摘要
随着金属材料常规力学性能研究的不断深化与完善,疲劳、蠕变等长时间服役性能越来越成为制约金属材料发展的瓶颈问题。钢铁材料是最重要的工程结构材料之一,为阐明其疲劳失效机理,关于钢铁材料显微组织与疲劳性能关系的研究更是领域内长久以来的热点和难点问题。随着钢铁冶炼技术的日新月异,对于疲劳性能的组织影响因素研究也逐步从夹杂物向亚稳奥氏体、析出物等特征组织因素转变。因此,为进一步分析疲劳性能的组织影响因素研究的可行方向,本文着重综述了先进钢铁材料中亚稳奥氏体组织对疲劳性能的影响规律,总结了相关学者针对低周疲劳、高周疲劳等不同服役条件提出的亚稳奥氏体对疲劳性能的影响机制,并进一步以已有实验结果为数据支撑,通过支持向量机、BP神经网络等机器学习算法对亚稳奥氏体组织特征与疲劳性能关系进行了定量化评估,初步形成了亚稳奥氏体含量/稳定性与疲劳寿命的定量关系,为钢铁材料疲劳性能的机理研究提供基础与方向性指导。
With the deepening and improvement of the research on the conventional mechanical properties of metallic materials, the long-term service properties, such as fatigue and creep, showed more and more critical influence on the development of metallic materials. As one of the most important engineering structural materials, in order to clarify the fatigue failure mechanism, the research of steels on the relationship between microstructure and fatigue properties has been a hot and difficult problem for a long time. With the rapid development of smelting technology for steels, the research on the influencing factors of fatigue gradually changes from inclusions to microstructures as metastable austenite, precipitates, etc. Therefore, in order to further analyze the feasible direction of the research on the influence of microstructure on fatigue, this paper summarizes the influence and mechanism of metastable austenite on the fatigue property of advanced steel materials. The influence mechanism of metastable austenite on fatigue property by relevant scholars under different service conditions such as low cycle fatigue and high cycle fatigue was reviewed. Based on the experimental results, the relationship between metastable austenite and fatigue properties was quantitatively evaluated by machine learning. The quantitative relationship between the content/stability of metastable austenite and fatigue life was established, which could provide the basis direction for the further study of the mechanism of fatigue for steels.
作者
徐伟
黄明浩
王金亮
沈春光
张天宇
王晨充
XU Wei;HUANG Minghao;WANG Jinliang;SHEN Chunguang;ZHANG Tianyu;WANG Chenchong(State Key Laboratory of Rolling and Automation,Northeastern University,Sheryang 1l10819,China)
出处
《金属学报》
SCIE
EI
CAS
CSCD
北大核心
2020年第4期459-475,共17页
Acta Metallurgica Sinica
基金
国家自然科学基金优秀青年基金项目No.51722101
国家重点研发计划项目No.2017YFB0703001
牛顿高级学者基金项目No.51961130389。
关键词
先进钢铁材料
亚稳奥氏体
疲劳性能
失效机制
数据挖掘
advanced steel
metastable austenite
fatigue property
failure mechanism
machine learning