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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI-Enabled Materials Processing: Integrating Accelerated Experimental Workflows and Processing-Aware Machine Learning
Presentation Title Development of an automated heat-treatment and evaluation system for autonomous exploration of process–structure–property relationships in structural metallic materials
Author(s) Goro Miyamoto, Yulin Xie, Sayaka Sekida, Tadashi Furuhara
On-Site Speaker (Planned) Goro Miyamoto
Abstract Scope Automated autonomous experimentation is increasingly needed to explore the vast processing space of materials, yet few systems have been demonstrated for solid bulk metals. In this study, we develop an automated evaluation system for the process–structure–property relationship of structural metallic materials toward autonomous exploration. By integrating a robot arm with heat-treatment, X-ray diffraction, and impact-testing modules, fully automated processing and characterization are achieved for bulk metallic specimens. The automated sequence, excluding heat treatment, is completed within 800 s per specimen. The capability of the system is demonstrated through high-throughput experiments on one-step and two-step tempering of JIS SCM435H steel. The reliability of the obtained property data is validated by manually measured hardness. Furthermore, diffraction peak width shows a positive correlation with strength, demonstrating automated extraction of structure–property relationships. Integration with the NIMS Orchestration System provides a foundation for closed-loop autonomous optimization.
Proceedings Inclusion? Planned:
Keywords Iron and Steel, Process Technology,

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