共查询到20条相似文献,搜索用时 796 毫秒
1.
2.
3.
通过Gleeble-1500热模拟试验机对GCr15轴承钢(/%:0.99C,0.31Mn,0.24Si,0.010P,0.003S,1.44Cr,0.01V)热轧材进行800~1 150℃,变形速率0.1~3 s~(-1),变形量0.7的等温压缩变形试验。采用已建立的Hensel/Spittel变形抗力模型,运用LARSTRAN/SHAPE有限元模拟软件对200 mm×200 mm连铸方坯连轧Φ70 mm GCr15轴承钢棒材10道次热连轧过程进行三维热力耦合有限元模拟。通过分析各道次轧件温度场、应力应变场、宽展及轧制力参数的变化规律,预测了轧件在第5道次最有可能在角部出现裂纹,因此在轧制过程应减小第5道次变形量,防止产生裂纹;各道次出口处轧件横截面宽度、高度尺寸的模拟值与实测值的相对误差分别为0.40%~5.90%和0.28%~6.11%。 相似文献
4.
5.
6.
7.
8.
热轧三机架连轧轧板过程的二维有限元模拟 总被引:2,自引:0,他引:2
应用MARC/AutoForge有限元软件,对轧件在三机架连轧变形过程进行热力耦合模拟。研究了模拟过程中的轧件温度场的分布及变化规律以及轧制力能参数在轧制过程中的变化。分析计算说明,采用有限元模拟的方法可以较好地反映金属的实际变形情况。 相似文献
9.
通过对棒材热连轧过程的分析,建立了20CrMnTi钢800~1150℃,变形量0~0.8,应变速率0~3 s-1的Hensel-Spittel流变应力模型;利用LARSTRAN/SHAPE有限元软件模拟了20CrMnTi从200 mm×200 mm的方坯经8道次连轧为Φ90 mm圆棒的过程,分析了轧件在圆弧侧壁的圆孔型和直线侧壁的圆孔型下轧制过程中的应力场、应变场、温度场和轧制力及力矩的变化情况。模拟结果表明,轧件圆角部位等效应力、等效应变较大且温度较低,容易出现轧制质量缺陷;圆弧侧壁的圆孔型轧制圆钢时的精度略高于直线侧壁的圆孔型。 相似文献
10.
采用非线性有限元分析软件ABAQUS,通过建立的线材与轧辊的3维热机耦合模型,对钢厂82B钢(/%:0.79~0.86C、0.15~0.35Si、0.60~0.90Mn、≤0.030S、≤0.030P)φ20mm至φ16.5mm 4道次预精轧过程中轧件的温度场、应力-应变场和轧制力进行数值模拟和分析。结果表明,数值模拟结果与实测结果相符;预精轧过程轧件心部温度和表面温度的差值为~130℃;运用该模型对现场轧制过程中的前滑进行了分析,得出了影响前滑的因素主要有延伸系数、轧制孔型尺寸、轧制速度以及辊径与轧件厚度比值。 相似文献
11.
An FE model was developed to study thermal behavior during the rod and wire hot continuous rolling process. The FE code MSCMarc was used in the simulation using implicit static arithmetic. The whole rolling process of 30 passes was separated and simulated with several continuous 3D elastic plastic FE models. A rigid pushing body and a data transfer technique were introduced into this model. The on line experiments were conducted on 304 stainless steel and GCr15 steel hot continuous rolling process to prove the results of simulation by implicit static FEM. The results show that the temperature results of finite element simulations are in good agreement with experiments, which indicate that the FE model developed in this study is effective and efficient. 相似文献
12.
采用MSC.Marc有限元软件和接触分析技术,对TC4钛合金(%:0.08C、6Al、4V、0.3Fe、0.05N、0.015H,余量Ti)Φ32 mm棒材在平三角孔型中的三维热变形过程进行了模拟仿真,并定量分析了轧件温度场的变化。结果表明,开轧温度950℃时,TC4棒材在平三角孔型中稳定轧制,变形均匀,但沿横断面温度出现梯度,最低温度910℃,最高966℃。 相似文献
13.
14.
为了更好的对精轧过程进行数值模拟,提供更为合理的轧制参数,以LS-DYNA软件中的单元更新方法模拟了精轧过程。建立好第1道次轧制模型并数值模拟,对第1道次计算结果进行了分析。以第一道次轧制后的单元形状为基础,进行单元更新,改变边界条件和载荷,对第2道次进行数值模拟,用同样方法对3~7道次进行数值模拟。该方法在准确地模拟了精轧过程中板带变化的同时,为各精轧道次提供准确的轧制参数。 相似文献
15.
16.
Stretchreducingprocessisoneofmainproduc tionmodesforhot rollingsteeltubes .Bothdiameterandwallthicknesscanbecontrolledonstretchre ducingmill (SRM ) .Themethodfortubeproduc tioncanobtainthelargestdiameterreductiononconditionthatthewalloftubedoesn’tbecomepol… 相似文献
17.
3D thermo-meehanical coupled simulation of whole rolling process for 60 kg/m heavy rail was accomplished by FEM method. The finite element model, physical parameters of U75V and parameter setting of simulation were introduced in detail. The whole rolling process of 60 kg/m heavy rail was divided into 27 time cells to simulate respectively, and the model rebuilding and temperature inheritance method in intermediate pass were proceeded. Then, based on simulation results, the workpiece deformation result, metal flow, stress and strain of 60 kg/m heavy rail for typical passes were obtained. The temperature variation curves of whole rolling process for section key points of 60 kg/m heavy rail were plotted, and the temperature falling law of whole rolling process for 60 kg/m heavy rail was studied. In addition, temperature distribution of 60 kg/m heavy rail after whole rolling process was analyzed, and the results showed that temperature was highest at center of rail head and lowest at fringe of rail base. Moreover, the simulation results and measured results of rolling force for 60 kg/m heavy rail were compared, and the regularity was in good agreement. 相似文献
18.
19.
A combination of finite element method and neural network methods was used for rapid prediction of the roll force during skin pass rolling of 980DP and 1180CP high strength steels. The FE based commercial package DEFOEM-2D was used to develop a mathematical model of the skin pass rolling operation. Numerical experiments were designed with different process parameters to produce training data for a neural network algorithm. The friction coefficient was considered as an input parameter in the neural network but it was optimised using an iterative method employing an equation that relates the friction coefficient to the rolling force. The load prediction method described in this paper is sufficiently rapid that it can be used in real-time as an adjustment tool for skin pass rolling mills with error within 10% (based on plant data from POSCO). 相似文献