Combined approach to capture the evolution of oxidation of Nickel based superalloys using data driven approaches

Khatavkar, N and Singh, AK (2024) Combined approach to capture the evolution of oxidation of Nickel based superalloys using data driven approaches. In: Physical Review Materials, 8 (5).

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Abstract

Nickel-based superalloys are an exceptional class of materials that are indispensable for high-temperature applications in the aerospace and power sector industries worldwide. The prolonged application of these materials in a demanding environment is hindered by the increased oxidation rates and deformation due to mass gain at high temperatures and the presence of corrosive agents. Calculating the oxidation properties using experimental techniques is laborious and highly cost/time intensive, which presents a considerable challenge to reducing the oxidation in these materials. In this work, we establish an extensive database consisting of the specific mass gain due to oxidation (�m) and the parabolic oxidation rates (kp) of nickel-based superalloys spanning all the superalloy generations. Highly accurate machine learning (ML) models are developed to predict (�m) using artificial neural networks and tree-based XGBoost. The ML models are extended by unsupervised k means clustering to improve the accuracy of the models and generate insights on the composition-property linkages. Additionally, the ML model for kp developed utilizing XGBoost yields unprecedented results with errors of 0.04. The ML model is analyzed using the SHapely Additive exPlanations parameters to determine the effect of individual features on the model. Further, we employ a genetic algorithm-based approach utilizing the developed ML models to minimize the kp to improve the performance of the superalloys at high temperatures. The genetic algorithm-assisted optimization successfully yields several compositions for new Ni superalloys with up to 20 reduction in the kp. This work presents essential advances for accelerating the targeted discovery of new materials for highly specialized and demanding applications. © 2024 American Physical Society.

Item Type:	Journal Article
Publication:	Physical Review Materials
Publisher:	American Physical Society
Additional Information:	The copyright for this article belongs to American Physical Society.
Keywords:	High temperature applications; K-means clustering; Neural networks; Nickel; Nickel alloys; Oxidation; Superalloys, Aerospace sectors; Data-driven approach; Exceptional class; High-temperature application; Highest temperature; Machine learning models; Mass gain; Nickel-based superalloys; Oxidation rates; Power sector, Genetic algorithms
Department/Centre:	Division of Chemical Sciences > Materials Research Centre
Date Deposited:	03 Jun 2024 10:28
Last Modified:	03 Jun 2024 10:28
URI:	https://eprints.iisc.ac.in/id/eprint/85113

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