A research team at the National Institute for Materials Science (NIMS) has developed an innovative automated high-throughput system that significantly accelerates the generation of structural materials databases. This groundbreaking technology is capable of producing extensive datasets from a single sample of a superalloy used in aircraft engines. In a remarkable achievement, the system generated an experimental dataset containing several thousand records in just 13 days, a process that would take traditional methods approximately seven years and three months.
The automated system focuses on creating what are known as Process–Structure–Property datasets, which encompass a wide range of interconnected factors, including processing conditions, microstructural features, and resulting yield strengths. These datasets are crucial for understanding material mechanisms and advancing innovations in materials design.
Accelerating Materials Innovation
Producing high-precision experimental data has been essential for researchers aiming to formulate theories, construct models, and perform numerical simulations. Traditionally, developing comprehensive databases has posed significant challenges, requiring extensive time and resource investments. The new system from NIMS promises to alleviate these issues, particularly in the optimization of heat-resistant superalloys, which are critical for aerospace applications.
The NIMS team focused on a Ni-Co-based superalloy, specifically created for use in aircraft engine turbine disks. Utilizing a gradient temperature furnace, they thermally treated the superalloy sample to map a variety of processing temperatures. Measurements of precipitate parameters and yield stress were conducted at various coordinates along the temperature gradient, employing a scanning electron microscope and a nanoindenter, both controlled by a Python API.
The rapid evaluation of the gathered data has positioned this automated system at the forefront of materials research. The sheer volume of data generated in just 13 days demonstrates a drastic improvement over conventional methods, which typically rely on manual processes.
Future Applications and Goals
Looking ahead, the NIMS research team plans to leverage this automated system to develop databases for additional superalloys. They aim to explore new technologies for measuring high-temperature yield stress and creep data, fundamental aspects of materials performance under extreme conditions. Additionally, the team intends to establish multi-component phase diagrams based on the constructed superalloy databases, which are essential for the design of advanced materials.
As part of their broader mission, the researchers aspire to create new heat-resistant superalloys that could play a role in achieving carbon neutrality. This ambitious goal reflects the growing importance of sustainable practices in materials science and engineering.
The findings of this research have been published in the journal Materials & Design, providing a significant contribution to the field and paving the way for future advancements in materials innovation. For further details, refer to the work of Thomas Hoefler et al, titled “Automated system for high-throughput process-structure-property dataset generation of structural materials: A γ/γ′ superalloy case study” (DOI: 10.1016/j.matdes.2025.114279).
