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Meeting 2024 TMS Annual Meeting & Exhibition
Symposium Novel Strategies for Rapid Acquisition and Processing of Large Datasets from Advanced Characterization Techniques
Sponsorship TMS Structural Materials Division
TMS: Advanced Characterization, Testing, and Simulation Committee
Organizer(s) Sriram Vijayan, Michigan Technological University
Rakesh R. Kamath, Argonne National Laboratory
Austin McDannald, National Institute of Standards and Technology
Fan Zhang, National Institute of Standards and Technology
Sarshad Rommel, University of Connecticut
Scope Quantification and correlation of microstructural data to material properties and process variables are key to the design of novel materials and optimization of advanced manufacturing processes. The investigation of the evolution of microstructural features (size, morphology, and chemistry) across different length and time scales in novel material systems and materials subject to advanced manufacturing processes demand the need for a thorough multiscale characterization approach, and typically results in large datasets. Recent developments in high-throughput and autonomous experimental approaches combined with advances in instrumentation, computational capabilities and analysis software have compounded the challenge of curating these large datasets. There is an imminent need for development of novel approaches/strategies to extract high quality and actionable microstructural information from these datasets in a rapid and efficient manner. This symposium seeks to bring researchers from industry and academia alike interested in discussing these novel strategies on data obtained from a single or a combination of techniques, which include - optical microscopy (OM), scanning electron microscopy (SEM), scanning/transmission electron microscopy (S/TEM), neutron and synchrotron x-ray-based techniques, atom probe tomography (APT), and x-ray micro-computed tomography (XCT).


Topics include, but are not limited to:
* Advances in methods for feature extraction and quantification from large imaging datasets (OM, SEM, S/TEM, radiography, tomography) and their accelerated analysis through computer vision and/or machine learning packages.
* Novel developments in hardware enabling rapid acquisition of microstructural data for high-throughput characterization techniques and analysis workflows for autonomous characterization experiments.
* Utilization of machine learning enabled pipelines for fast reduction and quantification of microstructural information from large imaging, spectroscopy and/or diffraction-based datasets.
* Techniques for tracking and analysis of microstructural evolution in real time or post-facto from in situ characterization datasets
* Workflows for on-the-fly data extraction and feedback for advanced manufacturing routes using in situ monitoring techniques - e.g.- IR thermography, back-scatter electron imaging in additive manufacturing machines.
* Challenges and opportunities related to curation, handling, access and storage of metadata/data from large characterization datasets and the adherence to FAIR data principles.

Abstracts Due 07/15/2023
Proceedings Plan Planned:
PRESENTATIONS APPROVED FOR THIS SYMPOSIUM INCLUDE

A Framework for the Optimal Selection of High-Throughput Data Collection Workflows by Autonomous Experimentation Systems
Accessing the microstructure state space
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
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
Machine learning for advanced microstructure characterization of sigma phases in high entropy alloys
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)
TESCAN TENSOR a 4D-STEM for Multimodal Characterization of Challenging and Interesting Specimens
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


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