硅钢资料

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... 

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... 

以能量平衡和辐射换热理论为基础,通过合理假定,建立了硅钢无氧化加热炉数学模型。采用数值计算的方法,通过自编程序,完成对带钢加热过程温度场的仿真。结果显示:数学模型能够反映带钢在无氧化炉内的加热过程,其升温曲线能够与工艺曲线相吻合;现行炉温分布并非最优,数学模型能够为炉温分布的优化、炉段的设计提供理论依据。 Mathematical model of NOF Section of Continuous Annealing Furnace is established based on energy balance and radiation heat exchange. The temperature field of steel is simulated by mathematical computation. The simulating results show that the mathematical model can reflect the heating process of silicon steel in NOF section, and the heat-up curve is coincide with the processing curve. From the results,it is known that the current furnace temperature gradient is not the best, and the mathematica... 

研究表明,硅钢中的夹杂物对成品带钢的磁性能有显著影响。为研究冶炼过程硅钢中的夹杂物遗传变化,进而提出更有效的控制措施加以去除,本文结合典型的无取向硅钢生产炉次,采用非水溶液电解提取+扫描电镜观察方法分析冶炼过程中上述炉次典型试样的夹杂物。结果表明:转炉冶炼结束、RH精炼开始时,钢的氧化物夹杂总量最大,约为0.23%;RH精炼过程中,氧化物夹杂总量不断降低,并在脱碳结束时达到最低,约为0.02%;连铸过程中,氧化物夹杂总量仍有不断降低趋势,但夹杂物的平均尺寸变化不大。本试验条件下,中间包试样的夹杂物数量约为1.59×104个/mm3。 As we all know, the non-metallic inclusion effects magnetic properties of silicon steel sheets obviously. The article aims to study the heredity of non-metallic inclusion in non-oriented silicon steels during the steel making process, and then provides a more effective controlling measure to remove the inclusions. Based on the typical non-oriented silicon steel charges, the non-aqueous solution extraction and SEM observation were adopted to analyze the non-metallic inclusions. Results show that,...