钢厂
电脉冲对取向硅钢凝固组织的影响研究
对取向硅钢熔融态钢液进行处理,对比研究了不同脉冲参数的作用效果。结果表明,电脉冲对钢锭晶粒组织具有明显的细化作用,凝固组织等轴晶比例大幅上升。在脉冲电容、频率、处理时间和电压中,影响等轴晶比例的最显著性因素为脉冲频率;最优正交实验参数为:电容1 200μF,脉冲频率1 Hz,处理时间5 s,脉冲电压800 V;随着输入能量的增大,等轴晶率先增大后减小,脉冲输入能量为某值时,等轴晶率最大,通过经典形核理论和热力学对这一现象进行了解释。 Research on the influence of the electric pulse on the solidification structure of oriented silicon steel was preformed.Electric pulses in different parameters were applied to molten steel and results were compared.The result shows that solidification structure of oriented silicon steel can be improved by the electric pulse,and the equiaxed crystal ratio increases obviously.Among the four parameter factors of electric capacity,frequency,applied time and voltage,the most effective factor is frequ...
二次冷轧法与三次冷轧法制备取向硅钢薄带的织构转变规律
本文以热轧常化板为初始材料,采用二次冷轧法与三次冷轧法制备了0.1 mm厚的取向硅钢薄带,测定相应的磁性能,并通过EBSD取向成像技术检测了二次冷轧法与三次冷轧法各工艺过程中织构与组织演变规律。结果表明,采用最终冷轧压下率适中的三次冷轧法,能在冷轧至0.1 mm时保存较多的高斯晶核,使得高温退火后的磁性能明显优于二次冷轧法。最终冷轧压下率通过影响脱碳退火后样品中的{111}<112>织构组分及Goss晶粒数量对最终二次再结晶产生重要影响。 Grain-oriented silicon steel sheets with a thickness of 0. 1 mm were produced from hot-rolled and normalized sheets by two-step-rolling and three-step-rolling methods. Their magnetic properties were measured,and the textures were detected by EBSD technique. The results show that the three-step-rolling method,which has a moderate reduction rate of final cold rolling,can maintain more { 110} < 001 >nucleus,and thus obtaining better magnetic properties compared with the two-step-method. The f...
无取向硅钢钢液增钛原因分析
对无取向硅钢炼钢全流程钢液增钛的原因进行了分析,认为铁水钛含量、转炉出钢温度、转炉下渣量、精炼渣TiO2含量、钢水罐及RH浸入管混钢种生产是影响钢液增钛的主要原因。通过采取低钛铁水冶炼,减少转炉下渣量,提高出钢温度,添加白灰改质精炼渣等措施,均能够降低钢液中的钛含量。 After analyzing the causes leading to increased content of titanium in molten nonoriented silicon steel during whole steelmaking process, it was concluded that such factors as content of titanium in hot metal, tapping temperature from converter, quantity of roughing slag entered into the ladle from converter, content of TiO2 in refining slag, molten steel ladle car,carrying out the steelmaking of different steel grades by the same ladle and same RH immersion tube were the main causes ...
低温高磁感取向硅钢高温退火过程织构及析出物的演变行为
对低温法生产的以AlN为主抑制剂的Hi-B取向硅钢高温退火过程进行了中断实验,借助EBSD及TEM技术对高温退火连续升温过程中织构与析出物的演变进行了研究。实验结果表明,800℃时ODF图出现高斯织构组分,但强度很弱,高斯晶粒偏离角在10°以上;950~1 000℃时高斯晶粒异常长大,偏离角3~6°;高温退火过程析出物主要有球形、规则立方形及不规则多面体形3种形貌,由于渗N的影响,Zener因子先增大再减小,并且析出物在高斯晶界前沿优先粗化。 The annealing process at high temperature of Hi-B silicon steel using low slab reheat temperature and with AlN as the inhibitor has been studied by interrupting test,and the evolution of texture and precipitates during continuous heating-up in the annealing process at high temperature was analyzed by EBSD and TEM. The results showed that Goss texture appears in ODF at 800 ℃,but the intensity of Goss texture was very weak and the deviation angle was more than10°. Goss grains grow abnormally durin...
