金属热处理 ›› 2021, Vol. 46 ›› Issue (5): 25-31.DOI: 10.13251/j.issn.0254-6051.2021.05.004

• 组织与性能 • 上一篇    下一篇

42CrMo钢加热过程中的奥氏体晶粒尺寸演变

彭则1, 李萌蘖1, 卜恒勇1, 李其2   

  1. 1.昆明理工大学 材料科学与工程学院, 云南 昆明 650093;
    2.二重重型装备有限公司, 四川 德阳 618000
  • 收稿日期:2020-10-15 出版日期:2021-05-25 发布日期:2021-07-21
  • 通讯作者: 李萌蘖,教授,博士生导师,E-mail:limengnie@163.com
  • 作者简介:彭 则(1996—),男,硕士研究生,主要研究方向为传统钢热处理过程的数值模拟,E-mail:15917899173@163.com。
  • 基金资助:
    科技部重点研发计划(2017YFB0701804)

Evolution of austenite grain size of 42CrMo steel during heating

Peng Ze1, Li Mengnie1, Bu Hengyong1, Li Qi2   

  1. 1. Faculty of Materials Science and Engineering, Kunming University of Science and Technology, Kunming Yunnan 650093, China;
    2. Erzhong Heavy Equipment Co., Ltd., Deyang Sichuan 618000, China
  • Received:2020-10-15 Online:2021-05-25 Published:2021-07-21

摘要: 通过金相试验方法测定42CrMo钢在890~930 ℃下保温10~240 min后的晶粒尺寸。结果表明,42CrMo钢在加热到试验温度890~930 ℃时已经完全奥氏体化,保温过程中的晶粒生长属于正常生长;加热温度对晶粒尺寸的影响较大,保温时间对晶粒尺寸的影响较小;随保温时间的延长晶粒生长缓慢,晶粒尺寸与保温时间满足指数小于1的函数关系。基于试验数据,通过线性回归得到晶粒长大的Beck模型参数,通过非线性回归得到Sellars和Anelli模型参数,3个模型的预测精度都较好,而Anelli模型的适用性要高于Beck模型和Sellars模型,故在预测42CrMo钢的奥氏体晶粒长大规律时宜使用Anelli模型。

关键词: 42CrMo钢, 晶粒长大, 晶粒长大模型, 非线性回归

Abstract: Austenite grain size of the 42CrMo steel heated at 890-930 ℃ for 10-240 min was measured through optical metallography exams. The results reveal that the 42CrMo steel is fully austenitized when heated between 890-930 ℃ and the grain growth is normal during the heating process. Temperature has an important effect on grain size, but the effect of holding time is minor. Grain growth is slow as holding time prolongs, and grain size and holding time satisfy a function whose exponent is less than 1. Experimental data are fitted to determine parameters of the Beck model, Sellars model and Anelli model. The prediction accuracy of all three models is better. Anelli model is considered being the most suitable, thus is recommended for the prediction of austenite grain growth law of the 42CrMo steel.

Key words: 42CrMo steel, grain growth, grain growth model, nonlinear regression

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