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Meeting 2026 TMS Annual Meeting & Exhibition
Symposium Novel Strategies for Rapid Acquisition and Processing of Large Datasets From Advanced Characterization Techniques
Presentation Title High-Throughput Crystallography by Quantitative Large-Area Polarized-Light Microscopy
Author(s) Brian G. Hoover, Cesar H. Ornelas-Rascon
On-Site Speaker (Planned) Brian G. Hoover
Abstract Scope New techniques in optical microscopy can quantify microstructure at exceptionally high rates, exceeding 40,000 pps, also reducing sample-preparation overhead relative to electron microscopy. This data rate, including image processing, is demonstrated by c-axis imaging of titanium alloys, utilizing a laser-based ellipsometric polarized-light microscope (EPLM) (CrystalView(TM)) with 5um spatial resolution. Reduced sample preparation is proven with a 3"x5" Ti64 plate that was electropolished. Credentials for high quality, quantitative, and actionable microstructural data are then demonstrated, first by seamless stitching to obtain c-axis images over several square inches, without loss of resolution, then custom grain segmentation that eliminates optical non-linearities near grain boundaries. Further examples highlight large-area grain statistics (LAGS) of HCP and cubic alloys, the latter following chemical etching. The unique capability of EPLM, compared to traditional PLM, to map orientation over the complete hemisphere is demonstrated diagrammatically and experimentally, offering more complete crystallographic characterization for downstream computational analysis and modeling.
Proceedings Inclusion? Planned:
Keywords Characterization, Other, Titanium

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Topology Optimized Specimen that Provides the Yield Surface in a Single Test
Advancements in Titanium Microstructure Characterization: Integrating AI for Automated Complex Quantification
Advances in Analytical S/TEM-Based Workflows: Emerging Techniques for Smarter Data Acquisition and Processing
Advances in Instrumentation for Spatially Resolved Acoustic Spectroscopy (SRAS)
Advancing Segregation Characterization in Steel and Alloys: A Novel BEX-EDS-SEM Approach
Affine Transformations to Correlate Experimental and Simulated EDS Spectra for Multi-Element Systems
Automated 2D Microscopy Workflows for Multimodal Characterization of Structure, Crystallography and Composition
Comprehensive Microstructural Characterization of Aging-Treated Aluminum Alloy AA2024 Using Integrated SEM, STEM, EDS, and EBSD Workflows
Conditional Diffusion Models for Microscopy Modality Transfer of Electron Backscattering Microscopy Diffraction Misorientations Maps from Optical Microscopy
Discovering Hidden Fingerprints in Multimodal Process-Structure-Property Data via Joint Embedding
Emphasizing the Importance of Data Exchange in Constructing a Digital Twin for Metals-AM
Enabling Data Starved Microstructural Segmentation With Foundation Models as First-Pass Segmentors in Low-Contrast Al-Si Solidification
Examining Growth Twinning in Ni-Based Films via a High-Throughput Methodology
Frequency-Domain Thermoreflectance Automation for High Throughput Microstructural and Thermal Characterization
High-Throughput Crystallography by Quantitative Large-Area Polarized-Light Microscopy
High-Throughput Exploration of Large Material Design Spaces Using Small Samples and Bayesian Strategies
High-Throughput Processing and Accelerated Characterization of Cu–Ti Alloy
High-Throughput Time-Resolved X-Ray Computed Tomography to Characterize Flaw Evolution in LPBF Parts During Creep
High Throughput Texture Analysis of Quartz via Automated Polarized Reflective Light Microscopy
Increasing the Throughput of Ultra-High Temperature Ceramic Tensile Testing
Lightweight Services and Federated Storage for Data-Intensive Structural Materials Research
Linking Energetic Material Sensitivity and Microstructure Variability Across Length Scales
Machine Learning Assisted Structure-Property Relationships by Nanoindentation
Mapping Microstructure to Field Properties in Materials Under Dynamical Loading
Nanocrystalline Films: Imaging, Orientation Mapping, Machine Learning and Data Analytics
New Strategy of Surface Defect Detection in Metallic Coatings Using a Machine Learning-Based Model
Same-Day Product Quality Assessment of CaCl₂-Assisted Direct Reduction of Chromite via µCT and Deep-Learning Segmentation Against a Reference Dataset
Segmentation Methods for Tracking Dislocation Dynamics

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