云边一体化系统架构下硅钢制造管理业务数字化融合应用
提出以“云边一体化架构”构建硅钢智慧决策系统，来解决原硅钢制造L1～L5系统架构模式下的数字信息孤岛、业务功能割裂等问题。在此基础上，开发了云边协同的自学习型控制模型及业务决策模型，构建起硅钢“智慧大脑”，形成了以研发、制造、服务等核心业务数字化融合的智能化决策支持新模式，探索出一条钢铁制造业数字化、智能化转型之路。 SIDS(Silicon-steel Intelligent Decision-making System)based on \"cloud-edge integration architecture\" was proposed to solve the problems of data silos and business function fragmentation in the original L1～L5 system architecture.On this basis,the self-learning control model and decision-making model of cloud-edge collaboration were developed,the \"smart brain\" of silicon steel department was constructed,and a new intelligent decision-making support model of digital integration of core businesses s...
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...
低温普通取向硅钢高温退火过程中高斯晶粒的演变
对低温加热工艺生产的普通取向(common grain-oriented,CGO)硅钢的高温退火过程进行了中断实验,材料为含3.0%Si、0.5%Cu、0.009 8%S(均为质量分数)的以Cu2S为主抑制剂的普通取向CGO钢。原始板坯厚度为230 mm,于1 200℃均热后经4道次粗轧、7道次精轧至2.3mm;热轧板采用两次冷轧法轧至0.3mm,中间完全脱碳退火,最后于1 200℃高温退火。最后样品的磁性能:铁损P17/50为1.182W/kg,磁感应强度B8为1.897T。借助配有EDAX OIM电子背散射衍射(EBSD)系统的ZEISS SUPRA 55VP扫描电子显微镜,对高温退火过程中高斯晶粒的演变进行了研究,结果表明:升温过程中晶粒尺寸增长缓慢,650℃时取向分布函数(ODF)图出现高斯织构组分,但强度很弱,高斯晶粒偏离角小于9°;950℃时高斯晶粒平均生长速度超过其他晶粒;950~1 000℃时高斯晶粒异常长大,偏离角降至约3°;在950℃之前高斯取向晶粒相比于其他晶粒没有尺寸优势。 The high-temperature annealing process of common grain-oriented(CGO)silicon steel was investigated by interrupting test.The samples were rolled from CGO silicon steel slab under low reheating temperature.The CGO silicon steel,taking Cu2S as the main inhibitor,contains3.0%Si,0.5%Cu,and 0.0098%S.The original casting slab is 230mm in thickness.After 1 200℃reheating,four-pass rough rolling and seven-pass finish rolling were conducted to make the thickness of the slab get to 2.3mm.Then the hot rolled...
冷轧产线硅钢激光高速切边实验研究
为了解决目前冷轧产线硅钢现有切边技术存在的微裂纹、应力、毛刺和边浪等问题,采用光纤激光器进行了高速切割实验,对激光功率、切割速度、激光模式等影响因素进行了分析,同时对高速切割时的切割前沿形状进行了研究。结果表明,切割最高速度随着功率的增加而增加;随着离焦量的增加,切割质量下降,挂渣增多,切缝宽度增加,切割深度变浅;基模激光器能量密度更高,所以薄板切割时的切割速度高于多模激光器;切割前沿随着切割速度的增加会变得平缓,速度足够大时,切割前沿甚至接近与切割方向平行,此时切缝下部存在挂渣现象。 In order to solve the problems such as micro crack,stress,burr,edge waves and so on,existing in cold rolling production line of silicon steel side cutting,experimentsof high speed laser cutting using fiber laser were carried out.Laser power,cutting speed,laser mode and other factors were analyzed,at the same time,cutting front geometries were studied.The results show that the maximum speed increases with power.And the increases of defocus result in poor quality,wider kerf and shallowerkerf.Energ...
