INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING AND TECHNOLOGY (IJMET)

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1 INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING AND TECHNOLOGY (IJMET) International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 ISSN (Print) ISSN (Online) Volume 3, Issue 1, January- April (2012), pp IAEME: Journal Impact Factor (2011): (Calculated by GISI) IJMET I A E M E SIMULATION AND OPTIMIZATION OF MATERIAL FLOW FORGING DEFECTS IN AUTOMOBILE COMPONENT AND REMEDIAL MEASURES USING DEFORM SOFTWARE Piyush Gulati 1, Rajesh Kanda 2, JaiInder Preet Singh 3, Manjinder Bajwa 4 ABSTRACT 1, 3, 4 Assistant Prof., Department of Mechanical Engineering, Lovely Professional University, Jalandhar, Punjab, India 2 Assistant Prof., Department of Mechanical Engineering, PEC University of Technology, Chandigarh, India Corresponding Contact: piyushgulati@gmail.com There are many types of defects like pitting, cracks, folds or laps, unfilling and size variations prevalent in forging process. This paper presents the analyses of material flow related defects with the aim of solving them using DEFORM 3D software. The main focus is on the positioning of the billet to be kept on the bottom die and its temperature limit to prevent the defects. A range for the positioning of the billet and temperature limit is proposed and it is found that if the billet is kept beyond that limit, it showed the defects of unfilling. The research is conducted on a ST 52/3 steel End plate used in automobiles, and the results of the simulation are correlated with the Statistical results. Key Words: DEFORM 3D, ST 52/3 Steel, Material flow, End Plate 1. INTRODUCTION The process of forging is concerned with the shaping of metals by the application of compressive forces. Forging is often classified according to the temperature at which it is performed: '"cold," "warm," or "hot" forging. Forged parts can range in weight from less than a kilogram to 170 metric tons. Forged parts usually require further processing to achieve a finished part. The main advantage of hot forging is that as the metal is deformed work hardening effects are negated by the recrystallization process [8]. Forged parts are stronger and tougher than cast or machined parts made from the same material 204

2 due to the reason that the hammering process arranges the micro-structure of the metal so that the crystal grains get aligned along the part profile. [7] Usually, the shapes of components manufactured by forging are complex; and many defects are induced during the process of forging such as: under filling, laps and folds. In the past, the problems were solved by seasoned technician with trial and error. Nowadays, the finite element method (FEM) has proven its efficiency and usefulness simulating steady and non-steady metal forming processes. Following the development of computer technology, the commercial based forging analysis software is gradually perfect. An algorithm for optimal design of non-isothermal metal forming processes has been presented. The methodology is applied to optimize the preform die shape in twostage forging and the initial temperature of the work-piece [2]. The authors have analyzed the changes of structure and temperature field in process of crankshaft forging, and the rules of metal flow are summarized, the defects formation and preventive actions were analyzed, and the shape of blank was optimized. The authors have discussed that the forging analysis model can minimize the testing requirements. [11] The authors have summarized the distribution of strains in the various regions of the part. This has been shown that friction and lubrication increases the amount of load required in the forging process [1]. The authors have been able to analyze the material flow of a forging component using DEFORM -2D. This has been shown that the material yield can be increased by developing a flash less version of the component using DEFORM-2D [3]. Simulation of stresses, strains and temperature at different regions have also been done for defect analysis [10] Simple model for heat transfer coefficient between work piece and dies have also been developed [9]. Authors have also used MSC Super Forge for simulation of the forging process [6]. Various authors have discussed about various factors related with FE techniques used for forging process. However, the issues related with positioning of the billet on the bottom die and the temperature limit for billet are not being addressed. The aim of this research is the analysis of the material flow defects like unfilling by taking into consideration, the above stated issues. This research would be beneficial in reducing the material flow defects. This paper has been divided into 6 sections. First section is the introduction about forging and various literature reviews are discussed. In the second section, the methodology being followed in the paper is being discussed. Third section discusses about the numerical simulation of the forging process. In the fourth section various results are discussed. Fifth section gives the conclusions of the research and sixth section is the references used for the current research. 2. METHODOLOGY This research begins with the modeling of the dies in the 3D modeling software Pro-e. The modeled drawings are then imported in.stl format in DEFORM 3D software. 205

