多目标质量的覆盖件成形工艺参数优化

覆盖件冲压是复杂的塑性变形过程,成形工艺参数较多,难以精确地建立工艺参数与成形质量之间的关系。选取优化合理的工艺参数匹配是保证无破裂、无起皱和板料厚度均匀分布等成形质量的关键。通过将压边力、拉深筋高度、凸筋圆角半径、凹筋圆角半径、摩擦因数作为自变量的条件下,以正交试验为设计方案,模拟轮包覆盖件成形,获得成形质量目标破裂、起皱、厚度最大变薄率的数据。通过层次分析法计算多质量目标的权重,利用灰色系统理论,分别计算成形质量对理想单目标值的关联系数和多目标函数的关联度,进一步计算获得各成形工艺参数的平均关联度,用优化的工艺参数进行有限元模拟验证,最终指导设计、试模,板料冲压结果表明成形的质量明显提高。     

数值模拟在摩托车油箱外壳体冲压成形中的应用研究

应用DYNAFORM软件对摩托车油箱外壳体冲压成形过程进行有限元数值模拟。针对实际生产中油箱外壳体材料厚度不均匀、局部变薄严重的现象，分析其变形特点及主要工艺参数对板料厚度变化的影响，确定了油箱外壳体冲压成形的三个主要工艺参数及模具调整过程中的修整原则。指出应先修整模具拉延筋高度，然后修整凹模圆角半径，再修整凸模圆角半径。生产实践表明，与传统设计的试模方法相比，采用有限元数值模拟方法，模具调试时间缩短75％，且得到的产品板料厚度均匀，刚性好，精度高。     

板料局部管状凸台壁部增厚成形工艺与模具

采用拉深、翻孔等常规工艺,在板料上形成局部管状凸台,然后对管状凸台进行镦压成形,以保证凸台的高度和增强管壁厚度;运用DEFORM软件对给定零件的增厚成形过程进行了数值模拟,设计了模具并进行了实验研究。实验结果表明,不论是从外壁扩径增厚成形还是从内壁缩径增厚成形,一次成形均容易在圆角处产生折叠,而采用二次成形,利用模芯圆角半径R≥6mm进行预成形,然后精整圆角部分,可以避免在圆角处产生折叠的缺陷,提高筒壁与底部的连接强度,保证增厚成形部分的质量。     

板材U形弯曲成形理论及有限元仿真分析

基于Prandtl Reuss流动法则及Von Mises屈服法则,结合更新拉格朗日法,建立增量弹塑性大变形理论,以rmin法则处理边界及弹塑性状态的转换,用修正的库仑摩擦法则的增量法处理边界接触面上滑动与黏滞摩擦,导入整体刚性矩阵,推导出刚性方程。对金属板材U型弯曲成形进行仿真分析,分析各项参数,包括摩擦系数凸模圆角半径、凹模圆角半径、冲裁间隙对成形性能的影响,以应力、应变、厚度及翘曲量为考察指标,模拟实验研究表明,U型弯曲成形性能受到模具及工艺参数的影响,并与实验结果具有良好的一致性。     

基于正交试验和能量转换的精密级进模多步拉深成形研究

应用正交试验方法,采用Dynaform对精密级进模多步拉深成形进行有限元数值模拟。结合能量转换原理,研究各步拉深中凹、凸模圆角半径、拉深高度以及压边力等工艺参数对成形件厚度和回弹的影响,并找出各因素影响的主次顺序。以射频(RF)连接器的壳件为例,通过上述方法优化各步成形参数,用以指导模具设计。     

镀层薄板拉深过程中镀层厚度变化影响因素分析

对镀层板料拉深成圆筒件的过程进行模拟及试验研究,得到了圆筒件各部分镀层厚度的变化规律。选取镀层最薄处厚度为镀层质量评价因素,利用LS-DYNA软件,基于正交法分析了拉深过程中的主要工艺参数（压边力、凹模圆角半径、凸模入口圆角半径和摩擦因数）对镀层厚度变化的影响规律,并结合拉深机理对其进行了深入研究,得到了所选工艺参数的最优组合。     

