钢厂
无取向电工钢XG800WR连铸电磁搅拌试验研究
采用铸坯低倍组织检验和化学分析的方法,研究板坯连铸机二冷区电磁搅拌器电流和频率参数对无取向电工钢XG800WR板坯中心偏析和等轴晶率的影响,结果表明:铸坯等轴晶率随着搅拌器电流强度和电流频率的增大而增加。采用二冷区电磁搅拌可减小中心易偏析元素S的偏析,试验得出:减小铸坯S偏析效果最好的电磁搅拌参数为电流380~400A,频率6Hz。 The effect of electromagnetic stirring current and frequency parameter at secondary cooling area on the central segregation and equiaxed crystal ratio of XG800WR non - oriented electrical steels slab was investigated through the methods of chemical and macrostructure analysis.The results shows that the rate of equiaxed crystal zone is going up with increasing of current intensity and current frequency.Electromagnetic stirring in the secondary cooling area can be easily reduced segregation caused...
高硫硅钢的硫化物析出行为及其微观组织和电磁性能变化
为了弄清楚高硫硅钢中的硫化物析出行为及其对钢的微观组织和电磁性能的影响,以便为工业化生产制定更为合理的硫含量控制标准和采取更为有效措施减轻炼钢生产的硫含量控制压力,结合0.25% Si 无取向硅钢 ,采用非水溶液电解提取 + 扫描电镜/透射电镜观察相结合的方法 ,研究了0.006 8%、0.010 2%、0.025 5% 和 0.035 3% 硫含量条件下,钢中的硫化物夹杂物组成和存在形式及其形貌、种类、尺寸、数量变化,以及相应的热轧、成品试样的微观组织和电磁性能变化。结果表明,随着钢中硫含量的增加,钢中的硫化物逐渐由 MnS→MnS+Cu2S→Cu2S转变,数量逐渐增多,尺寸向高低两个方向发展。相应地,导致热轧再结晶组织劣化和抑制了成品晶粒尺寸长大。随着钢中硫含量的增加,钢的磁感、铁损劣化程度逐渐增大。钢中的硫含量平均每增加 0.01%,涡流损耗、磁滞损耗分别劣化0.24 W/kg 和 0.41 W/kg,而磁感会劣化 0.009 T。但是 ,在硫含量为 0.010 2% 时 ,铁损可以低于 6.0W/kg,而在硫含量为 0.025 5% ... In order to find out the precipitation behavior of sulfide inclusions and the corresponding changes of microstructure and electromagnetic properties of high sulfur silicon steel sheets, so that to design more suitable sulfur concentration controlling limit for industrial manufacture and to release the steel-making difficulty effectively, Based on the change of given sulfur concentration 0.006 8%, 0.010 2%0.025 3% and 0.035 3%, the type and composition, the size and number, and the size distribut...
高硅钢薄板退火过程中的织构演变
采用传统的轧制和退火工艺制备了0.30mm厚的6.5%(质量分数)Si高硅电工钢薄板,采用X射线衍射技术对退火过程中的再结晶织构进行了研究。冷轧高硅钢薄板700℃退火形成以{111}〈112〉为峰值的γ织构(〈111〉∥ND)和以{001}〈210〉为峰值的{001}织构;而900℃以上温度退火则形成强{001}〈210〉织构。进一步的研究表明是在晶粒长大过程中{001}〈210〉发展成为主要再结晶织构组分。 High silicon steel thin sheets with thickness of 0.3mm were successfully produced by conventional rolling and annealing methods.Recrystallization texture was investigated by means of X-ray diffraction.It is found that recrystallization texture is mainly composed of γ fiber(〈111〉∥ND)with peak at {111}〈112〉 and {001} fiber with peak at {001}〈210〉 after annealing at 700℃,while strong {001}〈210〉 component dominates recrystallization texture after annealing above 900℃.It is during grain growth that {...
双辊连铸法制备硅钢薄带的组织和性能
采用双辊连铸工艺制备了硅的质量分数分别为0.5%,1.0%,3.0%,4.5%的硅钢薄带,用光学显微镜观察其组织,并研究了后处理工艺对薄带组织和性能的影响。结果表明:硅含量为0.5%和1.0%的薄带适合采用一次冷轧+850~950℃退火的后处理工艺,而硅含量在3.0%以上的薄带适合采用二次冷轧+950℃退火的后处理工艺;硅含量为3.0%和4.5%的薄带在冷轧并950℃退火后,其磁性能最佳,铁芯损耗约为4.30 W·kg-1,磁感应强度约为1.68T。 Silicon steel thin strips with silicon content of 0.5wt%,1.0wt%,3.0wt%and 4.5wt%were prepared by twin-roll continuous casting process,and the microstructure of the strips were observed by means of optical microscopy,and on the basis,the effect of post-treatment process on microstructure and properties of the strips was studied.The results show that the post-treatment process of one-time cold rolling and annealing between 850—950℃was suitable for the strips with silicon content of 0.5wt%and 1.0wt...
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...
