4.7 Article

Correspondence between low-energy twin boundary density and thermal-plastic deformation parameters in nickel-based superalloy

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出版社

ELSEVIER
DOI: 10.1016/S1003-6326(21)65508-5

关键词

Nimonic 80A superalloy; twin boundary; microstructure evolution; dynamic recrystallization; grain size; stored energy

资金

  1. Chongqing Basic Research and Frontier Exploration Program, China [cstc2018jcyjAX0459]
  2. Fundamental Research Funds for the Central Universities, China [2019CDQYTM027, 2019CDJGFCL003, 2018-CDPTCG0001-6]
  3. Open Fund of State Key Laboratory of Materials Processing and Die & Mould Technology, China [P2020-001]

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This study developed an improved twin density model to investigate the relationship between low-energy twin boundary density (BLD Sigma 3n) and grain size, stored energy in thermal-plastic deformation process and solved it for Nimonic 80A superalloy based on EBSD statistical results. The results showed that BLD Sigma 3n increases with increasing stored energy and decreasing grain size, and higher BLD Sigma 3n with finer grains corresponds with lower temperatures and higher strain rates.
To deeply understand and even describe the evolutions of the low-energy twin boundary density (BLD Sigma 3n) in a thermal-plastic deformation process, an improved twin density model as a function of average grain size and stored energy is developed. For Nimonic 80A superalloy, the model is solved based on the EBSD statistical results of grain size and BLD Sigma 3n in the specimens compressed at temperatures of 1273-1423 K and strain rates of 0.001-10 s(-1). The corresponding relationships of BLD Sigma 3n with stored energy and grain size varying with temperature and strain rate are clarified by the superimposed contour plot maps. It is summarized that BLD Sigma 3n increases with increasing stored energy and decreasing grain size, and higher BLD Sigma 3n with finer grains corresponds with lower temperatures and higher strain rates. Such relationships are described by the improved twin density model, and the prediction tolerance of the solved model is limited in 2.8%.

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