A research team at the National Institute for Materials Science (NIMS) has developed an innovative automated high-throughput system that produces extensive datasets from a single sample of a superalloy used in aircraft engines. This groundbreaking system generated a comprehensive dataset containing thousands of records in just 13 days, a process that typically takes over seven years with conventional methods.
The automated system focuses on creating what are known as Process–Structure–Property datasets. These datasets include critical information about processing conditions, microstructural features, and mechanical properties, specifically yield strengths. The rapid generation of such data is expected to significantly enhance data-driven materials design, facilitating advancements in high-performance superalloys.
Accelerating Materials Research
High-precision experimental data is vital for understanding material mechanisms and developing theoretical models. With accurate Process–Structure–Property datasets, researchers can optimize the processing methods of heat-resistant superalloys, which are crucial for applications in extreme environments like aircraft engines. Traditionally, constructing these databases requires extensive time and resources, often hindering the innovation of new materials.
The NIMS team’s automated evaluation system can produce thousands of data points from a single sample of a Ni-Co-based superalloy specifically designed for turbine disks. This process involves using a gradient temperature furnace to thermally treat the superalloy, allowing for a wide range of processing temperatures to be mapped. Measurements of precipitate parameters and yield stress were collected using a scanning electron microscope and a nanoindenter, both operated through a Python API.
In just 13 days, this system successfully generated a volume of Process–Structure–Property data that would have taken traditional methods approximately seven years and three months to produce.
Future Applications and Goals
The research team intends to apply this automated system to develop databases for various superalloys and innovate technologies that measure high-temperature yield stress and creep data. Furthermore, they plan to create multi-component phase diagrams, which are essential tools for materials design.
Using the constructed databases, the team aims to explore new superalloys with enhanced properties through data-driven techniques. The ultimate objective is to manufacture new heat-resistant superalloys that could contribute to the goal of achieving carbon neutrality.
This research was published in the journal Materials & Design on November 11, 2025, and highlights the transformative potential of automation in materials science. The findings underscore the importance of rapid data generation in fostering innovation and advancing the development of high-performance materials necessary for modern engineering challenges.
For more information, refer to the study by Thomas Hoefler et al., titled “Automated system for high-throughput process-structure-property dataset generation of structural materials: A γ/γ′ superalloy case study,” available in Materials & Design.
