Numerical Prediction of Microstructure and Mechanical Properties During the Hot Stamping Process Dongbin Kan, Lizhong Liu, Ping Hu, Ning Ma, Guozhe Shen et al. Citation: AIP Conf. Proc. 1383, 602 (2011); doi: 10.1063/1.3623663 View online: http://dx.doi.org/10.1063/1.3623663 View Table of Contents: http://proceedings.aip.org/dbt/dbt.jsp?KEY=APCPCS&Volume=1383&Issue=1 Published by the American Institute of Physics.
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Numerical Prediction of Microstructure and Mechanical Properties During the Hot Stamping Process Dongbin Kan, Lizhong Liu, Ping Hu , Ning Ma, Guozhe ShenˈXiaoqiang Han, Liang Ying State Key Laboratory of Structural Analysis for Industrial Equipment, School of Automotive Engineering, Dalian University of Technology, Dalian 116024, P.R.China Abstract. Numerical simulation and prediction of microstructures and mechanical properties of products is very important in product development of hot stamping parts. With this method we can easily design changes of hot stamping products’ properties prior to the manufacturing stage and this offers noticeable time and cost savings. In the present work, the hot stamping process of a U-channel with 22MnB5 boron steels is simulated by using a coupled thermo-mechanical FEM program. Then with the temperature evolution results obtained from the simulation, a model is applied to predict the microstructure evolution during the hot stamping process and mechanical properties of this U-channel. The model consists of a phase transformation model and a mechanical properties prediction model. The phase transformation model which is proposed by Li et al is used to predict the austenite decomposition into ferrite, pearlite, and bainite during the cooling process. The diffusionless austenite-martensite transformation is modeled using the Koistinen and Marburger relation. The mechanical properties prediction model is applied to predict the products’ hardness distribution. The numerical simulation is evaluated by comparing simulation results with the U-channel hot stamping experiment. The numerically obtained temperature history is basically in agreement with corresponding experimental observation. The evaluation indicates the feasibility of this set of methods to be used to guide the optimization of hot stamping process parameters and the design of hot stamping tools. Keywords: Hot stamping, Numerical simulation, 22MnB5 steel, Phase transformations, Hardness estimation. PACS: 81.05.Bx, 81.20.Hy
1. INTRODUCTION In order to reduce the weight of automobiles and improve the crash safety, more and more high-strength or ultrahigh-strength steel components are used on the vehicles. Conventional cold stamping of high-strength steel leads to disadvantages such as low formability and sever springback, so hot stamping technology is consequently developed for quenchenable boron steels such as 22MnB5. However, there are many process parameters in the hot stamping process and the tools need to be designed with cooling system, these make the design of hot stamping process and tools really complex. And this problem will become much knottier when we design hot stamping products with tailored properties. Numerical simulation and prediction of microstructures and mechanical properties of products can solve these problems mentioned above. Through this method, we can simply modify hot stamping technical process parameters and the design of tools before the manufacturing stage until the mechanical properties of the hot stamping products meet our expectation. In other words, the hot stamping process can be controlled precisely and quantitatively. This will greatly reduce the development costs and shorten the research cycle time. In this paper, the hot stamping process of a U-channel with 22MnB5 boron steels was simulated by using FE program LS-DYNA. Then with the temperature evolution results obtained from the simulation, models proposed by Li et al [1] were applied to predict the microstructure evolution during the hot stamping process and final
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The 8th International Conference and Workshop on Numerical Simulation of 3D Sheet Metal Forming Processes AIP Conf. Proc. 1383, 602-609 (2011); doi: 10.1063/1.3623663 © 2011 American Institute of Physics 978-0-7354-0949-1/$30.00
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mechanical properties of this U-channel. The hot stamping experiment was designed to evaluate the prediction method.
2. EXPERIMENTAL SET-UP The material studied is a low alloyed boron steel called 22MnB5 with a thickness of 1.6mm. The chemical composition of this material is given in Table 1. The tests were performed in a hydraulic press machine. The tool consists of a punch and a die, both with cooling systems, Fig. 1. In the experiment, the blank was heated in a furnace to 950ć, and held for 5 minutes. After having a homogeneous austenitic microstructure, the blank was transferred to the water cooled tools where stamping and quenching took place simultaneously. After deformation, the hot blank remained in the tool to be cooled for 8s. Figure 2 shows the dimensions of the rectangular blank and the location of the temperature measurement point. The temperature evolution of the blank during the whole process was measured at the point on the sheet surface, using type K thermo-couple. C (%) 0.22–0.25
TABLE 1. Chemical composition of the 22MnB5 steel given in wt.% B (%) Si (%) Mn (%) Cr (%) P (%) S (%) 0.002-0.005 0.2–0.3 1.2–1.4 0.11-0.2 0–0.02 0–0.005
Al (%) 0.02-0.05
FIGURE 1. Illustration of hot stamping tools with cooling system.