3 Modeling of dies in Pro-e software Importing the modeled drawings into DEFORM 3D software in.stl Setting of all the input parameters in the pre -processor module of DEFORM 3D software. Positioning of the billet on the bottom die Starting the simulation in the Simulator module Checking the simulation for uniformity of material flow by giving a pause to the simulation. Is material flow uniform? No A Yes Continue Simulation Viewing of results in the post-processor module of DEFORM 3D software Fig. 1 Methodology of the process followed in the research 206

4 The dies are imported in the preprocessor module of DEFORM 3D software. In this module all the input parameters are provided. These input parameters include the material of the workpiece and the dies, object meshing, temperature range, friction coefficient, positioning of the workpiece. After inputting all the parameters, the simulation is started in the simulator module. The simulation can be paused in between and we can check whether the material flow is uniform in the die cavity or not. The results of the simulation are viewed in the post processor module of the software. Figure 1 shows the methodology followed in this research. Deform software also gives an option for fast solution processing. This product is a well-tested, industrial simulation engine with an interface that allows the user to make use of it to the fullest potential. 3. NUMERICAL SIMULATION 3.1 DEFORM 3D Software The forging process generally consists of heating the billet material to a specific temperature after which it is deformed plastically into certain shapes by applying compressive force on the work piece (billet). At the end of the deformation process, the shape of the die is acquired by the work piece and a desired geometry is obtained. This research tests the forging ability of 3D Forming software package called DEFORM 3D package. The forging problem analyzed in this paper is that of a ST 52/3 steel end plate which is used in automobile axles. In this paper various defects occurring in forging due to material flow like laps, unfilling are analyzed with the help of simulation using DEFORM TM 3D Version 6.1. Figure 2 shows the simulation of the forging process on a cylindrical billet and Figure 3 shows the meshing of the billet. The meshing of the billet is done by the software into elements. Fig. 2 Simulation of the forging process Fig. 3 Meshing of the billet 207

5 3.2 Simulation of the forging of End Plate As mentioned earlier, the process analyzed is that of an actual industrial production forging. This problem was provided by R.B. Forgings Pvt. Ltd. Punjab India. The defects are analyzed using an End Plate used in the axles of automobiles. Figure 4 shows the original end plate and Figure 5 shows the simulation of the end plate. The defect analysis of the end plate is done by performing a number of simulations on the HOT FORGING option in the software package DEFORM 3D v6.1 SP1. Different Simulations are carried out using different orientations (rotational and offset) of the work piece and optimization of the work piece is done by these simulations. The Simulations are carried out on the basis of change in position of the workpiece and the change in temperature of the workpiece. The logic behind the change in the position of the workpiece is that, there is a proper range of the Position along X-axis and Position along Y-axis (in mm) of the workpiece (initially to be kept between the dies). If the workpiece is kept beyond that range, then the defect of partial unfilling of the final component will occur. Different Simulations are also carried out at different temperatures (900 C, 1000 C, and 1200 C.) The objective of these simulations is to check for the optimum temperature for the forging process of this component. Before starting the forging process, the raw material (billets) is kept in the furnace at 1200 C for about one hour. Now if the billet is not kept in the furnace for proper time, i.e. if it is taken out of the furnace after 30 or 40 minutes, then the temperature in the workpiece upto the core does not reach 1200 C and if the workpiece is not heated (completely) upto an exact temperature, the material will cool down early during the forging process and will not properly fill in the die impression. Due to this there will be the defect of unfilling of the component. Due to the improper flow of the material, more material will be accumulated as required, at some place in the die cavities and this will create the final components of oversize. The results obtained by simulations are then validated statistically using Analysis of Variance in the Statistic module of MATLAB software. A Mathematical Model using Regression Coefficients is prepared and the results are compared. Fig. 4 End Plate Fig. 5 Simulation of End Plate 208

