论文部分内容阅读
为了探明超细晶钢中的所有晶粒形态、粒径及其分布对钢力学性能的影响规律,必需对其进行精确测量和分类。针对目前此项工作人工模式的精度及效率偏低等问题,依据超细晶钢晶粒形态特点,作者提出了能够对其进行有效分类的新的特征参数,通过对晶内极小角、圆形度、形态系数等特征参数的定量分析,实现了对全形态晶粒的自动测量、数值化表征与精细分类。结果表明,该方法对晶内孔洞缺陷具有很好的填充效果,可准确识别出类圆、类多边形、条状、粗针状及尖针状晶粒,为超细晶钢微观组织定量分析提供依据。
In order to find out the influence law of all grain shape, particle size and its distribution on the mechanical properties of ultrafine steel, it is necessary to accurately measure and classify it. Aiming at the problems such as the low accuracy and low efficiency of artificial working mode, the author proposed the new characteristic parameters which can be effectively classified according to the grain shape characteristics of ultra-fine grain. Shape, shape coefficient and other characteristic parameters of the quantitative analysis of the realization of the whole shape of the automatic measurement of grains, numerical characterization and fine classification. The results show that this method has a good filling effect on intracrystalline pore defects, and can accurately identify the quasi-circular, polygonal, strip-like, needle-like and needle-like grains for the quantitative analysis of ultrafine-grained microstructure in accordance with.