FIGURE 2. Dimensions of the blank and location of the temperature measurement point, all dimensions are given in mm.
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3. FE MODELING The FE-code LS-DYNA has been used in this simulation. The modeled punch, die and blank as shown in Fig. 3 are corresponding to the experimental set-up. Only half of the blank and tools is modeled and symmetry constraints are applied. The simulation model consists of 9760 Belytschko-Tsay shell elements, with 5 through thickness integration points. Set TSHELL to 1 in the keyword *control_shellˈso that in the thermal calculations of the blank, the shells will be treated as 12-node brick elements to accurately calculate the through thickness temperature gradient, which is very important in the simulation of hot stamping process [2,3]. Material model 106 [3] which is a thermal elastic visco-plastic material model is used. Stress versus strain data is taken from the Numisheet 2008 BM03 benchmark specification [4]. Fig. 4 shows stress-strain curves for 22MnB5 steel for different temperatures from 500ć to 800ć at a strain rate of 0.1s-1. Data for Young’s modulus, Poisson’s ratios, thermal conductivity and heat capacity of the blank as a function of temperature are taken from the literature [5]. And the heat capacity and thermal conductivity for the material used in the tools are extracted from [6].
FIGURE 3. FE-model of blank and tools.
FIGURE 4. Stress–strain curves at temperatures of 800–550ć at a strain rate of 0.1 s1.
A convection heat transfer coefficient of 8 Wm-2K-1 is used and the radiation heat transfer coefficient is set to107 Wm-2K-1. Thermal boundary conditions are turned off for areas in contact with tools. The contact heat transfer coefficient which has a strong dependency on the contact pressure is a most critical parameter controlling cooling of
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the blank. In this work, the contact heat transfer coefficient is defined as a function of contact pressure, and the data given in the Numisheet 2008 BM03 benchmark specification [4] is used. The frictional coefficients is defined as a function of temperature, the data is extract from [7]. According to the experiment, temperature of the blank at the beginning of the die movement is set to 810ć, and the tools are held at 75ć. The punch is given a velocity of 70mm/s which is the actual punch velocity in the experiment. After the stamping procedure, the blank is held in the tools for 8s for the quenching.
4. MODELING OF MICROSTRUCTURAL EVOLUTION The numerical prediction model of microstructure and mechanical properties consists of a phase transformation model to predict the austenite decomposition into ferrite, pearlite, bainite and martensite during the cooling process, and a mechanical properties prediction model to predict the products’ final hardness distribution.
4.1 Phase Transformation Model The diffusion controlled transformations of austenite decomposition into ferrite, pearlite, and bainite for the isothermal condition is described by a set of reaction kinetics equations developed by Li et al [1]. The austenite to ferrite reaction equation is given as:
20.41G Ae3 T exp 27500 RT X F 0.4 1 X F 1 X F 3
dX F dt where
0.4 X F
(1)
FC exp 1.00 6.31C 1.78Mn 0.31Si 1.12 Ni 2.70Cr 4.06 Mo
FC
For the pearlite reaction, the equation is stated as:
20.32G Ae1 T exp 27500 RT X P 0.4 1 3
dX P dt
XP
1 X P
0.4 X P
PC
where
(2)
exp 4.25 4.12C 4.36Mn 0.44Si 1.71Ni 3.33Cr 5.19 Mo
PC
And the bainite reaction equation is expressed as:
dX B dt where
BC
20.29G Bs T exp 27500 RT X B 0.4 1 2
XB
1 X B
0.4 X B
BC
(3)
exp 10.23 10.18C 0.85Mn 0.55 Ni 0.90Cr 0.36 Mo
In Equation (1)-(3), G is the ASTM grain size number for the austenite; Ae3 , Ae1are the critical temperatures from the Fe–C equilibrium diagram; Bs is the bainite start temperature; R is the universal gas constant, T is the current temperature; XF , XP and XB are the volume fraction of ferrite, pearlite and bainite, respectively. The diffusionless austenite-martensite transformation is modeled using the Koistinen and Marburger relation [8], which is expressed as
Xm
X J (1- e-D ( Ms -T ) )
(4)
where Xm is the volume fraction of martensite, X¤ is the volume fraction of austenite available for the reaction, ¢is a constant coefficient set to 0.011, Ms is the martensite start temperature, and T is the current temperature.
