Machine learning-driven new paradigm for Co-based superalloys

Abstract

Co-based superalloys exhibit exceptional high-temperature properties, granting them broad application prospects in the superalloy domain. However, constrained by the exorbitant trial-and-error costs and protracted research cycles inherent in their development, machine learning (ML) has emerged as the most pivotal research direction in this field. This review systematically examines ML-driven approaches for Co-based superalloys, progressing from fundamental regression models for property prediction to advanced multi-model, multi-scale computational paradigms-structured according to model sophistication and problem complexity. Furthermore, we discuss current challenges and future prospects in applying ML to Co-based superalloys, with particular emphasis on addressing data scarcity through the integration of high-throughput experimentation. This synergistic approach enables efficient establishment of standardized superalloy databases, accelerating research progress to meet evolving demands in aerospace applications.

Keywords

Co-based superalloys / machine learning / high-throughput experimentation / alloy design

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