ProgramMaster Logo
Conference Tools for 2024 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 2024 TMS Annual Meeting & Exhibition
Symposium Novel Strategies for Rapid Acquisition and Processing of Large Datasets from Advanced Characterization Techniques
Presentation Title Directional Reflectance Microscopy: Beyond Conventional Crystal Orientation Mapping
Author(s) Matteo Seita
On-Site Speaker (Planned) Matteo Seita
Abstract Scope Directional reflectance microscopy (DRM) is an optical technique that enables orientation mapping of crystalline surfaces in a similar fashion to conventional diffraction-based methods such as electron backscatter diffraction. DRM involves acquiring a series of optical micrographs of chemically etched solids under variable illumination angle. By analyzing the angle-dependent reflectivity of the surface, it is possible to compute the local crystal orientation at a spatial and angular resolution of ~5 Ám and ~5°, respectively. Besides the reduced cost of the hardware and the simplified sample preparation compared to conventional methods, DRM offers unparalleled capabilities to map crystal orientation on the curved surface of life-sized engineering components. These features make DRM a candidate characterization technique for in-line quality control and microstructure digitization across the industry.
Proceedings Inclusion? Planned:

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Framework for the Optimal Selection of High-Throughput Data Collection Workflows by Autonomous Experimentation Systems
Advanced Mechanical Properties Prediction of Functionally Graded Materials through High-Throughput Characterization.
Advances in Atom Probe Crystallographic Analysis
Connectivity of Experimental Equipment and Interoperability of Experimental Data: Challenges and Opportunities
Data-driven Discovery of Dynamics from Time-resolved Coherent Scattering
Data Management in Additive Manufacturing – Lessons Learned and Opportunities
Data Management, Data Sharing and the Future of Federal Research Funding
Deep Learning-Driven Semantic Segmentation of large 4D Lab-Scale X-ray Tomography Data for Quantification of Microstructural Features
Directional Reflectance Microscopy: Beyond Conventional Crystal Orientation Mapping
Enabling Uninterrupted In-situ X-ray Experiments through Rapid Data Feedback and On-the-fly Experiment Optimization
G-19: Accessing the Microstructure State Space
G-20: TESCAN TENSOR a 4D-STEM for Multimodal Characterization of Challenging and Interesting Specimens
Galaxy: A Critical Framework for Large Data Volumes and Data-intensive Processing in the Synchrotron World
Hierarchical Bayesian Data Analysis for Accelerating Structural Materials Characterization
HPC+AI@Edge Enabled Real-Time Materials Characterization
Melt Pool Quantification from In Situ Radiography of Directed Energy Deposition of Nickel Superalloys
New Strong and Ductile Titanium-oxygen-iron Alloys Enabled by AM and Insights from Multiscale Microscopy
Probabilistic Orientation Analysis via Direct ODF Calculation from Far Field HEDM
Quantitative 2D and 3D Characterization of Precipitates Microstructure in the Additively Manufactured Titanium Alloy
Real-Time In-Situ Characterization with Web Technologies at Any Scale
Streamlining Engineering Diffraction Analysis Using the MAUD Interface Language Kit (MILK)
Understanding Relaxation Dynamics Beyond Equilibrium Using AI-Informed X-ray Photon Correlation Spectroscopy
Using Video Games for Training Data on Microstructural Design
Utilizing Advanced Computer Vision Techniques Based on Machine Learning and Artificial Neural Networks to Process Micrographs of Ni-base Superalloys
Utilizing Deep Learning Techniques to Accelerate X-ray Absorption and Diffraction Contrast Imaging

Questions about ProgramMaster? Contact programming@programmaster.org