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为实现固态钢基体内夹杂物在冷轧过程中的控制,将硅脱氧304不锈钢热轧板经多道次冷轧至不同的厚度,利用扫描电子显微镜对试样内夹杂物在冷轧过程中的演变行为进行了研究。结果表明,硅脱氧304不锈钢内夹杂物的类型主要为低熔点SiO2-CaO-MnO-Al2O3,其在热轧板内的形貌为大尺寸长条状。冷轧时,这些长度为2.0~23.0 μm的长条状氧化物夹杂发生断裂,形成多个1.0~3.0 μm小尺寸夹杂物。随着冷轧压下量的增加,断裂后形成的夹杂物尺寸逐渐减小。但当夹杂物尺寸降低至约0.5 μm时,夹杂物不再发生断裂。同时,断裂后形成的小尺寸夹杂物之间的距离与夹杂物的初始尺寸无关,由冷轧板的伸长率决定。 相似文献
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不锈钢对冷板表面质量要求高,轧制过程中夹杂物是产生表面缺陷的主要原因之一。为了明确夹杂物对轧制过程表面缺陷的影响规律,通过中试模拟试验研究了热轧、退火和冷轧过程硬质镁铝尖晶石和低熔点硅酸盐2种典型夹杂物的变形特点,并采用数值模拟对冷轧过程夹杂物的变形机理进行了分析。结果表明,热轧过程高熔点的镁铝尖晶石不变形,低熔点的硅酸盐夹杂物在1 200~1 250℃热轧温度下为半熔融状态,具有良好的变形能力。硅酸盐夹杂物长宽比高、抗拉强度低,冷轧过程更容易断裂延伸,随着轧制的进行,断裂后夹杂物之间的距离逐渐增加,尺寸减小。相反,镁铝尖晶石不容易断裂、延伸,而且存在断裂延伸的临界尺寸,该临界尺寸随冷轧变形量的增加而减小。由于镁铝尖晶石容易造成不锈钢轧制缺陷,因此生产过程中应尽量避免其生成或控制其粒径。 相似文献
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为了改善热轧不锈钢复合板晶粒组织的均匀性,采用正交试验优化设计方法设计数值模拟方案,研究轧制工艺参数对基层不均匀因子、基层平均晶粒尺寸、复层不均匀因子和复层平均晶粒尺寸的影响,分析各参数的影响显著性顺序,并采用综合平衡法得到优选参数组合,轧制压下率为60%,轧制温度为1 100 ℃,轧制速度为300 mm/s。对优化后的参数组合进行有限元模拟,得到了热轧过程中沿不锈钢复合板厚度方向晶粒尺寸的分布及其变化规律。通过试验与仿真模拟获得的晶粒尺寸进行对比验证,得出晶粒尺寸误差在5%以内,验证了有限元模型的正确性与可靠性。 相似文献
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根据热轧不锈钢工程设计中的经验总结并结合国内已建成的宽幅不锈钢生产厂的工艺设备情况,从轧线布置形式和主要设备技术性能方面分析了不锈钢热轧板带生产工艺及设备选型的主要特点,指出不锈钢热轧生产与碳钢生产在工艺设备选型方面的区别。认为采用R1+F1~F7的工艺布置方式,较为经济、实用。为保证生产过程中的温度控制和产品质量,宽幅不锈钢粗轧机轧制力宜选择在42 000 kN以上,主电机功率12 000 kW以上。中间坯保温方式宜采用热卷箱形式。精轧机组前应设置小立辊轧机对带坯进行边部轧制。 相似文献
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采用X射线衍射技术和光学显微分析等方法,研究了同步和异步冷轧方式对SUS430铁素体不锈钢微观组织、织构演变以及力学和成形性能的影响。结果表明,与同步冷轧相比,由于异步轧制剪切变形的引入,可使异步冷轧板材的平均晶粒尺寸减小。在同步冷轧过程中,其二次冷轧再结晶织构为完整的γ纤维织构,导致铁素体不锈钢的最终性能优于同步一次冷轧;而在异步冷轧过程中,异步一次冷轧再结晶织构为强点{111}<[1][1]2>的γ纤维织构,异步二次冷轧再结晶织构属于随机取向织构,其结果是异步一次冷轧板材的性能优于异步二次冷轧。综合分析表明,与异步二次冷轧方式相比,同步二次冷轧方式有利于铁素体不锈钢性能的提高。 相似文献
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真空热轧法制备不锈钢复合板组织和力学性能 总被引:2,自引:0,他引:2
为了研究轧制温度对复合板界面结合强度的影响,采用真空热轧法制备了不锈钢复合板,利用OM、EPMA观察分析了不锈钢复合板界面组织和合金元素扩散。结果表明,碳钢中碳、铁元素向不锈钢扩散,不锈钢中铬、镍等元素向碳钢扩散,界面处出现Si-Mn-O三元化合物,合金元素扩散随轧制温度的升高而趋于严重。远离界面碳钢的组织为铁素体和珠光体组织,靠近界面碳钢的组织为铁素体组织。碳钢至界面处硬度先减小后升高,界面至不锈钢内部硬度先升高后下降,距界面约40 μm碳钢侧的维氏硬度值最低约为121.8HV,距界面约20 μm不锈钢侧的维氏硬度值最高约为245.5HV。从1 100到1 300 ℃,剪切强度随轧制温度的升高而升高,1 300 ℃轧制获得的界面剪切强度为463 MPa,远远超过基体的剪切强度。 相似文献
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采用热挤压和冷轧工艺生产HR3C无缝钢管,金属冷成型过程和成品性能均表明热挤压加冷轧工艺生产高合金难变形材料具有明显优势.热挤压加工变形时金属承受三向压应力,可以提高金属的综合性能;通过冷轧加工改善管材表面质量和尺寸精度,可以确保材料在特殊环境中使用安全性更高. 相似文献
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采用电化学测试技术与化学浸泡实验相结合,对比研究了409L和430铁素体不锈钢热轧板材耐点蚀和耐晶间腐蚀的能力.结果表明:409L不锈钢的击穿电位与保护电位的差小、钝化膜的修复能力较强,表现为小尺度的浅点蚀孔;430不锈钢的击穿电位较高,耐点蚀能力高于409L不锈钢,但430不锈钢热轧态的条带组织有明显的腐蚀微电池效应,表现为比较严重的全面腐蚀;409L不锈钢含Cr量低,且其热轧态未经过稳定化处理,使得409L不锈钢耐晶间腐蚀能力劣于430不锈钢.经微观分析:409L不锈钢为沿着基体等轴晶界的典型晶间腐蚀形貌特征,而430不锈钢在轧向碳化物(M23C6)与基体的相界面上呈现分层腐蚀痕迹. 相似文献
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不锈钢与碳钢冷连轧产品特性和标准要求不同,控制要求和数学模型也存在差异。笔者认为不锈钢与碳钢混合轧制时数学模型的结构和基本功能两者可以共用,但由于不锈钢自身材质的特点和产品要求的特殊性,不锈钢生产过程控制的设定要求有别于碳钢,因此,数学模型的参数层别划分及具体控制参数有较大差异。 相似文献
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利用扫描电子显微技术结合能谱分析对316L/Q345R热轧复合板结合界面组织及元素扩散情况进行了检测,通过热力学计算分析了界面附近碳的分布规律,并测量了结合界面的显微硬度与剪切强度。结果表明,结合界面碳钢一侧存在约50μm的铁素体带,而不锈钢侧存在约100μm的元素扩散影响区;不锈钢中铬、镍等元素向碳钢中扩散,碳钢中碳元素向不锈钢中扩散;复合板界面剪切强度为373 MPa,明显高于标准规定的210 MPa,略低于Q345R与316L剪切强度和的1/2(379 MPa)。 相似文献
