材料科学
拓本
压痕硬度
微观结构
复合材料
摩擦学
模数
奥氏体
缩进
奥氏体不锈钢
可塑性
冶金
纳米压痕
变形(气象学)
腐蚀
作者
Daria Grabco,Olga Shikimaka,Constantin Pyrtsac,Daria Topal,Dragiša Vilotić,Marko Vilotić,Sergei Alexandrov
出处
期刊:Metals
[MDPI AG]
日期:2023-07-16
卷期号:13 (7): 1278-1278
摘要
This work is devoted to the study of the tribological properties of AISI 316L austenitic steel and the effect of the relative velocity of rubbing bodies on the microstructure and mechanical properties. The specificity of the deformation is investigated in the mode of dry friction “metal/metal”, namely, steel AISI 316L/steel St3sp, with a process duration of 15 h. The change in the microstructure of the samples as a result of friction and the determination of mechanical properties are carried out on the friction surface and on the cross-section of the samples. The mechanical parameters are studied by depth-sensitive indentation using a Berkovich indenter. It is shown that low friction with the relative velocity of rubbing bodies of about 30 rpm is capable of introducing noticeable microstructural and strength changes. Strength and relaxation properties (hardness, Young’s modulus, plasticity index, and resistance index) increase in samples subjected to friction compared to the original undeformed sample. A change in the microscopic structure of the samples near the friction surface increases such material properties as microhardness (H) and Young’s modulus (E). In particular, the microhardness increases from 1.72 GPa for the undeformed sample to 3.5 GPa for the sample subjected to friction for 15 h. Young’s modulus increases from 107 GPa to 140 GPa, respectively. A comparison with the properties of samples deformed at the relative velocity of rubbing bodies of about 300 rpm shows a further increase in the microhardness and Young’s modulus. Also noted is the sensitivity of the relaxation parameters to the friction process and the relative velocity of rubbing bodies. In particular, the relaxation parameters hc and hres decrease while he-p increases.
科研通智能强力驱动
Strongly Powered by AbleSci AI