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

机译:沉淀硬化马氏体不锈钢15-5 PH中的铜沉淀的定量电子显微镜和基于物理的建模

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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.
机译:沉淀硬化的马氏体不锈钢依靠非常细的沉淀物来获得最佳的机械性能。这些多组分合金在回火过程中容易发生团聚和沉淀反应,其中Cu是添加以刺激沉淀的合金元素之一。使用集成计算材料工程(ICME)方法来定制合金和热处理以设计这些合金是有效的。目前,最有希望的基于物理的降水建模是CALPHAD框架内的Langer-Schwartz-Kampmann-Wagner(LSKW)建模。这种方法已成功用于模型合金,但对多组分不锈钢的可靠结果却很少见。因此,我们结合定量透射电子显微镜和LSKW模型研究了马氏体不锈钢15-5 PH在500摄氏度下的回火。显微组织表征表明,铜的析出和生长发生在三个阶段:i)Cu BCC,n )Cu 9R和iii)Cu FCC,回火时间长达1000 h。沉淀物的大小,体积分数和数量密度的模型预测与实验结果吻合良好。因此,结合使用定量电子显微镜和使用CALPHAD型数据库的LSKW建模的方法,有望进一步优化沉淀硬化型马氏体不锈钢。 (C)2018 Elsevier Ltd.保留所有权利。

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