Dr. Rowenhorst is the head of the Metallurgical Design and Research Section at the US Naval Research Laboratory (NRL). He joined NRL in 2004 as NRC Postdoctoral Associate after he received his Ph.D. from Northwestern University where he studied 3D microstructural characterization methods to analyze particle coarsening in Pb-Sn systems in microgravity environments. He continued on at NRL as a staff scientist in the Phase Transformations and Joining Section where he was one of the first to integrate EBSD methods into 3D analysis of materials. His work continues to concentrate on advanced characterization and analysis of microstructures, especially within an Integrated Computational Materials Engineering (ICME) framework, including 3D characterization of grain growth in polycrystalline materials, phase transformations in high strength steels, the 3D microstructures associated with additive manufacturing, and new automation methods for serial sectioning and high-throughput microscopy.
Presentation Title:
Automating the 3D Characterization Pipeline for High-Throughput Microstructure Quantification
Presentation Abstract:
Bridging the gap between Integrated Computational Materials Engineering (ICME) and the realization of fully autonomous laboratories requires overcoming critical bottlenecks in materials characterization. For structural alloys, the precise quantification of microstructural features is essential to power robust predictive feedback loops. However, the materials science community currently lacks facilities capable of performing automated microstructure analysis at scale. To address this deficiency, the Naval Research Laboratory (NRL) has developed the Robotic Serial Sectioning System for 3D (RS3D). This high-throughput platform pioneers the physical automation necessary for continuous materials characterization. Furthermore, by bringing together unsupervised machine learning techniques and powerful open-source software tools, the RS3D system will provide an end-to-end microstructural data collection and quantitative analysis, which will accelerate the evaluation of structural alloys, providing the high-fidelity, high-volume data streams necessary to drive the next generation of autonomous materials discovery and ICME frameworks.