筒形件颗粒软凹模拉深成形试验研究

分析了颗粒软凹模成形的工艺特点,设计了筒形件颗粒软凹模拉深成形试验模具,对筒形件颗粒软凹模拉深成形进行了试验研究,成形出了质量良好的圆筒零件.颗粒软凹模拉深成形零件各部分的厚度均减小,而且随着拉深高度的增加,减薄量增大.与刚性模具拉深工艺相比,该工艺可有效提高板材的拉深成形极限.颗粒软凹模拉深成形和刚性模具拉深筒形件各部位表面微观形貌的对比分析表明:颗粒软凹模成形可抑制凸模圆角区域微裂纹的产生.     

膨胀节冲压工艺的研究及应用

针对锅炉膨胀节产品设计了冲压工艺和模具。模具整体结构为转轴式,主要包括凸模、凹模、凹模块、凹模复位支撑块、拉簧、拉杆和轴座。为验证该冲压工艺和模具结构的合理性,进行冲压试验。以压制产品的开口尺寸是否在92 mm～94 mm范围为评价指标,探究凸模圆角半径和角度对产品的影响,进而获得合理的设计参数,修改完善模具。试验结果表明:所设计的冲压工艺及冲压模具可以实现膨胀节的冲压制造,而且当凸模圆角半径R为42.7 mm、角度α为152°时,能够获得合格的膨胀节产品。     

正交试验设计在TA2筒形件拉深成形过程中的应用

研究了TA2筒形件的拉深成形过程,对无压边圈情况下拉深成形的5个因素(凹模入口圆角、凸凹模间隙、模具与板坯之间润滑系数、凸模圆角、坯料直径)进行了有限元数值模拟及其正交试验,提出了采用成形后板坯最大厚度与最小厚度之差Δt的概念来作为描述零件成形结果的评价标准,且论证了采用该评价标准的合理性。对试验结果进行了直观分析和方差分析,得出了各因素对拉深成形过程的影响次序及显著性。     

高强钢拼焊板V形模压弯曲回弹数值预测

基于ABAQUS有限元软件的UMAT和VUMAT接口,利用径向回退映射算法开发变模量的各向同性强化、线性随动强化和非线性随动强化的本构模型,耦合Aitken算法提高了非线性随动强化模型的收敛速度。利用耦合变模量的三种强化准则的模型,基于显式计算成形隐式计算回弹算法,在不同模压力和不同凸模圆角半径下对高强钢拼焊板V形模具弯曲的回弹进行预测,通过试验验证耦合应力相关变模量的非线性随动强化模型的回弹预测精度较高,最大预测误差为2.2%。应用此模型在不同凸模圆角半径和不同模压力下对高强钢拼焊板V形模具弯曲回弹后模量场和卸载后弯曲角进行预测与分析,拼焊板厚侧母板模量变化区较薄侧母板变化区域大,并在凸模圆角与直边过渡段上方出现反弯区。卸载后弯曲角随凸模圆角半径增大而增大,随着模压力的增大而减小。验证通过施加不同模压力可提高高强钢拼焊板V形模具弯曲工艺柔性的正确性,为进一步研究高强钢拼焊板V形模具弯曲自适应成形奠定基础。     

数值模拟分析工艺参数与拉深件壁厚变化的关系

在分析板料拉深成形有限元理论的基础上建立数值模拟的分析模型,利用数值模拟技术系统地对拉深过程进行模拟。主要研究模具圆角半径、摩擦因数、压边力与模具间隙等工艺参数与拉深件壁厚最大变薄率的内在关系。     