ICP-AES法测定硅钢中铌的不确定度评定
分析了电感耦合等离子体发射光谱法(ICP-AES)测定硅钢中铌的检测过程,讨论了该检测过程中不确定度的主要来源,建立了该方法的定量的数学模型,并根据这一模型计算出了检测结果的合成标准不确定度和扩展不确定度。 The measurement of niobium content in silicon steel by inductively coupled plasma atomic emission spectrometry(ICP-AES) was analyzed,by which the main factors affecting the uncertainty of the measurement were ascertained and the calculation formula was given.Finally,according to the formula the combined uncertainty and expanded uncertainty were obtained.
进入硅钢叠片内的漏磁通和附加损耗的模拟实验与仿真
基于简化的取向硅钢片模型,系统地对不同的交流激励下的硅钢叠片内铁损、交链磁通和空气中指定位置的法向漏磁的分布进行了\"单片级\"的测量,并建立了相应的硅钢叠片级问题的三维有限元分析模型,进行了大规模的数值计算分析。模型实验和数值分析的结果表明垂直进入硅钢片的漏磁通和损耗呈现浅透入的特点,在硅钢片内引起的涡流损耗在总铁损中占据了\"举足轻重\"的份额。用电磁场有限元分析软件MagNet瞬态场时步法计算结果与测量结果相吻合,说明本文方法研究复杂的硅钢叠片问题的有效性。 The measurement and 3D finite element analysis of the iron loss,interlinkage flux inside the laminated silicon steel sheets and the magnetic flux densities at the specified positions are carried out based on a verifying silicon steel sheet model.The modeling results show that the leakage flux vertically through the silicon steel sheets has the peculiarity of shin effect,and the eddy current loss caused by the AC leakage flux is a significant component of the total iron loss.The calculated result...
离散粒子群优化算法在硅钢涂层近红外光谱厚度检测中的应用研究
提出一种基于粒子群优化算法实现的硅钢涂层厚度近红外光谱检测新方法。首先,采用近红外光谱仪采集获得了硅钢表面绝缘涂层的近红外光谱,然后,采用离散粒子群算法筛选出近红外光谱数据的最佳波长变量并组成新的光谱数据,最后,建立涂层厚度的核偏最小二乘定量分析模型。实验显示,所建定量分析模型对检验样本分析的绝对误差范围为-0.12~0.19μm,最大相对误差为14.31%,完全符合现场检验需要。研究表明,离散粒子群算法可以有效地筛选出携带更多有用信息的波长变量,提高定量分析模型的分析准确度和速度,是一种有效的近红外光谱波长筛选方法,同时,近红外光谱法也是一种有效的硅钢绝缘涂层厚度检测方法。 A novel thickness measurement NIR spectrometry for surface insulation coating of silicon steel based on discrete binary particle swarm optimization(DBPSO) algorithm is presented.First,we used NIR spectrometer to collect the NIR spectra of insulation coating of silicon steel,and then,DBPSO algorithm was used to select the optimal wavelength variates and composed a new spectra set.Last,the authors created the thickness quantitative analysis model using kernel partial least square algorithm.The exp...
火花源原子发射光谱法在线测定电工钢中的超低碳
采用火花源原子发射光谱分析测定电工钢中超低C,研究试样制备方法、Ar纯度和压力等条件对分析结果的影响,并对工作曲线进行了优化,实现了一次分析同时测定电工钢的多种元素,满足炉前和精炼在线分析的要求。 Ultra-low carbon in electrical steel is determined with spark-source atom emission spectrum.It is researched the influence of sample making method,purity of Ar,and pressure on analysis result.The work curve is optimized.The determination of many elements in electrical steel only in one analysis is realized.It meets demands of on-line analysis for blast furnace and refining.
低温高磁感取向硅钢高温退火过程高斯晶粒的演变
对低温加热工艺生产的以AlN为主抑制剂的高磁感取向硅钢高温退火过程进行中断实验,借助电子背散射衍射技术对高温退火过程中高斯晶粒的演变进行了研究.在升温过程中高斯晶粒平均尺寸先减小再增大.800℃时取向分布函数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;900℃时高斯晶粒平均生长速率超过其他晶粒;950~1000℃时高斯晶粒异常长大,偏离角3°~6°;在1000℃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 high permeability grain-oriented silicon steel with AlN as an inhibitor was studied by interrupting test.The evolution of Goss texture in this process was analyzed by electron back-scattered diffraction.It is found that the Goss grain size first decreases and then increases with the rise of temperature.Goss texture appears in the orientation distribution function at 800 ℃,but the intensity is very weak and the deviation angle is more than 10°.The average...
辉光放电发射光谱法测定电工钢中8种元素
通过对辉光放电发射光谱法分析电工钢样品光谱行为的研究,分析其工作参数如:电压、电流、预溅射时间和积分时间对光谱强度和稳定性的影响,并以铁为内标元素,优化了工作参数。确定了直流辉光放电光谱法测定电工钢中碳、硅、锰、磷、硫、铬、镍、铜共8种元素的定量分析方法,并对该方法分析的精密度和准确度进行验证,结果表明,各元素的测定结果与认定值和其他方法测定值一致,测量元素结果RSD值小于2%。 Based on research on analyzing the spectrum behavior of the sample taken from the electrical steel by glow discharge optical emission spectrometry,the effect of its operational parameters such as current,voltage,pre-sputtering time and integrating time on the spectral intensity and spectral stability is analyzed.Taking the Fe as an element applied by internal standard method,these operational parameters are optimized,and therefore the quantitative analysis method for testing eight kinds of eleme...

