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Meeting 2021 TMS Annual Meeting & Exhibition
Symposium AI/Data informatics: Design of Structural Materials
Presentation Title High-throughput Alloy Design via Additive Manufacturing
Author(s) Olivia Dippo, Kevin Kaufmann, Grant Schrader, Kenneth Vecchio
On-Site Speaker (Planned) Olivia Dippo
Abstract Scope 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, can be coupled 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.
Proceedings Inclusion? Planned:
Keywords Additive Manufacturing, Characterization,

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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