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Meeting 2021 TMS Annual Meeting & Exhibition
Symposium AI/Data informatics: Design of Structural Materials
Presentation Title Structural Response Statistics of Deformed Polycrystals Leading to Rare Events
Author(s) Curt A. Bronkhorst, Peter Marcy, Hansohl Cho, Scott Vander Wiel, Satyapriya Gupta, Veronica Anghel, George Gray
On-Site Speaker (Planned) Curt A. Bronkhorst
Abstract Scope We know qualitatively that the structure of metallic polycrystals has a strong impact on its response to mechanical loading. The heterogeneous nature of aggregate composite materials leads to highly statistical structural response at length scales related to grain size and dislocation interaction. A two-times difference between minimum and maximum von Mises stress for deformed cubic materials is typical. The von Mises stress variability is also very high in the vicinity of grain boundaries and triple lines and remains so for several microns away. Peach-Koehler force anomalies such as split screw dislocation core found in some materials leading to non-Schmid effects alters the statistical response of materials. Grain size effects can also alter the statistical response of materials in ways which we a just now beginning to explore. Quantitative results using advanced theories for granular behavior will be presented. We will also discuss implications for engineering models and material design.
Proceedings Inclusion? Planned:

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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