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The flow behaviour and processing map of a duplex stainless steel were studied via hot compressive tests in a temperature range of 1223–1473?K and a strain rate range of 0.01–30?s??1. The effect of strain rate and temperature on the hot workability, strain partitioning and dominant flow behaviour of the alloy was systematically investigated. It is found that the softening mechanism of each constituent phase differs from each other. The ferrite is softened by dynamic recovery and continuous dynamic recrystallisation (CDRX), while the austenite is softened only by the limited discontinuous dynamic recrystallisation (DDRX). At lower strain rates (0.01 and 0.1?s??1), the strain is mainly accommodated by ferrite due to its excellent softening capability, which causes the apparent activation energy Qp to decline continuously with the increase in true strain. In this case, plastic deformation of the austenite rarely occurs, and at this time, DDRX of austenite is not observed. When the strain rate increases, CDRX of ferrite is weakened at a relative low temperature, which prompts the strain transfer into austenite and induces the strain hardening due to its restricted softening. Accordingly, interactions between the strain hardening in austenite and weakened softening of ferrite leads to one or more platforms of Q formed at the medium stage of deformation (1–30?s??1). The processing map shows that two flow instability regions appear at high strain rate due to the lack of sufficient response time for dynamic restoration at the early deformation stage. As the strain increases, dynamic softening mechanism is activated at a higher temperature, resulting in a gradually narrowed flow instability region. Differently, a decrease in temperature suppresses dynamic softening of the alloy with a high strain rate, which deteriorates the hot workability of the alloy and induces microcrack formation after straining of 0.8. 相似文献
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Thermodynamic studies were carried out to investigate the effects of temperature, molten metal composition on the relationship among MoO3, CaMoO4 and ??Fe??, ??Mn??, ??C??, ??Si??, ??Cr?? during the AOD remelting process. The calculated results show that MoO3 and CaMoO4 can easily be reduced by its reactions with active alloying elements during the refining process of 316L stainless steel. First of all, the feasibility of molybdenum oxide alloying for 316L stainless steel smelting was proved theoretically. Then an industrial test of 316L stainless steel alloyed with molybdenum oxide was performed in a 180t AOD furnace. The results show that the molybdenum oxide has no influence on composition of inclusions in the steel and quality of the cold rolling plate. Above all, molybdenum oxide used as alloying material during AOD of 316L stainless steel is applied in TISCO factory, which can relieve the environmental contamination burden from ferro- molybdenum alloying during the AOD process. 相似文献
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《钢铁冶炼》2013,40(4):298-304
AbstractTransformation induced plasticity (TRIP) steels exhibit excellent strength and ductility and can be engineered to provide excellent formability for manufacturing complex parts. In this study, a data driven multi-input multi-output multilayer perceptron based neural network model has been developed to predict the flow stress, yield strength, ultimate tensile strength and elongation as a function of composition and thermomechanical processing parameters for strip rolling of TRIP steels. The input parameters in this generalised regression artificial neural network (ANN) model are steel chemistry, cooling rate and finish roll temperature. The network training architecture has been optimised using the Broyden–Fletcher–Goldfarb–Shanno algorithm to minimise the network training error within few training cycles. The algorithm facilitates a faster convergence of network training and testing errors. There has been an excellent agreement between the ANN model predictions and the target (measured) values for flow stress and mechanical properties depicted by the respective regression fit between these values. 相似文献