Developing stronger and tougher structural metals such as steels requires exploring an enormous range of processing conditions. Even for the same alloy composition, heat treatment can dramatically change the material's microstructure and mechanical properties. If there was a way to automate the process, we could pinpoint ideal conditions much more efficiently. However, experiments on bulk metallic materials are difficult to automate because they require handling solid specimens and integrating large, specialized equipment.
A research team led by Yulin Xie, Sayaka Sekida, and Goro Miyamoto at the Institute for Materials Research (IMR), Tohoku University, and Tadashi Furuhara (formerly at IMR) at the National Institute for Materials Science (NIMS), worked together with TsubameLab to develop an automated experimental system that integrates heat treatment, structural characterization, and mechanical testing of bulk metallic materials. This enables a continuous workflow from heat treatment to structural analysis and mechanical property evaluation without human intervention.
Automating experiments on bulk structural metals has been particularly challenging compared with liquids, powders, and thin films because solid specimens must be reliably transported, positioned, heated, characterized, and mechanically tested. The researchers overcame these challenges by designing dedicated specimen-handling tools and integrating the individual instruments under unified computer control.
The system combines an induction heating furnace, an X-ray diffractometer (XRD), and a micro-impact testing machine using a robotic arm to transfer specimens automatically between each process. Using the newly developed platform, the team demonstrated automated experiments on one- and two-step tempering of a commercial JIS SCM435H low-alloy steel. Excluding the heat-treatment time itself, the complete sequence of specimen handling, XRD measurement, mechanical testing, and specimen recovery can be completed in approximately 800 seconds per sample.
The automated system evaluates mechanical properties with accuracy comparable to conventional measurements manually performed by researchers, while conducting the experimental sequence more than ten times faster. It is an accurate, fast, and convenient all-in-one system that runs on its own.
Importantly, the system simultaneously generates processing, structure, and property data. Such process-structure-property datasets are essential for applying artificial intelligence to materials development. The researchers also implemented an AI optimization tool within the NIMS Orchestration System (NIMO), demonstrating automated optimization of furnace control parameters and establishing the technological basis for future closed-loop materials exploration.
In a future autonomous system, AI will analyze the results obtained from each experiment and propose the next heat-treatment conditions to investigate. The experimental platform will then automatically perform the proposed treatment, characterize the resulting structure and properties, and return the new data to the AI. Repeating this closed-loop cycle could allow promising processing conditions to be identified efficiently from an enormous number of possibilities.
"This work demonstrates that the entire experimental workflow can be automated, even for tricky bulk structural metallic materials," said Miyamoto. "By combining this platform with AI that proposes the next experimental conditions, we aim to accelerate the autonomous discovery of innovative processing routes and structural materials."
The development represents an important step toward autonomous laboratories for structural metals. By enabling efficient generation of process-structure-property data, the system could substantially expand the range of heat-treatment conditions that researchers can explore and accelerate the development of advanced structural metallic materials.
The study was published online in Science and Technology of Advanced Materials: Methods on September 18, 2026.