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Meeting 2021 TMS Annual Meeting & Exhibition
Symposium AI/Data informatics: Design of Structural Materials
Presentation Title Design of Ti-Al-Cr-V Alloys for Maximum Thermodynamic Stability
Author(s) Rajesh Jha, George S. Dulikravich
On-Site Speaker (Planned) Rajesh Jha
Abstract Scope Chemistries of Ti-Al-Cr-V alloys were computationally Pareto-optimized for simultaneously maximizing the Young’s modulus and minimizing density for a range of temperatures. Compositions at different temperatures of these alloys were then analyzed for phase stability in order to generate new data for compositions and volume fractions of stable phases at various temperatures. This resulted in a large dataset where a lot of data were still missing as all the phases are not stable at a given temperature for all the compositions. The concept of Self Organizing Maps was then applied to correlate alloy compositions, stabilities of desired phases at various temperatures, associated Young’s moduli and densities, and the effect of the composition of phases on these properties. This work should help alloy designers to determine the required chemical composition of a new alloy with reference to the temperature of application and see the effect on stable phases and properties of alloys.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, High-Temperature Materials, Titanium

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Physics-informed Bayesian Experimental Autonomous Researcher for Structural Design
Alloy Design for Additive Manufacturing
Combined Statistical and Energetic Approach to Understand Grain Boundary Embrittlement for Segregation Engineering
Data-driven Approaches for Automated Analysis of Non-metallic Inclusions that Form during Steel Processing
Data Science Approaches for Microstructure-property Connections in Structural Materials
Design of Ti-Al-Cr-V Alloys for Maximum Thermodynamic Stability
Discovery of Optimized ω-phase Free Ti-based Alloys Using CALPHAD and Artificial Intelligence Approach
Evaluating Uncertainty in Clustering of Nanoindentation Mapping Data
Fast and High-throughput Synthesis of Film and Bulk High-entropy Alloys
High-throughput Alloy Design via Additive Manufacturing
High-throughput Calculation to Predict the Eutectic Point in Quaternary System
Incorporating Historical Data & Past Analyses for Improved Tensile Property Prediction of 9% Cr Steel
Machine Learning Approach to Understanding Abnormal Grain Growth
Machine Learning Assisted Exploration of FeCoCrNi Based Nanocrystal-amorphous Dual-phase Alloys
Machine Learning for the Recognition and Synthesis of Polycrystalline Metal Microstructures
Model Reification with Batch Bayesian Optimization
Multi-objective Lattice Optimization Using an Efficient Neural Network Approach
Physics-informed Data-driven Machine Learning Approach for Mesoscale Materials Science
Prediction of the Mechanical Properties of Aluminum Alloy Using Bayesian Learning for Neural Networks
Solving Inverse Problems for Process-structure Linkages Using Asynchronous Parallel Bayesian Optimization
Structural Response Statistics of Deformed Polycrystals Leading to Rare Events
Topology Optimization for Design of Stress-dependent Material Properties
Unsupervised ML to Bridge Molecular Dynamics and Phase field Simulations
Using Machine Learning for Targeted Alloy Design in High Entropy Composition Spaces
Zoning Processing Spaces for Additive Manufacturing: Applications for Inverse Design

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