High-throughput Kinetic Simulations: Evgeniya Kabliman1; Nora Barschkett1; Sebastian Tonatiuh Carrion Ständer1; 1Technical University of Munich
Different material modeling approaches are applied in the computational materials design to describe the process-structure-property relationship across the length scale. Among them, the CALPHAD (CALculation of PHAse Diagrams) approach plays an important role. Using this approach, one can predict in a very short time the distribution of phases (type, amount) which can be expected in the studied material depending on the chemical composition and manufacturing conditions, e.g. temperature. Recent developments demonstrate how to use CALPHAD for the screening of a large number of chemical compositions by performing the equilibrium and non-equilibrium (Scheil-Gulliver type) high-throughput calculations. The present work demonstrates how to extend this approach by considering the precipitation kinetics in the solid state. The focus is on the optimization of chemical composition and heat treatment parameters for additively manufactured metallic alloys. The developed tool can be however also applied to other manufacturing processes like casting or hot deformation.
9:50 AM
Sustainable Aluminum Alloy Design Using Physics-informed Machine Learning: Fatih Sen1; Marat Latypov1; Heath Murphy1; Dasha Artsykhovska1; Kyle Haines1; Shruthi Raj1; Aurele Mariaux1; Sazol Das1; Yudie Yuan1; Vishwanath Hegadekatte1; 1Novelis
Aluminum has been increasingly the sustainable material of choice for aerospace, automotive and beverage cans due to its high strength-to-weight ratio and infinite recyclability. Aluminum recycling is surging, and more end of automotive life aluminum scrap will be available in the market. To minimize prime aluminum use, design of new aluminum alloys which can utilize more diverse sources of scrap is needed, while simultaneously satisfying the performance requirements for desired applications. In the present work, we have coupled scrap mass flow model with a physics-informed machine-learning framework to design sustainable aluminum alloys. Microstructural features pertaining to strength, formability, and corrosion properties we estimated using CALPHAD methods and integrated into machine learning framework. The model predictions for sustainable alloy chemistries were then validated through lab trials.
10:10 AM
ICMD: ICME-based Genomic Materials Design: Jiadong Gong1; 1Questek Innovations LLC
Sixty years of academic collaboration and thirty years of commercialization by a network of small businesses have delivered a mature technology of computational materials design and accelerated qualification grounded in the CALPHAD system of fundamental databases now known as the Materials Genome. The national Materials Genome Initiative acknowledging the reality of this technology has spurred global interest and rapid adoption by US apex corporations. Designed materials with broad market impact now span a range from consumer electronics to space exploration. Ongoing design addresses the new alloys enabling new manufacturing methods such as 3D printing as well as the materials supporting affordable approaches to sustainability. The advanced design and qualification software system, ICMDŽ, developed at QuesTek is now prepared for commercial application.
10:30 AM Cancelled
An Integrated Process-structure-property Framework for In-silico Design of Additively Manufactured 18Ni-300 Maraging Steels: Akash Bhattacharjee1; Pravin Kumar1; Himanshu Nirgudkar1; Surya Ardham1; Pramod Zagade1; Gerald Tennyson1; BP Gautham1; 1TCS Research, Tata Consultancy Services Limited
Processing parameters such as laser power, hatch distance, scan speed and baseplate temperature govern the final mechanical properties of an additively manufactured component. Process-structure-property (P-S-P) maps can guide in selection of process parameters to tailor the microstructure for desired properties of the component. However, the available information pertaining to microstructure evolution during non-equilibrium conditions is limited. This study is aimed at developing process-structure-property maps for selective laser melting of 18Ni-300 maraging steel using a combination of theoretical solidification models in the context of non-equilibrium solidification and prediction of grain sizes, phase fractions and estimation of microhardness from the resultant microstructure parameters. A parametric study of the effect of above-mentioned processing parameters on the final microstructure parameters will be presented. The PSP maps are then overlayed on the processing defect maps to assess the printability of the component. The potential of this in-silico approach to tailor site-specific properties will be presented.
10:50 AM Break