6 4. RESULTS AND DISCUSSIONS 4.1 Deform 3d Simulation Results In the DEFORM 3D Software package, the Simulations are done in the Hot Forging option. The billet taken is cylindrical as used in the industry, and its dimensions are 58 mm Diameter and mm length. The volume of the workpiece is mm 3. The billet material taken is AISI_1016 due to its close resemblance to ST 52/3 (used in industry for this component).the dies are modeled in Pro-e Modeling software and are imported as STL format to DEFORM 3D Simulation Results based on the change in position of the workpiece: After carrying out many simulations with different positions at which the workpiece is kept in between the dies, it was found that the complete cavity of the die is filled (i.e. without defect of unfilling) in the following range: Position along X-Axis = (-185 to -195 mm) (Translational) Position along Y-Axis = (170 to 180 mm) (Translational) The position along Z axis (Translational) and along X-axis (Rotational) and Z-axis (Rotational) are kept constant as 29mm, 90 and -22 respectively. Figure 6 to Figure 11 shows the results of simulation of the forging when the billet is kept in the defined range of positioning. These results show the complete filling of the die with billet material without any defect. Fig. 6 Simulation of the end plate within the within the Range (X= -195mm, Y=170mm) Fig. 7 Simulation of the end plate Range (X= -195mm, Y=175mm) 209

7 Fig. 8 Simulation of the end plate within the within the Range (X= -190mm, Y=170mm) Fig. 9 Simulation of the end plate Range (X= -190mm, Y=175mm) Fig. 10 Simulation of the end plate within the Range (X= -190mm, Y=180mm) Fig. 11 Simulation of the end plate within Range (X= -185mm, Y=175mm) Simulations are also carried out by keeping the billet beyond the range defined above. These simulations showed the defect of unfilling of the component. Figure 12 and Figure 13 shows the simulation of the forging keeping the billet beyond the range: 210

8 Fig. 12 Simulation of the end plate beyond the beyond Range (X= -190mm, Y=180mm) Fig. 13 Simulation of the end plate Range (X= -185mm, Y=175mm) Table 1 shows the results of simulation based on positioning of the workpiece on the bottom die during forging process Position along axis (mm) S No. Observation X Y Completely Filled Completely Filled Completely Filled Completely Filled Completely Filled Completely Filled Completely Filled Completely Filled Partially Unfilled Partially Unfilled 211

9 4.1.2 Simulations Results based on the different temperatures Simulations are carried out at different temperatures (1200 C, 1000 C, 900 C), keeping the other parameters constant and variations in the output are noticed. Figure 14 shows the simulation of the end plate at 1200 C, the result is completely filled forging. Figure 15 and 16 shows the unfilled components when the simulation was carried out at 1000 C and 900 C respectively. Fig. 14 Simulation of end plate at 1200 C Fig. 15 Simulation of end plate at 1000 C Fig. 16 Simulation of end plate at 900 C 212

10 Table 2 shows the results of simulation based on positioning of the workpiece on the bottom die during forging process. S No. Temperature ( C) Observations Completely Filled Partially Unfilled Partially Unfilled Design of Experiments Results F-test is based on F-distributions and is used to compare the variance of two independent samples or factors. This test is also used in the context of Analysis of Variance (ANOVA) for judging the significance of multiple correlation coefficients. [4] The operating variables considered in this experiment are Position along X axis (X) and Position along Y axis (Y). The output variable is the scrap volume. With each set of combinations of the operating parameters the resultant volume changes. Here we will optimize the output scrap volume. The scrap volume is calculated by subtracting the volume of the final component (end plate after trimming) from the total volume (i.e. volume of the end plate including that of the flash) Table 3 shows the different levels of the operating parameters (X & Y) Parameters Level 1 Level 2 Level 3 X (mm) Y (mm) Table 4 shows the set of combination of these parameters at which different experiments are performed. S No. Combinations of Parameters X (mm) Y (mm)

