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The Cumulative Fatigue Damage Behavior of MAR-M 247 in Air and High-Pressure Hydrogen

材料科学 环境科学
作者
MA McGaw,Sreeramesh Kalluri,Dwight G. Moore,Jens Heine
标识
DOI:10.1520/stp15081s
摘要

The cumulative fatigue damage behavior of the cast nickel-base superalloy, MAR-M 247 (in a fine grain form), was characterized experimentally at room temperature in ambient air and in high-pressure gaseous hydrogen. The material was first characterized in simple fatigue, consisting of fully reversed strain- and load-controlled axial fatigue tests. These data were used to establish the reference life behavior for use in the cumulative fatigue experiments. The principal tool used to study the cumulative damage behavior was the two-level loading test (single-block) wherein low-cycle fatigue loading is applied initially to the specimen for various low-cycle fatigue life fractions, and the specimen is subsequently cycled under high-cycle fatigue conditions to failure. MAR-M 247 was found to exhibit a strongly nonlinear cumulative interaction behavior, with the remaining high-cycle fatigue life capability in the second load level reduced by as much as a factor of 30 over results predicted from a linear damage rule. The Damage Curve Approach of Manson and Halford was used to predict this nonlinear cumulative behavior. Similar experiments were performed at room temperature in a high-pressure gaseous hydrogen environment to assess the cumulative damage behavior and applicability of the Damage Curve Approach under differing environmental conditions. To study the applicability to more general loading cases, multi-block loading experiments were performed in air, in which each block consisted of both low-cycle and high-cycle fatigue cycling in prescribed amounts, ranging from one low-cycle fatigue cycle plus a fixed number of high-cycle fatigue cycles, to many low-cycle fatigue cycles plus a fixed number of high-cycle fatigue cycles. This basic loading block was then repeated until failure of the specimen. The results of these experiments and the predictions using the Damage Curve Approach are presented and discussed.
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