Physics‐Informed Generative Machine Learning for Designing Crack‐Free <i>γ′</i> ‐Strengthened Ni‐Based Superalloys for Laser Powder Bed Fusion

ABSTRACT Fabricating γ′ ‐strengthened nickel‐based superalloys via laser powder bed fusion (LPBF) faces significant obstacles due to their severe susceptibility to cracking, which strictly limits their industrial application. To address this, a physics‐informed machine learning (ML) framework based on a generative design concept and high‐throughput thermodynamic calculations is proposed. A thermodynamic database containing 210,000 samples was constructed, and a conditional variational autoencoder (CVAE) was trained and subsequently used to generate potential target compositions. Guided by critical physical metallurgy features, including γ′ phase content, carbide characteristics, and cracking indices, a novel superalloy entitled SHA800, tailored for service temperatures of 800°C–900°C, was designed and experimentally validated. Experimental results demonstrated a wide crack‐free processing window. Additionally, the alloy attained a 43% γ′ volume fraction after heat treatment, achieving an exceptionally high hardness of 587 HV0.2. This integrated approach, leveraging large‐scale thermodynamic data and ML models, significantly accelerates the alloy design process for laser additive manufacturing and has important implications for the development of superalloys.