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High-Throughput Data Generation and Transfer Learning Enabled Microstructure-Property Integrated Design of

Zixin Li1,2,3,4, Hongtao Zhang1,2,3,4, Zichao Peng5

  • 1Beijing Advanced Innovation Center for Materials Genome Engineering, School of Advanced Materials Innovation, University of Science and Technology Beijing, Beijing, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
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Summary

We developed a data-driven framework to predict superalloy performance for aero-engines. This approach identified a new low-density alloy, USTB-PM750, with superior high-temperature strength and stability.

Keywords:
CALPHADalloy designdiffusion‐multiplenickel‐based powder metallurgy superalloystransfer learning

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Area of Science:

  • Materials Science
  • Metallurgy
  • Computational Materials Science

Background:

  • Nickel-based powder metallurgy (PM) superalloys are critical for aero-engine turbine disks.
  • Designing these alloys is challenging due to complex interactions and difficulty predicting long-term performance.

Purpose of the Study:

  • To develop a data-driven framework for predicting microstructural stability and mechanical properties of superalloys.
  • To identify novel, high-performance superalloy compositions for advanced aero-engines.

Main Methods:

  • Integration of high-throughput thermodynamic calculations, diffusion-multiple experiments, and transfer learning.
  • Calibration of computational models using sparse experimental data.
  • Screening of over 10^5 alloy compositions.

Main Results:

  • Accurate prediction of microstructural features and mechanical properties.
  • Identification of a promising low-density alloy, USTB-PM750 (8.33 g/cm³).
  • USTB-PM750 exhibits high yield strength (1138 MPa) and creep life (141 h at 750°C/480 MPa).
  • Superior performance attributed to low stacking-fault energy, promoting stacking faults, microtwins, and Lomer-Cottrell locks.

Conclusions:

  • The data-driven framework significantly improves superalloy design efficiency.
  • USTB-PM750 is a promising candidate material for advanced aero-engine turbine disks.
  • The study demonstrates a powerful approach for accelerated materials discovery.