Enabling Large Superalloy Parts Using Compact Coprecipitation of γ′ and γ′′ View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-03

AUTHORS

Andrew J. Detor, Richard DiDomizio, Reza Sharghi-Moshtaghin, Ning Zhou, Rongpei Shi, Yunzhi Wang, Donald P. McAllister, Michael J. Mills

ABSTRACT

Next-generation gas turbines will require disk materials capable of operating at 923 K (650 °C) and above to achieve efficiencies well beyond today’s 62 pct benchmark. This temperature requirement marks a critical turning point in materials selection. Current turbine disk alloys, such as 706 and 718, are limited by the stability of their major strengthening phase, γ′′, which coarsens rapidly beyond 923 K (650 °C) resulting in significant degradation in properties. More capable γ′ strengthened superalloys, such as those used in jet engine disks, are also limited due to the sheer size of gas turbine hardware; the γ′ phase overages during the slow cooling rates inherent in processing thick-section parts. In the present work, we address this fundamental gap in available superalloy materials. Through careful control of Al, Ti, and Nb levels, we show that fine (<100 nm) γ′ and compact γ′/γ′′ coprecipitate structures can be formed even under extremely slow cooling rates from high temperature. The presence of Ti is shown to have a dominant effect on phase formation, dictating whether γ′, γ′/γ′′ coprecipitates, or other less desirable acicular phases form on cooling. Sensitivity to cooling rate and aging heat treatment is also explored. A custom phase field model along with commercial precipitation kinetics software is used to better understand the phase evolution and stability of compact coprecipitates. The alloying strategies discussed here enable a new class of superalloys suitable for applications requiring large parts operating at high temperature. More... »

PAGES

708-717

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11661-017-4356-7

DOI

http://dx.doi.org/10.1007/s11661-017-4356-7

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https://app.dimensions.ai/details/publication/pub.1092151939


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