论文

磁性能指标是硅钢产品最关键的质量指标之一，但是目前磁性能判定100%依赖于样品的离线实验室检测结果，生产线配置的在线检测仪的测量结果由于精度问题，不宜直接用于成品牌号判级。本文在现有硅钢产品质量管控体系基础上，利用大数据技术对生产数据进行分析与建模，构建不同磁性能指标在线检测模型，并在现有信息系统上完成模型库的集成部署，实现硅钢产品全长、多指标磁性能结果的拟合数据输出，支撑取样优化、精准分切、辅助综合判定等功能应用，进一步优化硅钢产品质量管控体系。 The magnetic performance index of silicon steel products is one of the most critical quality indexes.However,at present,100%determination of magnetic performance depends on the offline laboratory test results of samples,and the measurement results of the online detector configured in the production line cannot be applied in practice due to the accuracy problem.Based on the existing quality control system of silicon steel products,big data technology was used to analyze and model the production d... 

某冷连轧机生产无取向硅钢板材时,其边缘降的长度合格率频繁超标。为满足用户的叠片厚度精度要求,这类产品必须以增大切边量的方式来保证交货质量,而这种方式又直接降低了金属收得率,提高了生产成本。基于此,以该产线现有设备特性为基础,详细分析其软、硬件功能模式,分别对辊形与反馈控制进行全面分析与优化改进。通过现场试验论证,充分明确依托辊形优化与反馈控制程序的改进能显著提高边缘降的控制水平,该技术的使用为生产现场的边缘降控制提供了合理的技术支撑。 A cold rolling mill production of non-oriented silicon steel sheet,the thickness difference of the edge( edge-drop) is usually exceed the preset standard value,In order to meet the accuracy requirements of the user flatness,these production must be trimed wider length to meet the delivery quality of the user need, which directly reduce the metal yield and increase the manufacturing cost. Therefor,the software and hardware of the production line has been analyzed, and the work roll shape and feed... 

聚焦无取向硅钢产品标准,从电磁性能、表面性能、机械性能以及尺寸公差与边部质量等维度对国际标准IEC、EN、JIS、ASTM以及国标GB进行了分析研究。分析得出,各类标准在电磁性能方面要求基本一致;ASTM标准的机械性能要求有别于其他几个标准。 In this paper,non-oriented silicon steel standards of EN,JIS,GB and ASTM were compared and analyzed. It is showed that,all types of standards in electromagnetic performance requirements are basically the same. Surface properties in ASTM standard are expressed in the most detailed. The mechanical properties requirements of the standards,except for ASTM,are basically the same. 

针对宝钢硅钢常化退火过程中产生的退火炉辊印缺陷问题,通过实际生产的大数据与产品质量问题相结合,将数据挖掘、数据分析方法应用到实际,一定程度上解决了现场实际生产中的痛点,为现场生产提供决策支撑,避免了以前通过人工识别判定存在疏漏和无法定量判断的问题,形成了一套具有鲁棒性和可操作性的钢铁生产过程数据分析方法。通过智慧决策系统平台获取实际生产和表检仪数据,基于Pearson相关系数算法进行变量挑选和特征工程,并应用随机森林算法对数据建立分类预测模型,实现了质量问题的溯源和监控,通过数据量化预测了炉辊印缺陷是否可通过轧制消除的质量问题,识别准确率达到96.43%。 In views of the normalizing annealing furnace roll marks problem occurred in the process of normalizing annealing of silicon steel in Baosteel,by combining big data from actual production with product quality problems,data mining and data analysis methods were applied to actual production to solve the pain points and provide decision support,a robust and practical data analysis method for the steel production process has been developed,which avoided the previous problems of omission and non-quan... 

利用DSC、XRD,结合热焓分析对硅钢级氧化镁和二氧化硅的固相反应行为进行了研究。比较了不同二氧化硅原料及混合方法对硅钢级氧化镁反应性的影响,找到了一种更为有效的硅钢级氧化镁反应性的评估方法,克服了传统柠檬酸活性法在比较不同厂家生产的氧化镁的反应性上的局限性。进一步研究了Na₂B₄O₇添加剂、TiO₂添加剂对氧化镁和二氧化硅反应行为的影响,结果表明,Na₂B₄O₇添加剂含量的增加对反应有利,而TiO₂添加剂对反应有阻碍作用。 The solid-state reaction behavior between silicon-steel grade MgO and SiO₂ was investigated by DSC and XRD combined with enthalpimetric analysis.The influence of different kinds of SiO₂ and different preparation methods were studied and a specifical measurement that is more reliable was proposed to evaluate reactivity of silicon steel grade MgO.It overcomes the limitation of the traditional citric acid activity（CAA） method,which can not be used to evaluate the reactivity of... 

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,... 

结合工业化生产的高效硅钢,进行了RH精炼稀土处理试验研究。针对不同的稀土处理条件,观察了夹杂物的形貌和尺寸分布,探讨了稀土处理后钢中的夹杂物形成、变化规律。结果表明:试验条件下,最佳的稀土合金添加数量为0.6～0.9 kg/t。经过合适的稀土处理后,可以有效抑制尺寸相对较小的、不规则的AlN、MnS复合夹杂生成,促进钢中微细夹杂物的聚合、上浮,钢质纯净度得到明显提高。此时,钢中全氧含量最低,脱硫效果最佳,钢中的夹杂物主要是尺寸相对较大的、近似球形或者椭球形的稀土类夹杂。 Based on the industrial production of non-oriented silicon steel,the experiment on RE treatment during the RH refining process was studied.The morphology and the size distribution were observed for the steel specimens treated with different RE treatment conditions.Further more,the formation and change of inclusions of final steel sheets after RE treatment were discussed.The results show that the suitable RE added amount was 0.6～0.9 kg/t.After the suitable RE treatment,the formation of AlN and Mn... 

