About this Abstract |
| Meeting |
2027 TMS Annual Meeting & Exhibition
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| Symposium
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AI-Enabled Materials Processing: Integrating Accelerated Experimental Workflows and Processing-Aware Machine Learning
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| Presentation Title |
AlloyBot Automated SEM–XRD Pipeline for High-Throughput Microstructure Characterization |
| Author(s) |
Ming En Pek, Tianhong Tan, Vignesh Selvaraj, Anagh Dutta, Joel Paulson, Sebastian A. Kube |
| On-Site Speaker (Planned) |
Ming En Pek |
| Abstract Scope |
Accelerated alloy development depends not only on rapid synthesis but also on characterization throughput that can keep pace. Conventional microstructure analysis remains largely manual, creating a bottleneck for data-driven design in self-driving laboratories. Following our AlloyBot automatic arc-melting synthesis, as next building block we here present a pipeline automating acquisition and analysis through X-ray diffraction (XRD) integrated with scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS). An efficient human-in-the-loop learning framework is used, in which expert oversight initially guides microstructure interpretation and is reduced as confidence in the system is established. Further, the workflow couples diffraction-based phase information with spatially resolved compositional and microstructural data, such that each modality refines the interpretation of the other. This yields consistent, machine-readable outputs suited for model training and rapid processing-structure-property assessment. We discuss the architecture of the pipeline and its role as a characterization backbone for increasingly autonomous experimental workflows. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Characterization, |