论文

采用电解法和扫描电镜研究了300 t转炉-RH精炼钙处理对无取向硅钢板（%:≤0.005C、1.2～2.2Si、0.2～0.6Mn、≤0.20P、≤0.005S、0.2～0.6Al、0～0.01Ca）中夹杂物的影响。结果表明,钢中Al含量为0.25%和0.35%时,钢中溶解氧均小于1×10^-4%,钙处理后都会产生CaS夹杂物,尤其是含0.35%Al的钢水;钙处理可以有效减少钢中的夹杂物数量,尤其是0.5μm以下的微细夹杂物数量;钙处理后夹杂物的种类以AlN、CaS为主,同时还含有少量的氧化物夹杂物以及AlN-CaS复合夹杂物,尺寸主要为1.5～5.0μm。 The effect of 300 t converter-RH refining calcium treatment on inclusions in non-oriented silicon steel sheet （%:≤0.005C,1.2～2.2Si,0.2～0.6Mn,≤0.20P,≤0.005S,0.2～0.6A1,0～0.01Ca） has been studied by electrolysis and scanning electron microscope.Results show that with 0.25%and 0.35%Al content in steel,all the dissolved oxygen in liquid is less than 1×10^-4%,and the CaS inclusions are produced after calcium treatment,especial for the liquid containing 0.35%Al;the amount of inclusions in ste... 

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,... 

【机构】 宝山钢铁股份有限公司规划与科技部； ...

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... 

对碳-锰-硅钢进行不同配分温度的Q&P(Quenching and Partitioning)处理,测试了热处理后不同钢的力学性能和残余奥氏体含量,并用扫描电子显微镜和透射电镜观察其显微组织,分析了配分温度对显微组织和力学性能的影响。结果表明:试验钢显微组织基本由低碳板条状马氏体、块状铁素体和条状残余奥氏体组成;随配分温度的升高,试验钢的抗拉强度呈下降趋势,伸长率与奥氏体含量的变化趋势相同,但变化规律不确定;提高锰含量能稳定残余奥氏体,从而提高试验钢的伸长率,并使伸长率对配分温度不敏感。 The C-Mn-Si steel was quenched and partitioned at different partitioning temperatures,the mechanical properties and residual austenite contents were investigated,the microstructure was observed by SEM and TEM,and the effect of partitioning temperature on microstructure and mechanical properties was analyzed.The results show that the microstructure of the tested steel consisted of lath martensite with low carbon,nubby ferrite and banded residual austenite.The tensile strength of the tested steel ... 

研究了热处理工艺(退火温度、保温时间、冷却方式)对含Mn的Fe-6.5wt%Si高硅钢热轧带的显微组织及硬度的影响。结果表明,退火温度对Fe-6.5wt%Si合金的组织和显微硬度均有较大影响,随退火温度升高,平均晶粒尺寸和显微硬度均明显增大。小于1 h退火时,晶粒长大缓慢,而长时间(>1 h)退火时,一些次表层晶粒将发生异常长大。水冷组织比空冷组织略细小,但水冷显著降低了显微硬度。Mn含量提高能抑制850℃退火时晶粒的长大,并且促进退火后高硅钢的软化。 The influences of heat treatment process(annealing temperature,heating time,cooling methods) on the microstructure and microhardness of Fe-6.5wt%Si high silicon steel containing manganese for hot rolled strip were studied.The results show that the annealing temperature has a great effect on the microstructure and microhardness of Fe-6.5wt%Si alloy,and as the annealing temperature increases,the average grain sizes and microhardness will significantly increase.When annealing time for less than 1 h... 

结合工业化生产的50A1300牌号无取向硅钢,分析了化学成分、RH精炼脱氧方式、板坯装炉温度以及热轧平整工艺等对磁性能的影响,探讨成品钢磁性能的改善。结果表明,采取改善后,50A1300牌号无取向硅钢的磁性能得到明显改善。2012年,该钢种平均铁损、磁感应强度分别达到5.26 W/kg、1.762 T,能够较好地满足用户市场需求及同行对标需要。 Based on the industrial manufacture of 50A1300 grade non-oriented silicon steel sheets,effects of chemical composition,deoxidization method in RH vacuum refining,charging temperature of steel slabs,hot rolling and flattening progress on magnetic properties are analyzed.The optimization methods of magnetic properties of steels are discussed. Results show that the magnetic properties are obviously improved by the adopted measures above. In 2012,the average core loss and the magnetic induction of 5... 

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... 

结合工业化生产的无取向硅钢,进行了RH精炼喂CaSi线去除钢中的非金属夹杂物试验研究。针对不同的钙处理条件,分析了CaS夹杂生成热力学,观察了夹杂物的形貌和尺寸分布,确定了夹杂物的类型、数量,探讨了钙处理后钢中夹杂物的变化规律。结果表明,本试验条件下,钙处理可以有效抑制MnS、AlN夹杂物的生成,有效促进钢中微细夹杂物的聚合、上浮、去除,钢质纯净度明显提高。经过合适的钙处理后,钢中的夹杂物以独立存在的CaO为主,同时有少量含CaO、SiO2、MgO的复合夹杂,没有发现CaS夹杂存在。这部分夹杂物的尺寸集中分布在2～20μm,数量约为1.8×105个/mm3。 Experimental study on removal of non-metallic inclusions in non-oriented silicon steel obtained from industrial production by CaSi wire feeding during RH refining process was carried out.The thermodynamics of CaS inclusion formation was analyzed,the morphology and the size distribution of inclusions were observed,and the numbers and types of inclusions were also determined for the steel specimens treated under different calcium treatment conditions.Furthermore,the variation of inclusion characte... 

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... 

试验的铸态取向硅钢（/%:0.044⁰.056C,3.12³.32Si,0.08⁰.11Mn,0.002⁰.008S,0.002 9⁰.029 1Als,0.006 2⁰.010 9N）由30 kg高频真空感应炉熔炼。通过场发射扫描电子显微镜/能谱仪（FE-SEM/EDS）研究结果表明,0.002 9%Als钢中氧化物主要为SiO₂,存在片状、棒状及近似球状的独立MnS,未发现含铝的氧化物或氮化物;0.0090%Als钢中出现以Al₂O₃为主的复合氧化夹杂物,存在MnS与AlN的复合析出物。钢中Als增加,复合析出物多呈簇状发展。氧化物容易成为MnS-AlN复合析出的核心,钢中Als含量越低,夹杂物中的MnS含量越高;作为核心的氧化物夹杂的尺寸越小,形成的复合夹杂物的形状越规则,尺寸也越小。热力学计算结果表明,钢中Als含量主要影响了钢中氧化物夹杂的组成和AlN的析出温度及析出量。 Test as-cast grain-oriented silicon steel（/%:0.044 ⁰.056C,3.12 3.32Si,0.08 0.11Mn,0.002⁰.008S,0.002 9⁰.029 1 Als,0.006 2⁰.010 9N） is melted by a 30 kg high frequency vacuum induction furnace.The research results by using field emission-scanning electron microscope/energy dispersive spectrometer（FE-SEM/EDS） show that in 0.002 9%Als steel the main oxide is SiO₂,and there is independent laminable,rod-like and approximate ... 

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...