High throughput process-materials framework for repairing Ni-based superalloys

Laser-based Directed Energy Deposition (DED) presents numerous opportunities for the development, repair, and end-use manufacturing of advanced materials. However, significant challenges remain in processing high γ′-strengthened nickel-based superalloys, primarily due to limited understanding of their constitutive behavior and the variability introduced by localized thermal cycles. Exploring materials through a graded alloy approach, by systematically varying the content of crack-susceptible γ′ precursors such as Al and Ti, enables the synthesis of novel microstructures while expanding the compositional design space. However, the generation of large datasets to fully capture the process–structure–property (PSP) relationships remains constrained by the time and resource-intensive nature of conventional materials characterization. To address these limitations, this dissertation focuses on three key objectives: (1) Investigating the influence of DED process parameters and alloy composition on crack formation in high γ′ Ni-based superalloys; (2) Establishing high-throughput composition–process–structure–property (CPSP) correlations using mechanical testing and microstructure quantification; (3) Developing interpretable, data-driven models to enable DED process optimization and mitigate hot cracking. This work leverages advanced material characterization techniques such as small punch testing (SPT), automated SEM image analysis, and CALPHAD-based thermodynamic simulations to assess mechanical and microstructural properties efficiently across IN625–IN100 composition gradients. The resulting high-resolution dataset is used to train machine learning models that predict properties based on composition, heat treatment, and microstructure. Principal Component Analysis was then applied to in-situ thermal data to extract low-dimensional descriptors of thermal history, which were then used to classify crack susceptibility across different DED process conditions. Together, these efforts lay the groundwork for scalable, physics-informed frameworks that accelerate the qualification and repair of Ni-based superalloy components in additive manufacturing.

Date

2025-08-05

Resource Type

Text

Resource Subtype

Dissertation

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