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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Automating LabDCT Workflows via Intelligent, Data-Driven Guidance
Author(s) Mario Heinig, Florian Bachmann, Raghav Vacher, Gorm Gruner Jensen, Jette Oddershede, Erik Lauridsen
On-Site Speaker (Planned) Mario Heinig
Abstract Scope Laboratory diffraction contrast tomography (LabDCT) provides powerful, non-destructive 3D grain mapping capabilities directly in the laboratory. To make the workflow more robust, user choices of experimental geometries, scanning schemes, and reconstruction parameters are replaced by intelligent suggestions, as we introduce an automation framework designed to streamline the entire data pipeline from acquisition to final reconstruction. By integrating machine learning algorithms, the system provides real-time parameter guidance during scan setup, optimizing geometries and experimental schemes for each specific sample. Furthermore, automated data pre-processing enables reliable reconstructions with minimal supervision. This data-driven framework enhances the existing LabDCT workflow to ensure higher consistency, faster time-to-results, and maximum grain map quality for both routine and complex microstructures. Ultimately, these advancements make 3D microstructural characterization more robust and user-friendly than ever before.
Proceedings Inclusion? Planned:
Keywords Characterization, Machine Learning, Other

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