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Meeting 2023 TMS Annual Meeting & Exhibition
Symposium Accelerated Discovery and Insertion of Next Generation Structural Materials
Presentation Title Computational Design of High Entropy Alloy Hardmetals
Author(s) Joshua Berry, Robert Snell, Magnus Anderson, Olivier M.D.M. Messe, Iain Todd, Katerina A. Christofidou
On-Site Speaker (Planned) Joshua Berry
Abstract Scope High Entropy Alloys (HEAs) present an opportunity for the design and development of new wear resistant hardmetals, to replace the conventional WC-Co cemented carbides, used in demanding metal forming applications. However, the vast compositional space occupied by HEAs, results in unguided experimental searches being unfeasible. Here, a random forest machine learning architecture, in conjunction with the CALPHAD method, is trained from experimental HEA databases, to perform high-throughput phase formation and hardness predictions. Nine of the hardest predicted FCC solid solution forming HEA compositions from the machine learning model were selected and fabricated. Mechanical and thermal assessments of these selected alloys will demonstrate their potential suitability for WC-Co replacement, while simultaneously enabling comparison and verification of the machine learning methodology and providing further data for future model development. This work was supported by Oerlikon AM Europe GmbH, Engineering and Physical Sciences Research Council UK [EP/S022635/1] and Science Foundation Ireland [18/EPSRC-CDT/3584].
Proceedings Inclusion? Planned:
Keywords High-Entropy Alloys, Machine Learning,

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Design Space for Tunable Ceramic-polymer Composites
A Diffusion Couple Approach to β-Ti Alloy Development: Evaluating the Oxidation Performance of Ti-Fe-X+ Alloys
A High-throughput Setup for Materials Exposure to Simultaneous Irradiation-corrosion Conditions
Accelerated Discovery of Novel Titanium Alloys using High-throughput Manufacturing, Characterization and Testing
Accelerating Multimodal Data Collection: A Workflow for Metallic Films
AI and Machine Learning Tools for Development and Analysis of Image Driven 2D Materials
Combinatorial Mechanical Microscopy via Correlated Nanoindentation and EDX Mapping
Computational Design of an Ultra-strong High-entropy Alloy
Computational Design of High Entropy Alloy Hardmetals
Design of a Compact Morphology Cobalt-based Superalloy for Additive Manufacturing
Efficient Conductivity and Hardness Optimization in Cu-Ag-Ni Alloys using Bayesian Active Learning
High-throughput Electric-Field-assisted Sintering and Characterization Techniques for Materials Discovery
High-throughput Prediction of Fracture and Brittle to Ductile Transition in Tungsten using Variable Temperature Nanoindentation
High-throughput Synthesis and Mechanical Characterization of Sputtered Metallic Alloys
How Should You Select an Algorithm for a Materials Discovery Campaign with Multiple Objectives, Complex and High-dimensional Structure-processing-property Relationships, and a Small Adaptive Design Budget?
Machine Learning-assisted Discovery of Novel High Temperature Ni-rich NiTiHfZr Multi-component Shape Memory Alloys
Rapid Characterisation of Active Slip Systems in Titanium Ordered-bcc Compounds using an Algorithm for Automated Indentation Slip Trace Analysis.
Using Machine Intuitive Learning to Predict Advanced Steel Properties

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