A NIMS research team has developed an automated high-throughput system capable of generating datasets from a single sample of a superalloy used in aircraft engines. The system successfully produced an experimental dataset containing several thousand records—each consisting of interconnected processing conditions, microstructural features and resulting yield strengths (referred to as “Process–Structure–Property datasets” below)—in just 13 days. Datasets are generated over 200 times faster than when using conventional methods. The system’s ability to rapidly produce large-scale, comprehensive datasets has the potential to significantly accelerate data-driven materials design. This research was published in Materials & Design , an international scientific journal, on June 20, 2025.
High-precision experimental data is essential for investigating material mechanisms, formulating theories, constructing models, performing numerical simulations and machine learning and driving materials innovation. In particular, large quantities of accurate Process – Structure – Property datasets are indispensable for optimizing heat-resistant superalloy processing methods and the complex, multi-element microstructures of these materials. However, developing such databases typically requires years of continuous experimental work and substantial resource investment. These challenges have long hindered the development of high-performance superalloys.
This NIMS research team recently developed a new, automated high-throughput evaluation system capable of generating Process–Structure–Property datasets containing thousands of data points from a single sample of a Ni-Co-based superalloy developed by NIMS for use in aircraft engine turbine disks. These datasets include processing conditions (heat treatment temperatures), microstructural information (e.g. precipitate parameters) and mechanical properties (e.g. yield stress). The superalloy sample was thermally treated using a gradient temperature furnace developed by the team, thus mapping a wide range processing temperatures across it. Precipitate and yield stress measurements were obtained at various coordinates along the temperature gradient using a scanning electron microscope automatically controlled using a Python API and a nanoindenter. The system then rapidly evaluated and processed the collected data. As a result, the system successfully generated a volume of Process–Structure–Property data that would have taken conventional methods approximately seven years and three months to produce in just 13 days.
The research team plans to apply this system to the construction of databases for various target superalloys and to the development of new technologies for acquiring high-temperature yield stress and creep data. In addition, the team aims to formulate multi-component phase diagrams—essential for materials design—based on the constructed superalloy databases, and to explore new superalloys with desirable properties using data-driven techniques. The ultimate goal is to fabricate new heat-resistant superalloys that may contribute to achieving carbon neutrality.
Materials & Design
Experimental study
Not applicable
Automated System for High-throughput Process-Structure-Property Dataset Generation of Structural Materials: A γ/γ′ Superalloy Case Study
20-Jun-2025