Materials pp 109-115 | Cite as

Joining of Austenitic Stainless Steels for Cryogenic Applications

  • T. A. Siewert
  • C. N. McCowan
Chapter
Part of the Advances in Cryogenic Engineering book series (ACRE, volume 38)

Abstract

The welds that are used to fabricate a structure from wrought stainless steel subcomponents usually have poorer mechanical properties than the wrought material at cryogenic temperatures. This means that the critical fracture path in these structures could be through the welds. For many applications the welds may never be stressed to critical levels, but for very aggressive structural designs it can be a real concern. For these aggressive designs, the structural designer could place the welds in less critical regions, however, such a design philosophy might be difficult to implement. It would be better to learn how to make welds with improved properties.

We have developed quantitative data for many of the factors that influence the strength and toughness of welds, to allow more intelligent choices of welding processes and compositions for demanding applications. This paper reviews these factors and discusses the interactions between them. For example, the cryogenic strength is influenced most by the composition, with the strength being increased strongly by N addition. The toughness is decreased by residual delta ferrite (FN) and inclusions, but can be increased by addition of Ni. Recently, a gas metal arc weld with 25 wt.% Ni has produced the best combination of strength and toughness ever measured at 4 K in our laboratory. Changes in the inclusion fraction are the primary cause of differences in mechanical properties between welds produced by the various welding processes. A secondary cause of differences is a nonuniform distribution of elements in the microstructure.

Keywords

Fracture Toughness Austenitic Stainless Steel Electron Beam Welding Ferrite Content Stainless Steel Weld 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer Science+Business Media New York 1992

Authors and Affiliations

  • T. A. Siewert
    • 1
  • C. N. McCowan
    • 1
  1. 1.Materials Reliability DivisionNational Institute of Standards and TechnologyBoulderUSA

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