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... 

轧制力是影响冷轧带钢厚度精度的关键因素。为实现高精度的冷轧带钢厚度控制,通过优化变形抗力模型参数和摩擦系数模型参数提高冷轧轧制力模型计算精度,并使用指数平滑法的自学习算法保证轧制力精度的稳定性。在首钢股份公司迁安钢铁公司20辊森基米尔轧机生产线进行S12硅钢钢种轧制力优化试验,将优化的模型参数应用于L2并投入现场生产,结果表明该优化方法不仅提高了轧制力设定精度,而且减小了冷轧硅钢的厚度超差长度,提高了成材率。 The rolling force is the key factor which influences the accuracy of cold-rolling strip thickness. To implement high precision control of cold-rolling strip thickness,we optimize the deformation resistance model parameters and friction coefficient model parameters to improve the calculation precision of cold-rolling force model,and use exponential smoothing self-learn algorithm to ensure the stability of rolling force accuracy. In Shougang Qiangang 20-high Sendzimir mill single stand production ... 

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...