A team from the National Institute for Materials Science (NIMS) has unveiled an innovative automated high-throughput system designed to generate extensive datasets from superalloy samples used in aircraft engines. In an impressive feat, the system produced thousands of records, capturing essential processing conditions, microstructural features, and yield strengths, all within a rapid timeframe of just 13 days.
This new approach marks a significant advancement, as it generates datasets over 200 times faster than traditional methods. The ability to quickly compile large-scale, comprehensive datasets has the potential to revolutionize data-driven materials design, a critical aspect of modern engineering.
Transforming Materials Research
High-precision experimental data is vital for understanding material mechanisms and enhancing innovation in the field of materials science. Such datasets are crucial for optimizing heat-resistant superalloy processing methods and addressing the complexities of multi-element microstructures. Historically, the creation of these databases has been a lengthy process, often requiring years of continuous experimental work and substantial investments in resources.
The NIMS research team focused on a Ni-Co-based superalloy specifically developed for use in aircraft engine turbine disks. Their automated evaluation system is capable of generating Process–Structure–Property datasets that include vital information such as heat treatment temperatures, precipitate parameters, and mechanical properties like yield stress.
The system utilizes a gradient temperature furnace to thermally treat the superalloy sample, allowing for a comprehensive mapping of processing temperatures. Measurements of precipitate and yield stress were conducted using a scanning electron microscope, which was automatically controlled via a Python API, along with a nanoindenter. This synergy of technologies enabled the team to collect and analyze data at multiple coordinates along the temperature gradient efficiently.
The result of this automated approach is astounding: the system produced a volume of data in just under two weeks that would have taken conventional methods approximately seven years and three months to generate.
Future Applications and Goals
Looking ahead, the NIMS team plans to utilize this automated system for constructing databases that encompass various superalloys. They also aim to develop new technologies to acquire high-temperature yield stress and creep data, which are essential for advancing materials design.
Moreover, the researchers intend to formulate multi-component phase diagrams based on the constructed superalloy databases. This will facilitate the exploration of new superalloys with desirable properties through data-driven techniques. The ultimate objective is to create advanced heat-resistant superalloys that could contribute to achieving carbon neutrality.
This groundbreaking research was published on November 11, 2025, in the journal Materials & Design, highlighting the potential impact of these developments on the fields of materials science and engineering. As the demand for innovative materials continues to grow, the automated high-throughput system represents a crucial step towards faster and more efficient materials discovery.
