ProgramMaster Logo
Conference Tools for 2023 TMS Annual Meeting & Exhibition
Login
Register as a New User
Help
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools
About this Abstract
Meeting 2023 TMS Annual Meeting & Exhibition
Symposium Algorithm Development in Materials Science and Engineering
Presentation Title An Automated Approach to Data Extraction for SMAs
Author(s) Dylan Kennedy, Aaron Stebner, Branden Kappes
On-Site Speaker (Planned) Dylan Kennedy
Abstract Scope Modern materials research has been revolutionized by machine learning (ML) which uses large amounts of data to predict the properties of new materials. The analysis of this data represents a significant bottleneck in the development of ML models. This is especially true for shape memory alloys (SMAs), where phase transformations need to be characterized in addition to standard thermo-mechanical properties. Automating the data extraction process of database building can serve as a solution to this issue, allowing faster model deployment. Additionally, rapidly analyzing experimental data can allow databases to be established from larger and more densely populated search spaces, as these experiments can be easily performed if the desired data does not exist in literature. This work aims to develop a tool that will autonomously extract desired properties of SMAs from raw data collected from a wide array of experiments for use in ML models.
Proceedings Inclusion? Planned:

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A New AI/ML Framework for Materials Innovation
A Non-local Formulation of the Elastoplastic Self-consistent Crystal Plasticity Model: Applications to Modeling Deformation and Recrystallization
A Peridynamic-based Approach to Study the Influence of Oxide on Impact and Bonding in Cold Spray
A Recursive Grain Remapping Scheme for Irregular Morphologies in Phase-Field Models
Algorithms for Computing Diffraction Patterns from Dislocation Networks Generated via Discrete Dislocation Dynamics Simulations
An Automated Approach to Data Extraction for SMAs
An OpenMP GPU-Offload Implementation of a Cellular Automata Solidification Model for Laser Fusion Additive Manufacturing
Applications of Min-cut Algorithms for Image Segmentation and Microstructure Reconstruction
Characterization of the Evolution of the Grain Boundary Network Using Spectral Graph Theory
Characterizing Microstructure Evolution in Latent Space for Machine Learning Applications
Coupling of a Multi-GPU Accelerated Elasto-visco-plastic Fast Fourier Transform Constitutive Model with the Implicit Finite Element Method
Crystal Plasticity Finite Element Analysis of Crystalline Thermo-mechanical Constitutive Response
Data-Driven Bayesian Model-Based Prediction of Fatigue Crack Nucleation in Ni-based Superalloys
Data-driven Plastic Anisotropy Predictions Using Crystal Plasticity and Deep Learning Models
Data Assimilation for Estimation of Microstructural Evolution during Solid-state Sintering: Integration of Phase-field Simulation and In-situ Experimental Observation
Development of Structure-property Linkages for Damage in Crystalline Microstructures Using Bayesian Inference and Unsupervised Learning
Diffuse Interface Technique to Simulate Fluid Flow and Characterize Complex Porous Media
EAM-X: Simple Parameterization of Embedded Atom Method Potentials for FCC Metals and Alloys
EAM-X: Universal trends in FCC Grain Boundary Energies
Enabling Long Timescale Molecular Dynamics Simulation with ab initio Precision
Exascale Fracture Mechanics with Peridynamics
Finite Element Implementation of a Dislocation Thermo-mechanics Model: Application to Study Dislocation Structure Evolution during Laser Scanning
Investigating Magnetic Phase Transitions with Ising Models Accounting for Long-range Spin Interactions
M-14: Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing
Machine Learning Models of Effective Properties with Reduced Requirements on Microstructure
Microstructure-Sensitive Calculations of Metal Nanocomposite Electrical Conductivity
Modular and Scalable Solutions for Training Machine Learned Interatomic Potentials
Multifaceted Uncertainty Quantification for Structure-property Relationship
Multiphase Microstructure-based Modeling for Rolling Contact Fatigue Life Prediction
Novel Multi-scale Plasticity Modeling Using Defect Dynamics Element Method (DDEM)
Persistent Homology for Topological Quantification of Microstructure
Prediction of Cutting Surface Parameters in Punching Processes aided by Machine Learning
Prediction of Mechanical Properties in a Bulged and Annealed Steel Tube through a Multiscale Modeling Approach Based on CPFEM
PyEBSDIndex: Fast Indexing of EBSD data
Symmetry Relation Database and Its Application to Ferroelectric Materials Discovery
Thermographic Process Classification in Electron Beam Additive Manufacturing via Stacked Long Short-Term Memory Networks
Training Machine-learned Interatomic Potentials for Chemical Complexity - Application to Refractory CCAs
Questions about ProgramMaster? Contact programming@programmaster.org