Effects of forming media on hydrodynamic deep drawing

Apart from the punch and the die, a pressurized fluid (water or oil) is used in hydroforming. The presence of such pressure media is the main difference between hydroforming and conventional deep drawing. No comprehensive study has yet been conducted on the effect of forming media on the formation of cylindrical cups via hydrodynamic deep drawing assisted by radial pressure. This study investigated the formation of such cups through Finite element (FE) simulation and experiments. First, the process was modeled numerically using ABAQUS FE software. After simulation, copper and St14 sheets were formed with water and oil as the forming media. The effect of these forming media on thickness distribution and maximum punch force was investigated. By examining the thickness distribution curve of the hydroformed cup, a close agreement was found between experimental and numerical results. Using oil as the forming media reduced thinning at the corner radius zone of the punch and increased the maximum punch force. Changing the forming media does not significantly influence the maximum thickening at the cup wall region.

     

Redrawing analysis of aluminum–stainless-steel laminated sheet using FEM simulations and experiments

The behavior of two-layer aluminum–stainless-steel (AL-SUS) laminated sheets during deep drawing, direct and reverse redrawing processes (first and second drawing stages), have been examined by simulations and laboratory experiments. For the simulation a rigid-plastic finite element program has been used. The results of simulations are presented as the variation of drawing ratios with respect to various thickness ratios and setting conditions. They show that to achieve the highest drawing ratios in direct and reverse redrawing, the thickness ratio should be about (one-layer aluminum and three-layer stainless-steel) and the setting conditions are opposite to each other. Considering the FEM results, laminated sheets with a thickness ratio of 71.3% aluminum and 28.7% stainless-steel were used to prepare deep drawing and redrawing experiments. The results of experiments are presented as the variation of thickness strain distribution in the drawn cup and punch load–stroke curves with respect to the setting condition. Results show that while in direct redrawing, contact of stainless-steel with the punch leads to the maximum drawing ratio, in reverse redrawing, aluminum should contact the punch in order to achieve the highest drawing ratio. An explanation for this finding is offered based on the thickness strain distribution, and punch load–stroke curves.     

Deep drawing of a thin-walled hemisphere

A model of deep drawing of a thin-walled hemisphere with a flat bottom from a plane blank by a rigid punch, considering the work hardening and wall thickness variation is developed. Elastic bending and von Mises membrane rigid-plastic strain with different friction coefficients at the punch and die contact boundaries are considered. The computational model determines the distribution of the wall thickness and the material work hardening along the shell generatrix, the drawing force versus punch displacement relation, and the critical parameters of the process in which some defects are probable.     

The application of abductive networks and FEM to predict the limiting drawing ratio in sheet metal forming processes

The deep drawing process, one of the sheet metal forming methods, is very useful in the industrial field because of its efficiency. The limiting drawing ratio (LDR) is affected by many material and process parameters, such as the strain-hardening exponent, the plastic strain ratio, friction and lubrication, the blank holder force, the presence of drawbeads, the profile radius of the die and punch, etc. In order to verify the finite element method (FEM) simulation results of the LDR, the experimental data are compared with the results of the current simulation. The influences of the process parameters such as the blank holder force, the profile radius of the die, the clearance between the punch and the die, and the friction coefficient on the LDR are also examined. The abductive network was then applied to synthesize the data sets obtained from the numerical simulation. The predicted results of the LDR from the prediction model are in good agreement with the results obtained from the FEM simulation. By employing the predictive model, it can provide valuable references to the prediction of the LDR under a suitable range of process parameters.     

The effect of die/blank holder and punch radiuses on limit drawing ratio in angular deep-drawing dies

Deep-drawing is one of the most important methods used to form sheet metal. The radius of die/blank holder and punch is important for deep-drawing dies because of an effective way to promote deep drawability sheet metal. This paper presents an attempt to determine the effect of various radiuses of die and punch on the limit drawing ratio and was investigated using DIN EN 10130–91 sheet metal. The die/blank holder profile with angles of α?=?0°, α?=?2.5°, α?=?7.5°, α?=?12.5°, α?=?15° and die/punch profile with radiuses for R?=?10, R?=?8, R?=?6, R?=?4 and R?=?0 mm were analyzed to determine the influence of punch force on the limit drawing ratio. The aim of this study is to investigate the effect of radius and angle variables on drawability in the deep-drawing process and to obtain useful data from the industrial field. The experiments show that the limit drawing ratio increased with increasing radius of punch (R), die/blank holder angle (α).     

