钢厂
Al含量对2.2%Si无取向硅钢组织、织构和磁性能的影响
通过实验室25 kg真空感应炉冶炼和锻轧研究了0.26%~0.95%Al含量对0.5 mm 2.2%Si无取向硅钢冷轧板组织、织构和磁性能的影响。试验结果表明,0.26%~0.81%Al含量时,随钢中Al含量的增加,成品退火钢板的晶粒尺寸增加同时铁损减少;当Al含量大于0.81%时,随Al含量增加,钢板的晶粒尺寸减小,同时铁损增加。Al含量对硅钢板磁感的影响没有明显的规律。2.2%Si无取向硅钢的合适Al含量为0.48%~0.81%。 The effect of 0.26%~0.95%Al content on structure,texture and magnetic performance of 0.5 mm cold rolled sheet of 2.2%Si non-oriented silicon steel has been studied by a 25 kg vacuum induction furnace smelting,and forging -rolling in laboratory.Results show that as 0.26%~0.81%Al,with increasing Al content in steel the grain size of annealed finished sheet increases while iron loss of sheet decreases,and as Al content in steel is more than 0.81%,the grain size of sheet decreases while iron loss in...
无取向硅钢C6厚涂层性能的影响因素
本文介绍了无取向硅钢C6涂液的性能,研究了配水量、固化程度和涂层厚度等因素对无取向硅钢C6涂层性能的影响。结果表明,随着配水量的增加,完全固化所需的时间增加,涂液固体含量降低,涂层厚度减小;随着固化程度的提高,涂层硬度先增大然后趋于恒定,而柔韧性逐渐变差,在过固化后急剧恶化;涂层厚度对涂层的表面外观、附着性和绝缘层间电阻均有显著影响。 Based on the introduction about the performance of C6 varnish for non-oriented silicon steel sheets,effects of water amount,curing degree and coating thickness are discussed. Results show that with the increase of water amount,the time required to cure completely extends,and both the solid content of C6 varnish and the coating thickness decrease. As the curing degree increases,the hardness of the coating increases first and then tends to be constant,however the flexibility degenerates,especially...
常化和退火工艺对冷轧无取向硅钢高频磁性能和强度的影响
冷轧无取向硅钢(/%:0.003C,2.35Si,0.22Mn,0.011P,0.002S,0.36A1,0.003 0N)经890℃或940℃3 min常化的2.3 mm热轧板冷轧成0.35 mm薄板。研究了常化温度和800920℃3 min退火对该钢高频(400Hz)磁性能和抗拉强度的影响。结果表明,830920℃退火时高频铁损P10/400值最低,随退火温度增加,晶粒尺寸增大,钢的抗拉强度降低;该钢的最佳热处理工艺为常化温度940℃,退火温度830℃,其抗拉强度Rm、高频铁损P10/400和磁感应强度J50分别为565 MPa,21.5 W/kg和1.69 T。 The cold-rolled non-oriented silicon steel(/%:0.003C,2.35Si,0.22Mn,0.011P,0.002S,0.36A1,0.003 0N) is cold-rolled to 0.35 mm sheet from 2.3 mm hot-rolled plate normalized at 890 ℃ or 940℃ for 3 min.The effect of normalizing temperature and annealing process at 800 920 °C for 3 min on high frequency(400 Hz) magnetic properties and tensile strength of the steel has been tested and studied.Results show that with annealing at 830 920 ℃the high frequency iron loss value P10...
硅钢脱硫影响因素分析研究
对RH法(真空循环脱气法)生产的冷轧硅钢的脱硫原理及影响因素进行了分析研究。研究表明:降低顶渣中FeO、MnO的含量,提高钢液温度,增加脱硫剂的加入量并延长其循环时间有利于提高脱硫效率。 The principles of desulfurization and its influence factors of cold rolling silicon steel during RH process have been studied in this paper.The result indicated that reducing contents of FeO and MnO,rising temperature of the liquid steel,increasing quantity of desulfurizer,lengthening cycle time of desulfurzer are benefit for desulfurization.
提高热轧硅钢凸度精度的工艺措施
以太钢热连轧厂的硅钢为研究对象,利用凸度仪测量热轧带钢出口凸度值,采用矩阵和失效模式的分析方法了解影响带钢凸度变化的主次因素。研究结果表明:通过有效控制轧制过程中的加热温度、时间及采用合理的辊型,硅钢凸度精度提高了4.34%左右。 In this paper,we take the hot-rolled silicon steelsheet of Taigang as the research symbol,exit profile was measured by convex instrument,the primary and secondary factors that affect the strip profile were measured by using matrix and failure mode analysis methed.The results indicate that convexity accuracy has significantly improved by nearly 4.34% through the effective control of the heating temperature,heating time and the use of correct roller type in the process of hot rolling.
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...

