Conference Tools for 2020 TMS Annual Meeting & Exhibition

Help

About this Abstract
Meeting	2020 TMS Annual Meeting & Exhibition
Symposium	Characterization: Structural Descriptors, Data-Intensive Techniques, and Uncertainty Quantification
Presentation Title	A New Crystallographic Defect Quantification Workflow via Advanced-microscopy-based Deep Learning
Author(s)	Yuanyuan Zhu, Graham Roberts, Rajat Sainju, Colin Ophus, Brian Hutchinson, Danny Edwards, Mychailo Toloczko
On-Site Speaker (Planned)	Yuanyuan Zhu
Abstract Scope	Crystallographic defects, in particular dislocations, voids and precipitates are critical to the bulk physical and mechanical properties of metals and alloys. Transmission electron microscopy (TEM) is a standard tool for defect characterization at the nanometer scale, however, quantitative analysis of enough TEM images to provide a statistically satisfactory representation of these defects over a range of synthesis and deformation conditions can often be a time-consuming daunting task. In this talk, we propose a new workflow of high-throughput defects analysis demonstrated using a HT-9 martensitic steels. One unique advancement of our approach involves systematic improvement in all three key steps, including the development of a high-clarity defect imaging technique free of bend contour, a novel convolutional neural network (CNN) model-the MetalDefectNet-for defect identification, and a dedicated MATLAB algorithm for defect quantification. We envision that this new workflow can provide a more reliable and statistically meaningful experimental foundation for metallurgy.
Proceedings Inclusion?	Planned: Supplemental Proceedings volume

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

100 Years of Scherrer Modifications: Demystifying Diffractogram Width Analyses for Nanocrystalline Materials

3D Morphological Characterization of Porous Cu by Vapor Phase Dealloying Zn-Cu Alloys

A New Crystallographic Defect Quantification Workflow via Advanced-microscopy-based Deep Learning

Advancement of Data Intensive Approaches in Materials Discovery and Design

Adversarial Networks for Microstructure Generation and Modeling Phase Transformation Kinetics

Application of Machine Learning to Microstructure Quantification and Understanding

Artificial Intelligence Approaches to Microstructural Science

Automated Anomaly Detection in Unlabeled Computed Tomography Images

Basis Functions for Quantifying Grain Boundary Texture in Polycrystalline Microstructures

Characterizing GB Atomic Structures at Multiple Scales

Characterizing the Energetics and Structural Configurations of Silicon Carbide Grain Boundaries Using High-throughput Atomistic Techniques

Deep Convolutional Networks for Image Reconstruction from 3D Coherent X-ray Diffraction Imaging Data

Determination of Representative Volume Elements for Small Cracks in Heterogeneous Domains via Convolutional Neural Networks

Feature Engineering of Material Structure for Extracting Process-structure-property Linkages

GB Property Localization: Inference and Uncertainty Quantification of Grain Boundary Structure-property Models

Higher Order Spectral Terms in Grain Boundary Networks

Indexing of Electron Back-Scatter Diffraction Patterns Using a Convolutional Neural Network

Integrated Structural Methods Addressing Aviation Challenges in Composites

Investigating the Atomistic Nature of Grain Boundary Failure

Investigating the Effect of Solute Segregation to Grain Boundaries in Nanocrystalline Alloys Toward Stability and Strengthening

Investigations of Microstructural Effects on Porosity Evolution

Large-scale Defect Contrast Simulations for Scanning and Transmission Electron Microscopy

Large Scale Microstructure Synthesis Using LEGOMAT: Application to Additive Manufacturing

Machine Learning and Electron Backscatter Diffraction

Machine Learning Approach for On-the-fly Crystal System Classification from Powder X-ray Diffraction Pattern

Machine Learning Approaches to Image Segmentation of Large Materials Science Datasets

Machine Learning Reinforced Crystal Plasticity Modeling of Titanium-Aluminum Alloys under Uncertainty

Methods for the Correction of Epistemic Resolution Error through Data Collection Process Simulations

Microstructural Evolution Along Geodesics

Monte Carlo Studies of EBSPs Spectroscopy

Neural Networks for Real-time Processing of Scanning Transmission Electron Microscopy Data

Parametric Models for Crystallographic Texture: Estimation and Uncertainty Quantification

Predicting Compressive Strength of Consolidated Solids from Features Extracted from SEM Images

Predicting Crack Location Using a Radial Distribution Function as a Unique Descriptor of Pore Networks

Predicting Microstructure-sensitive Fatigue-crack Path in 3D Using a Machine Learning Framework

The Grain Boundary Octonion: Metrics, Paths, and Fundamental Zones

Uncertainty Propagation in a Multiscale CALPHAD-reinforced Elastochemical Phase-field Model

Uncertainty Quantification of Far-field HEDM Measurements

Uncertainty Quantification Techniques Applied to Ductile Damage Predictions in the 3rd Sandia Fracture Challenge

Utilizing Convolutional Neural Networks for Prediction of Process and Material Parameters from Microstructural Images

X-Ray Computed Tomography of 3D Crack Lattices in Advanced Ceramics and their Effect on Mechanical Response

Questions about ProgramMaster? Contact programming@programmaster.org