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Meeting TMS Specialty Congress 2025
Symposium Joint Sessions of AIM, ICME, & 3DMS
Presentation Title Capabilities and Applications of the Robot-Assisted Serial-Sectioning and Imaging (RASI) System
Author(s) Michael Moschetti, Dirk Bettge
On-Site Speaker (Planned) Michael Moschetti
Abstract Scope The Robot-Assisted Serial-sectioning and Imaging (RASI) system at BAM provides automated, high-resolution 3D microstructural characterization for diverse materials. Integrating robotics with precision sectioning, etching, and optical microscopy, RASI reconstructs large volumes (approaching 15×15×15mm3) with sub-micron detail. This presentation showcases RASI’s versatility through case studies on cast irons, sintered and additively manufactured steels, and ceramic-metallic packages. We demonstrate how RASI reveals true 3D architectures of features like graphite networks, pores, melt pools, and defects, often missed by 2D analysis. These quantitative datasets elucidate process-microstructure-property relationships and provide crucial 'ground truth' for validating computational models and developing digital twins. Ongoing RASI enhancements will also be highlighted.
Proceedings Inclusion? Definite: Post-meeting proceedings

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Architecture for Developing an Image Recognition Model Workflow for Workplace Safety Application
Building a Self-Driving Lab From Scratch
Capabilities and Applications of the Robot-Assisted Serial-Sectioning and Imaging (RASI) System
Customizing the NIMS RDE System for Optimal Data Management
Digital Twins for Accelerated Materials Innovation
Efficient, Coupled Process-Structure-Property Simulations of Additive Manufacturing Using the “Materialize” Framework
Enhancing AI Readiness Through Data Stewardship, Modular Ontologies, and FAIR Data Workflows
FactoryNet: A Labeled Image Dataset for the Manufacturing Environment
FIB-SEM Serial Sectioning Tomography: Towards 24-Hour Time-to-Results
Generalized Graph Foundation Models as Versatile Data-Driven Digital Twins for Complex Technological Systems
Harnessing Deep Learning Conditional Diffusion Models for Microscopy Modality Transfer of Light Optical Microscopy to Electron Backscattering Microscopy Diffraction Misorientations
Influence of 3D Crack Networks for High Toughness Responses in Tantalum Carbides
Innovations in 3D EBSD for Advanced Materials Characterization
Manufacturing and Control of Fiber Reinforced Polymer Composites Through FMEA-Based Digital Twin
Materials Microstructure Design Integrated With Image-Based Simulation
Modular and Interoperable Materials Data Science Ontology (MDS-Onto) for Knowledge Graphs and Semantic Reasoning
NIMS's Data-Driven Materials Research Platform: Enhancing MLOps With Literature-Based Data Integration
Pinax: A Machine Learning Platform for Data-Driven Materials Development
Smart Sustainable Packaging for Local Fruits—TRACE Your Food, KNOW Your Food, TAKE CARE of Trash
The Materials Science and Engineering Knowledge Graph: Establishing a Centralized Metadata Index for Enhanced Data Integration
Toward Sentient Manufacturing
Towards Structured Data Spaces: Prototypical Application of Semantic Technologies as a Driver for Innovation in Materials Science
Transforming Materials Science With Concepts for a Semantically Accessible Data Space
Uncertainty Quantification, Error Propagation, and Sensitivity Analysis for Synchrotron X-Ray Residual Stress Measurements
Using Novel EBSD Methods to Analyze Plastic Strain in Structural Alloys
X-Ray Diffraction Analysis Using TensorFlow and FAIR Data Pipelines

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