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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 Advances in Instrumentation for Spatially Resolved Acoustic Spectroscopy (SRAS)
Author(s) Nathan Hill, Mathew Hayne, Adam Wachtor, Richard Panduro-Allanson, Dan Hooks
On-Site Speaker (Planned) Nathan Hill
Abstract Scope Characterizing microstructure of metals is a necessary process in metallurgical manufacturing for determining material properties of an object. Spatially Resolved Acoustic Spectroscopy (SRAS) is a method for non-destructively characterizing microstructure providing similar information to that which you get from Electron Backscatter Diffraction (EBSD). The primary differences between these techniques come from the use of lasers and surface acoustic waves in SRAS vs electron diffraction in EBSD. SRAS has the advantages of being fully non-destructive, requiring little to no surface preparation, being performed in open air as opposed to vacuum, and being able to scan entire objects in a matter of hours. In this work we introduce the fundamentals of the technique and discuss improvements to established SRAS methodology using an array of instrumentation not currently discussed in the literature. These improvements in instrumentation are focused on improving the speed, resolution, and versatility of SRAS for applications in metallurgical manufacturing.
Proceedings Inclusion? Planned:
Keywords Characterization, Mechanical Properties, Modeling and Simulation

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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