Application of computational tools in alloy design
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Introduction Materials design is critical to achieving target performance, reliability, durability, and manufacturability of industrial components and products. Efficient and robust materials design and development methodologies have been sought for faster discovery, qualification, implementation, and industrialization of new materials. Under the framework of Integrated Computational Materials Engineering (ICME),1 there have been numerous efforts to develop computational capabilities and tools to design materials and predict material performance, as represented by the Materials Genome Initiative (MGI).2 Recent advancements in computational power and analytical capability have enabled the application of machine learning (ML) in materials design, as well as physics-based material models at various length scales. In this article, examples of successful applications of computational tools, both data-driven ML and physics-based models, in alloy design and optimization are described using single-crystal (SX) Ni-based superalloys as a model system. SX Ni-based superalloys are commonly used for hightemperature applications in aircraft engines and land-based power-generation gas turbines.3–5 Typical SX Ni-based superalloys contain multiple alloying elements, including Co, Cr, Mo, W, Re, Al, Ta, Ti, Hf, C, and B, and consist of a γ-Ni solidsolution matrix phase and γ′-Ni3Al strengthening precipitates with
cuboidal morphology. There are multiple property requirements, including mechanical, environmental, and physical properties, as well as processability, manufacturability, and cost (Table I) that need to be taken into consideration in alloy design. Traditionally, experimental candidate alloy compositions were selected based on empirical rules and simple regressions built for a few primary property requirements. Multiple iterations of alloy compositions were typically required to identify the final composition that meets the desired combination of properties. Ideally, alloy property models and a multi-objective optimization process would guide us to quickly reach the best solution, if there are property models capable of providing accurate predictions. However, many of the properties have complex interactions and dependencies on various factors, and it is difficult to predict the properties directly from alloy compositions. For example, sustained-peak low-cycle fatigue (SPLCF) resistance, which is one of the important properties for SX superalloys, includes elements of fatigue, creep, and environmental resistances.6,7 Creep resistance itself has been also known to be dependent on multiple compositional and microstructural factors, such as fraction and morphology of the strengthening γ′ precipitates, compositions of phases, lattice parameter misfit between the γ and γ′ phases, coarsening kinetics of the precipitates, and thermal stability of the phases
Akane Suzuki, GE Research, USA; [email protected] Chen Shen, GE Research, USA; [email protected] Natarajan Chennimalai Kumar, GE Research, USA; [email protected] doi:10.1557/mrs.2019.70
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