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4.2 Mechanical Properties Prediction Model The mechanical properties prediction model is given as [1]:
Hv
X M HvM X B HvB X F X P HvF P
(5)
where XM, XB, XF and XP are the volume fraction of martensite, bainite, ferrite, and pearlite, respectively; and HvM, HvB and HvF+P are the Vickers hardness of martensite, bainite, and the mixture of ferrite and pearlite, respectively. For the calculation of HvM, HvB and HvF+P, empirically based expressions suggested by Maynier et al. [9] are used:
HvM HvB
127 949C 27 Si 11Mn 8 Ni 16Cr 21log Vr
323 185C 330 Si 153Mn 65 Ni 144Cr 191Mo
89 53C 55Si 22Mn 10 Ni 20Cr 33Mo log Vr HvF P
42 223C 53Si 30 Mn 12.6 Ni 7Cr 19 Mo
10 19Si 4 Ni 8Cr 130V log Vr
(6)
(7)
(8)
where Vr is the cooling rate at 70Wć (ć/s).
4.3 Prediction Process The kinetics equations are solved by the RUNGE_KUTTA method. With the application of the additivity rule, Equation (1)-(3) can be used to computing phase transformations under continuous cooling conditions. A fourth order polynomial is used to fit the temperature history curves obtained from the simulation. And then the coefficients of the polynomial are used as initial data in the prediction program. Fig. 5 shows a schematic structure for the prediction program.
FIGURE 5. Schematic structure for the prediction program.
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5. RESULTS AND DISCUSSION 5.1 Temperature History The measured and calculated temperature history of the point (Fig. 2) is compared in Fig. 6. In the experiment, the hot blank is transferred from the furnace to the tools manually. The blank is air cooled to 810ć when the stamping begins. As it is illustrated in Fig. 6, the calculated temperature history is in good agreement with the measured data. The cooling rate of the point is higher than 150ć/s. However, deviation appears in the last few seconds of the quenching process. The main reasons can be summarized as follows: 1) The tools were held at 75ć in the simulation, which is not in correspondence with the actual situation. 2) The phase transformation latent heat is not considered in the simulation.
FIGURE 6. Measured and calculated temperatures for the point defined in Fig. 2.
冲压结束时刻，U形件的下圆角区 域温度最低。这是因为冲压过程 中，该区域始终与凸模圆角接触， 两者之间几乎没有发现相对运动， 接触压力大，传热效率高。而由于 U形件的底部及法兰区最后才与模 具接触，这些区域温度相对较高。
FIGURE 7. Calculated temperature distribution of the blank after the stamping process.
Figure 7 shows the calculated temperature distribution of the blank just after the stamping process. As we can see from the picture, the lowest temperature is found at the lower fillet of the U-channel. The blank material at this area keeps in contact with the fillet of the punch during the whole stamping process, almost no relative sliding is observed. Thus the contact heat transfer is high efficient because of the high contact pressure. The highest temperature value can be observed in the bottom and flange area, because these areas are the last ones which come in contact with the tools. Figure 8 shows the temperature evolution of four representative nodes (Fig.7) during the whole process. The average cooling rate (between 800 and 300ć) is the highest around the lower fillet of the U-
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channel, about 340ć/s. While the average cooling rate is the lowest at the bottom area and side wall, about 120ć/s. These can be also attributed to the contact conditions at these areas.
图为4个典型代表点整个热成形过 程中的降温曲线，U形件下圆角区 域平均冷却速率（800-300）最 高，为340。U形件侧壁及底部区 域冷却速率较低，在120左右。
FIGURE 8. Temperature evolution of four representative nodes illustrated in Fig.7 during the whole process.
5.2 Microstructure and Hardness The hot stamped U-channel can be divided into five parts: the flange area, the side wall, the bottom area, the upper fillet and the lower fillet. Prediction of microstructures and hardness is conducted by the developed program for selected points from each area. Location of selected points and prediction results are shown in the unfolding drawing of the U-channel FE model, illustrated as Fig.9. The cooling rates at these points are always high enough to drive the martensite transformation, so all the predicted values of martensite volume fraction are above 95%, and the values of hardness above 490. While at the upper and lower fillet, the values of martensite volume fraction and hardness are a little higher than those at other areas, because the cooling rate is higher at the fillet than at other areas.
. FIGURE 9. Location of selected points, predicted Martensite volume fraction (%) and Vickers Hardness(within parenthesis).
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6. CONCLUSIONS The simulation of hot stamping process of a U-channel component with 22MnB5 boron steels was conducted by using FE program LS-DYNA. In addition, a program was developed to predict the final microstructure and mechanical properties. The temperature history of the blank from calculated and measured results are basically in agreement, which indicates that this set of simulation is feasible. According to the simulation, the lower fillet of blank and the fillet of punch keep in contact with high pressure during the stamping process. As a result, the cooling rate is higher at this area, and the predicted values of martensite volume fraction and hardness are a little higher than those at other areas.
ACKNOWLEDGMENTS This work was funded by the Key Project of the National Natural Science Foundation of China (No. 10932003), “973” National Basic Research Project of China (No. 2010CB832700), “863” Project of China (No. 2009AA04Z101) and the Fundamental Research Funds for the Central Universities(No. 893324, DUT11ZD202). These supports are gratefully acknowledged. Many thanks are due to the referees for their valuable comments.
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