钢厂
Fe-3.15%Si低温取向硅钢不同常化工艺下的组织及析出相研究
利用OM,TEM,EDS与XRD技术,对Fe-3.15%Si低温取向硅钢热轧板不同常化处理后的显微组织、析出相及最终产品的磁性能进行了分析研究,并对热轧板和常化板经过冷轧后的冷轧板织构进行了对比分析.结果表明,采用1120℃保温3 min二段式冷却的常化处理工艺,常化板表层显微组织均匀,沿板厚方向的显微组织的不均匀性显著,对后续过程中形成高取向的Goss织构最有利,取向硅钢的磁性能最高;采用二段式冷却的常化冷却工艺最优,在此冷却工艺下析出的细小的析出物数量最多,且弥散分布在基体中,抑制剂的抑制效果最好,对成品获得高磁性最有利;热轧板、常化板经过冷轧后的冷轧板织构均主要由{111}〈110〉和{111}〈112〉织构组成,但常化板较热轧板冷轧后的冷轧板γ取向线织构密度明显增高,由此可以证实常化处理有助于取向硅钢最终获得高取向的Goss织构. The decreasing of slab heating temperature for grain-oriented silicon steel will reduce the amount of precipitates in hot rolled plate,and be disadvantage to the formation of ultimate Goss texture.The aim of normalizing is to control and adjust the amount,size and distribution of precipitates.Microstructures,precipitates and magnetic characteristics of finished products with different normalizing technologies for Fe-3.15%Si low temperature hot rolled grain-oriented silicon steel are researched,a...
热轧组织对无取向硅钢织构的影响
采用X射线衍射仪分析无取向硅钢冷轧织构和再结晶退火织构的演化,研究了热轧组织对无取向硅钢织构以及磁性能的影响。结果表明,具有均匀、粗大晶粒组织的热轧板,冷轧形成更多的剪切带,导致成品板形成高的高斯织构组分,并提高了{100}织构强度,降低了γ纤维织构,最终导致成品磁感应强度升高,铁损下降。 The texture evolution of non-oriented silicon steel during cold-rolling and recrystallization annealing was investigated by X-ray diffractometer.The effects of hot band microstructure on the texture and magnetic properties of non-oriented silicon steel were analyzed.The results show that more shear bands appear during cold rolling due to homogeneous coarse grain in hot band.So strong{110}<001> texture after recrystallization annealing forms.At the same time,the {100} texture components inc...
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...
