Abstract

Selection of materials to be used for components experiencing extreme conditions is a critical process in the design phase. Nickel-base superalloys have been frequently used for hot gas path components in the turbomachinery industry. These components are required to withstand both fatigue and creep at extreme temperatures during their service time. In general, the extreme temperature materials mostly embody polycrystalline, directionally solidified, and single crystal superalloys. Single crystallization has been utilized with nickel-base superalloys since 1980s. This method forms one grain by eliminating all of the grain boundaries, which has resulted with thermal, fatigue and creep properties superior to conventional alloys. It is essential for design engineers to predict accurate damage behavior and lifespan for these components to prevent catastrophic failures. This study presents generic elastic and stress-life models for single crystal nickel-base superalloys based on observed trends. Despite the development of over 50 variations of single crystal Nickel-base superalloys, the behavior of these alloys nominally follows similar mechanical behavior trends with respect to temperature and orientation. Temperature-, rate-, and orientation-dependence of these materials are studied. In this study, [001], [011] and [111] orientations are mainly considered. The goal is to eliminate extensive time and cost of experiments by creating parameters to be used in life calculations for generic single crystal alloys. While the stress-based approach to fatigue analysis of materials was the first to be developed, it continues to endure with broad usage in a wide variety of engineering applications. These models tend to be used for the cases with high number of cycles to failure behavior or called high-cycle fatigue (HCF) conditions. In this work, the total damage is divided into two different modules; fatigue and creep damages. Miner’s Rule is utilized to combine these modules. Models which can predict the cycles to failure data with the most usage-like conditions and require least amount of data are preferred. Parameters for the models are built on regression fits in comparison with a comprehensive material database. This database includes elastic, plastic, creep, and fatigue properties.

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