钢厂
无取向硅钢磁性能提升技术进步及其发展动向
无取向硅钢的磁性能主要取决于铁素体的晶粒尺寸、晶体织构和钢中的夹杂物。通过合适的化学成分设计以及采用适宜的夹杂物控制技术,可以获得最佳的夹杂物控制效果,使其纯净度大幅度提高或者无害,最终获得磁性能优良的高级别无取向硅钢。同时,为满足节能、环保、高效需求,无取向硅钢正朝着节能降耗、环境友好以及多功能、高效率、易加工等方向发展。 The magnetic properties of non-oriented silicon steel mainly depend on the grain size of ferrite,the crystallographic texture,and the inclusions in the steel.The optimum inclusion control effects can be obtained through a suitable chemical composition design and an appropriate inclusion control technique,and then the liquid steel will get be clean or the inclusion will get be harmless,finally the excellent magnetic property of the non-oriented silicon steel can be obtained.Meanwhile,in order to ...
晶粒尺寸对无取向硅钢磁性能影响的主成分回归分析
采用电子背散射衍射技术测定50SW1300冷轧无取向硅钢中不同尺寸范围晶粒的含量,利用主成分回归分析法,综合研究不同尺寸范围晶粒的含量对无取向硅钢磁性能的影响。结果表明:通过主成分回归分析法能够从不同尺寸范围晶粒的含量的多个影响因素中获取主要的因素,定量研究它们对无取向硅钢磁性能的影响规律。分析表明,无取向硅钢的铁损与不同尺寸范围晶粒的含量之间存在着可靠的多元线性关系,在一定范围内,较大尺寸晶粒的含量越多,其对铁损优化的作用越明显;而无取向硅钢的磁感与不同尺寸范围晶粒的含量之间并无线性关系。 Distribution of grain size in cold rolled non-oriented silicon steel 50SW1300 was measured by EBSD.The effects of the distribution of grain size on magnetic properties of the non-oriented silicon steel were comprehensively researched by means of principal component regression method.The results indicate that the main factors influencing the magnetic properties in the distribution of grain sizes,which can be used to guantitative study the magnetlic properies of the steel,are obtained by principal...
夹杂物尺寸及数量对无取向硅钢磁性能影响的主成分回归分析
采用扫描电镜、场发射扫描电镜、能谱仪等对50SW1300冷轧无取向硅钢中的夹杂物分不同尺寸区间进行数量统计,利用主成分回归分析法,即数据的标准化处理—主成分分析—回归分析—标准化的变量还原成原始变量—确定显著影响因素,综合分析夹杂物总量及各尺寸区间的夹杂物数量对无取向硅钢磁性能的影响。结果表明:主成分回归分析能够从夹杂物尺寸区间及数量的多个影响因素中提取主要的因素,定量研究其对磁性能的影响。分析表明,显著影响无取向硅钢铁损的夹杂物为100~500nm的AlN、AlN+MnS、MnS、Al2O3、AlN+Al2O3,而劣化磁感最明显的夹杂物尺寸区间为100~200nm。 Different size intervals of inclusions in cold rolled non-oriented silicon steel 50SW1300 were counted by scanning electron microscope(SEM),field emission scanning electron microscope(FESEM)and energy disperse spectroscopy(EDS).With principal component regression method:standardization for experimental data,principal component analysis,regression analysis,transform standardized variables into original variables,determination of significant factor,effects of the total number of inclusions and the...

