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Meeting TMS Specialty Congress 2025
Symposium Joint Sessions of AIM, ICME, & 3DMS
Presentation Title Transforming Materials Science With Concepts for a Semantically Accessible Data Space
Author(s) Bernd Bayerlein, Markus Schilling, Henk Birkholz, Philipp von Hartrott, Jörg Waitelonis, Alden A. Dima, June W. Lau
On-Site Speaker (Planned) Bernd Bayerlein
Abstract Scope The digital transformation in materials science enables more efficient and sustainable processes. Through technological adaptations and a commitment to the FAIR principles, materials and processes are holistically addressed across entire value chains. The Platform MaterialDigital (PMD) and related initiatives are developing innovative solutions to the challenges of digitalization. The focus is on the interoperable integration of heterogeneous materials and processes data in semantically accessible data spaces. An ontological framework, based on the PMD Core Ontology and application-specific ontologies, promotes semantic interoperability of cross-domain and multi-scale (meta)data. This framework can be extended through natural language processing in a script-supported manner, as demonstrated with the Microscopy Ontology. The presentation further highlights how freely available mechanical and microstructural datasets of various aging stages of an aluminum alloy can be semantically integrated and flexibly searched. Graph-based operations enable links between processing and microstructural properties to be established, facilitating enhanced correlation analysis and pattern recognition.
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
Ontology-Based Materials Data Management for High Temperature Alloy Oxidation Data
Pinax: A Machine Learning Platform for Data-Driven Materials Development
Practical Data Management in Computational Materials for Qualification and Certification
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
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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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