硅钢资料

结合工业化生产过程中出现的同卷带钢头、尾磁性能差异现象,对50SW1300牌号无取向硅钢同卷带钢头、尾试样的夹杂物、晶体织构和显微组织进行了分析研究。结果表明,夹杂物、晶体织构是影响成品钢卷磁性能的重要因素。夹杂物是造成同卷带钢头、尾铁损差异的主要原因。夹杂物数量越多,尤其是小尺寸的夹杂物数量越多,对成品带钢的磁性能影响越大,对于本试验而言,AlN和MnS是影响成品带钢磁性能的主要夹杂物。晶体织构是造成同卷带钢头、尾磁感应强度差异的主要原因。有益的{100}和Goss织构含量越大,有害的{111}<110>和{111}<112>织构含量越小,即有益织构与有害织构含量比越大,成品带钢的磁感应强度越大。 Based on the industrial manufacture of non-oriented silicon steel sheets 50SW1300, the magnetic property variation of head and tail of the same finished steel sheets was discussed by analyzing non-metallic inclusion, crystal texture, and microstructure. Results show that, both of the non-metallic inclusion and the crystal texture will affect the magnetic properties significantly. The non-metallic inclusion is the key factor of the core loss variation of head and tail of the same finished steel s... 

研究稀土Ce的添加对含Sn高磁感无取向电工钢磁性能及夹杂物的影响。对比分析了两种成分钢的磁性能以及各过程工艺条件下夹杂物种类和分布情况。结果表明,在含Sn高磁感无取向电工钢中加入稀土元素Ce可以粗化夹杂物,提高成品晶粒的均匀性,有效降低铁损;同时Ce的添加不影响Sn元素提高磁感的效果,磁感保持不变。 Effect of Ce on magnetic properties and inclusion of Sn-bearing high permeability non-oriented electrical steel was investigated. A comparative analysis of the magnetic properties and the types and distributions of inclusion in the two sheets with different composition shows that the addition of Ce to the Sn-bearing high permeability non-oriented electrical steel can coarse the inclusion,improve the homogeneity of the finished grain and effectively decrease the core loss. Also,the addition of Ce... 

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...