无取向硅钢中夹杂物的存在会抑止晶粒生长,使基体的均匀连续性中断,其在钢中的形态、含量及分布情况都不同程度影响着硅钢的性能,尤其是对磁性能起关键的作用。因此,全尺度分布考察夹杂物对无取向硅钢夹杂物的研究极为重要。本实验确定了适用于不同牌号无取向硅钢夹杂物全尺度分布的分析方法:样品制备—小样电解—过滤喷金—根据不同牌号的要求选择合适的放大倍率扫描观测—夹杂物颗粒的分类统计。通过统计的结果,结合电解的失重量可以得到不同尺度的体积分布数据。实验分析了不同牌号和工艺无取向硅钢夹杂物的种类、形貌、大小和尺度分布,并初步考查了夹杂物与磁性能的关系,对无取向硅钢的工艺研究具有一定参考价值。 Inclusions in non-oriented silica steel could inhibit the growth of grain and cause discontinuity of micro-structure.The configuration,content and size distribution of inclusion have different effects on the performance of silica steel,especially significant on the magnetic property.Therefore,it is very useful to completely characterize inclusions with full size distribution in silica steel.In our research,full size analysis method for inclusion in silica steel had been established as follows: s... 

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... 

采用RH精炼添加钙合金方式对硅钢进行钙处理,结果表明,钙合金添加量为0.67、1.00和1.67 kg/t钢时,钢中Ca含量分别为0、2×10-6和4×10-(6质量分数);随着钙合金添加量的增加,钢中夹杂物粒度逐渐由0～2μm向2～4μm、4～6μm偏移;不同钙处理条件下,钢中均存在粒径小于1μm和粒径为1～5μm的MnS、CuxS夹杂物,后者或单独存在,或与AlN、CaS夹杂复合;钢中粒径为5～10μm的夹杂物基本以Ca的氧化物和硫化物为主。与未经钙处理的炉次相比,钙合金添加量为0.67、1.00和1.67 kg/t钢时,粒径小于1.0μm的夹杂物减少幅度分别为68.06%、87.50%和94.94%。钙合金添加量为1.67 kg/t钢时,可以去除钢中绝大部分的微细夹杂物。 In order to improve the properties of final silicon steels,the calcium treatment was adopted by adding Ca alloys into the liquid steel during the RH refining process.Results show that when the addition of Ca alloys is 0.67 kg/t,1.00 kg/t and 1.67 kg/t,the corresponding Ca content in silicon steels is 0,2 × 10-6 and 4 × 10-6 respectively.With the increase of Ca alloy addition,the particle sizes of inclusions in steels become 2～4 μm and 4～6 μm,from 0～2 μm.Under different calcium treatments,there e... 

选用国内外无取向硅钢标杆企业A、B、C的产品,采用非水溶液电解提取+SEM(EDS)方法,分析了其典型牌号对应成品试样中的夹杂物。结果表明,从磁性能控制角度而言,受钢的化学成分及生产工艺影响,A、B、C企业成品试样的夹杂物尺寸、种类、数量存在差异,这些差异对其磁性能产生显著影响;从夹杂物控制角度而言,A、B、C企业对应成品试样的夹杂物,以MnS、CuxS复合或者AlN、MnS、CuxS复合为主,其中1.0μm以下的夹杂物数量分别为2.34×107个/mm3、2.98×107个/mm3和11.98×107个/mm3,1.0μm以上夹杂物数量均很少,夹杂物的平均尺寸从大到小依次为A企业、B企业、C企业。 According to the production of three benchmarking manufacture enterprises A,B and C of non-oriented silicon steel at home and abroad,the inclusions in finished steel samples with typical grades were investigated by electrolytic extraction from non-aqueous solution and SEM(EDS).Results show that,from the viewpoint of improving the magnetic properties,affected by chemical composition and production technique,the size,type and amount of inclusions in finished steel samples are various,which will af... 

采用RH精炼添加钙合金方式对硅钢进行钙处理。结果表明,钙合金添加量为0.67、1.00、1.67kg/t钢时,钢中钙含量分别为0、2×10-6、4×10-6;随着钙合金添加量增大,钢中夹杂物粒度逐渐由0～2μm向2～4、4～6μm偏移;不同钙处理条件下,钢中均存在粒径小于1μm和粒径为1～5μm的MnS、CuxS夹杂物,后者或单独存在,或同AlN、CaS夹杂复合;粒径为5～10μm区间,钢中的夹杂物基本以钙的氧、硫化物为主。与钙处理前相比,钙合金添加量为0.67、1.00、1.67kg/t钢时,粒径小于1.0μm的微细夹杂物减少幅度分别为68.06%、87.50%、94.94%。钙合金添加量为1.67kg/t钢时,可以去除钢中绝大部分的微细夹杂物。 Ca alloy was added into the liquid steel during RH refining,and the results show that Ca concentration in final Si steel sheets is insignificant,about 0,2×10-6 and 4×10-6 when the added amount of Ca is 0.67,1.00 and 1.67 kg/t steel,respectively.With the increase in the added Ca alloy amount,the inclusions in the steel gradually change from those of 0～2 μm to those of 2～4 and 4～6 μm.Under different Ca treatments,there exist MnS and CuxS inclusions whose size is below 1 μm as well as MnS and CuxS ...