Investigation of hydrodynamic deep drawing for conical�Ccylindrical cups

Forming conical parts is one of the complex and difficult fields in sheet-metal forming processes. Because of low-contact area of the sheet with punch tip in the initial stages of forming, bursting occurs on the sheet. Moreover, since most of the sheet surface in the area between the punch tip and blank holder is free, wrinkles appear on the wall of the drawing part. Thus, these parts are normally formed in industry by spinning, explosive forming, or multi-stage deep drawing processes. In this paper, forming pure copper and St14 conical?Ccylindrical cups in the hydrodynamic deep drawing process was studied using finite element (FE) simulation and experiment. The effect of pressure path on the occurrence of defects and thickness distribution and drawing ratio of the sheet was studied. It was concluded that at low pressures, bursting occurs on the contact area of sheet with punch tip. At higher pressures, the cup was formed, but the wall thickness distribution depends on the pressure path. It was also illustrated that for the pressure path with a certain maximum amount, the workpiece was formed adequately with minimum sheet thickness reduction. Internal pressures more than this maximum amounts did not affect on the thickness distribution. By applying the desired pressure path, conical?Ccylindrical cups with high deep drawing ratio were achieved.     

Study on process parameters and its analytic application for nonaxisymmetric rectangular cup of multistage deep drawing process using low carbon thin steel sheet

Multistage deep drawing process is widely used to obtain various nonaxisymmetric rectangular cups. This deep drawing scheme including drawing and ironing processes consists of several tool sets to carry out a continuous production within one progressive press. To achieve the successive production, design and fabrication of the necessary tools such as punch, die, and other auxiliary devices are critical, therefore, a series of process parameters play an important role in performing the process design. This study focuses on the tool design and modification for developing the rectangular cup with an aspect ratio of 5.7, using cold-rolled low carbon thin steel sheet with the initial thickness of 0.4 mm. Based on the design results for the process and the tools, finite element analysis for the multistage deep drawing process is performed with thickness control of the side wall in intermediate blanks as the first approach. From the results of the first approach, it is shown that the intermediate blanks could experience failures such as tearing, wrinkling, and earing by excessive thinning and thickening. To solve these failures, the modifications for the deep drawing punches are carried out, and the modified punches are applied to the same process. The simulation results for the multistage rectangular deep drawing process are compared with the thickness distributions before and after the punch shape modifications, and with the deformed shape in each intermediate blank, respectively. The results of finite element reanalysis using the modified punches show significant improvement compared with those by using the original designed punch shapes.     

Integration of artificial neural network and simulated annealing algorithm to optimize deep drawing process

Deep drawing is characterized by very complicated deformation affected by the process parameter values including die geometry, blank holder force, material properties, and frictional conditions. The aim of this study is to model and optimize the deep drawing process for stainless steel 304 (SUS304). To achieve the purpose, die radius, punch radius, blank holder force, and frictional conditions are designated as input parameters. Thinning, as one of the major failure modes in deep drawn parts, is considered as the process output parameter. Based on the results of finite element (FE) analysis, an artificial neural network (ANN) has been developed, as a predictor, to relate important process parameters to process output characteristics. The proposed feed forward back propagation ANN is trained and tested with pairs of input/output data obtained from FE analysis. To verify the FE model, the results obtained from the FE model were compared with those of several experimental tests. Afterward, the ANN is integrated into a simulated annealing algorithm to optimize the process parameters. Optimization results indicate that by selecting the proper process parameter settings, uniform wall thickness with minimum thinning can be achieved.