4.7 Article

Quantitative electron microscopy and physically based modelling of Cu precipitation in precipitation-hardening martensitic stainless steel 15-5 PH

期刊

MATERIALS & DESIGN
卷 143, 期 -, 页码 141-149

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.matdes.2018.01.049

关键词

Precipitation; Materials modelling; Precipitation-hardening stainless steel; Transmission electron microscopy; Tempering of martensite

资金

  1. VINNOVA
  2. Swedish Governmental Agency for Innovation Systems
  3. Swedish industry
  4. KTH Royal Institute of Technology
  5. China Scholarship Council (CSC) [201506460045]

向作者/读者索取更多资源

Precipitation-hardening martensitic stainless steels rely on very fine precipitates for optimal mechanical performance. These multicomponent alloys are prone to clustering and precipitation reactions during tempering, where Cu is one of the alloying elements added to stimulate precipitation. It is efficient to use an integrated computational materials engineering (ICME) approach to tailor alloying and heat treatment for design of these alloys. The most promising physically based modelling of precipitation for this purpose at present is Langer-Schwartz-Kampmann-Wagner (LSKW) modelling within the CALPHAD framework. This approach has been successful for model alloys, but reliable results for mulhcomponent stainless steels are less common. Hence, we combine quantitative transmission electron microscopy and LSKW modelling to investigate the tempering of a martensitic stainless steel 15-5 PH at 500 degrees C. The microstructural characterization shows that the Cu precipitation and growth occur in three stages: i) Cu BCC, n) Cu 9R, and iii) Cu FCC, during tempering up to 1000 h. The modelling predictions of size, volume fraction and number density of precipitates are in good agreement with the experimental results. Thus, the approach with a combination of quantitative electron microscopy and LSKW modelling using CALPHAD-type databases holds promise for further optimization of precipitation-hardening martensitic stainless steels. (C) 2018 Elsevier Ltd. All rights reserved.

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