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Research Papers

A Microstructure-Based Model for Creep of Gamma Prime Strengthened Nickel-Based Superalloys

[+] Author and Article Information
Ramkumar Oruganti, Adarsh Shukla, Sachin Nalawade, Sanket Sarkar, K. G. V. Sivakumar, T. Vishwanath, Sanjay Sondhi

GE Global Research,
Bangalore 560066, India

Andrew Wessman, Daniel Wei, Andrew Powell, Kenneth Bain

GE-Aviation,
Cincinnati, OH 45069

Jon Schaeffer, Arthur Peck, Michael Arnett

GE-Power,
Greenville, SC 29615

Girish Shastry

GE-Power,
Bangalore 560066, India

Francesco Mastromatteo

BHGE,
Florence 50127, Italy

Contributed by the Materials Division of ASME for publication in the JOURNAL OF ENGINEERING MATERIALS AND TECHNOLOGY. Manuscript received September 12, 2017; final manuscript received May 22, 2018; published online July 5, 2018. Assoc. Editor: Antonios Kontsos.

J. Eng. Mater. Technol 141(1), 011001 (Jul 05, 2018) (5 pages) Paper No: MATS-17-1264; doi: 10.1115/1.4040554 History: Received September 12, 2017; Revised May 22, 2018

This paper outlines a microstructure-based model relating gamma prime microstructure and grain size of Ni-base alloys to their creep behavior. The ability of the model to explain creep of multiple superalloys with a single equation and parameter set is demonstrated. The only parameters that are changed from alloy to alloy are related to the gamma prime characteristics and grain size. This model also allows prediction of creep performance as a function of heat treatment and explains some apparently contradictory data from the literature.

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References

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Figures

Grahic Jump Location
Fig. 1

(a) Experimental and predicted strain versus time for DS-GTD111 a directionally solidified Ni-base superalloy with ∼55% γ′ volume fraction. (b) Comparison of predictions from the Dyson model and the current model with experimental data on DS-GTD111.

Grahic Jump Location
Fig. 2

Predicted time-to strain versus experimental time-to-strain for four superalloys with different compositions and γ′ volume fractions. The data points include strains from 0.2% to 5%. The dashed lines indicate ±10% bounds. One model with microstructural inputs and no other change in parameters is able to predict the behavior of all the alloys (a) Rene' 65, (b) DS-GTD111, (c) GTD444, and (d) Rene' N5.

Grahic Jump Location
Fig. 3

Experimental data and predictions for DS-GTD111 with two different initial γ′ distributions. Both data converge to a common trajectory after a certain amount of time. The point of convergence shifts to lower times as temperature increases.

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