11 The values input parameters are input in the MATLAB software and it generated the results shown in Table 5. This ANOVA table gives the percentage contribution of the different parameters independently and their combined effect and the error. The results show that the %age contribution of the Position along X-axis is 10.10%, contribution of Position along Y-axis is 35.10% and the combined contribution of Position along X-axis and Position along Y-axis is 44.20% with 10.59% error. Table 5 shows the ANOVA results of Design of Experiments S No. Control Factor Sum of Squares Degree of freedom Variance F0 %age of Contribution 1. A: Position * along X-axis 2. B: Position along Y-axis * Interaction * Error * Total * The experimental work done to study the factorial effects is planned in accordance with the statistical techniques of the experimental design. With a well-designed experiment it is possible to determine accurately, with a much reduced effort the effect of change in any one variable of the process output (also known as response or yield) and the interaction effects between the different factors if any. If all the investigated factors are quantitative in nature, then it is possible to approximate the response Yu as a polynomial. The mathematical model is represented in equation number 1 as: k k Y u = b 0 + Σ b i x i + Σ b ii x 2 i + Σ b ij x i x j (1) i=1 i=1 i < j where Xi (i = 1,2, k) are coded levels of K quantitative variables and b0, b , etc are the least square estimates of the regression coefficients. The polynomial is also known as Regression function and the first term under the summation sign pertains to linear effect, the second term under the summation sign pertains to quadratic effects, and the third term pertains to interaction effects of the investigated parameters. The least square estimator of the regression coefficients is defined by equation 2 as: B^ = (X T X) T X T Y (2) 214

12 The values of Regression Coefficients are obtained by solving the above equations in MATLAB software. The values of Regression Coefficients for the given model are shown in table 6. Table 6 shows the values of regression coefficients calculated from MATLAB Regression b 0 b 1 b 2 b 11 b 22 b 12 Coefficients Value * * * * * *10 4 Using the values of regression coefficients, following mathematical model is prepared for the scrap volume. Then the results of the simulation and that of the mathematical model are compared for validation and the results relate closely. Equation 3 represents the mathematical model prepared for this research. Y = * *10 4 x *10 4 x *10 4 x *10 4 x *10 4 x 1 x 2 (3) Following Graph drawn in Microsoft Excel sheet describes the comparison between the experimental values of scrap volume, calculated by simulations and the values of scrap volume calculated by the mathematical model. 5. CONCLUSION Fig 17 Comparison of results of simulation and Mathematical Model This research is a step forward in finding a solution to the material flow related defects in the forging components. There are a lot of defects which are generally seen in the forging components as described in the earlier sections. 1. In this paper, the material flow related defects such as under fill, over sizing of the components has been analyzed. 2. An exact range has been defined for the positioning of the billet in between the dies. Positioning of the billet plays a very important role in controlling the 215

13 unfilling of the component. If the billet is placed outside this range, the defect of unfilling will occur. 3. Another way of controlling these defects is by checking the proper temperature of the billet. Generally, before the forging of the component, the billet is heated at 1200 C for about an hour. This is done to heat the billet upto the core so that, when load is applied by the forging press, the material flows easily inside the die cavity. As research on this subject is furthered, the actual effects of the input processes on the forging defects can be calculated. The use of Finite Element Software like the one used in this research for these practical problems will save a lot of time and money of the industry. In this software package, a lot of simulations have been done to reach at a final solution. But in actual practice, if the industry tries to reach at a optimum solution by performing different experiments on the presses, this will cost heavily to the industry and will also consume a lot of time. 6. REFERENCES 1. A.M. Jafarpour, A.S. Asl, R. Bihamta, Simulation and Studying of Conical Gears Forging, Trends in Applied Sciences Research, 2010 Academic Journals Inc. 2. Carlos C. Antonio, Catarina F. Castro, Lu_isa C. Sousa, Optimization of metal forming processes, Elsevier, Computers and Structures 82 (2004) Chris Wheelhouse, Dr Brian Miller, The Industrial Application of Forging Simulation At UEF Ltd., Confederation of British Metalforming Technical Conference. 4. Design and Analysis of Experiments by Douglas C. Montgomery, Arizona State University 5. Deform 3D Manual, DEFORM TM 3D Version Harshil Parikh, Bhavin Mehta, Jay Gunasekera, Forging process Analysis and Preform Design, Ohio University Athens Rob Mayer, President Queen City Forging Company, Cincinnati, Ohio Manufacturing Technology by P.N. Rao, Tata McGraw-Hill Publishing Company Ltd. 8. Mechanical Metallurgy by George E. Dieter, McGraw-Hill Book Company. 9. William R.D. Wilson, Steven R. Schmid, Jiying Liu, Advanced simulations for hot forging: heat transfer model for use with the finite element method, Journal of Materials Processing Technology (2004) 10. Zhang Z., Dai G., Wu S., Dong L., and Liu L. Simulation of 42CrMo steel billet upsetting and its defects analyses during forming process based on the software DEFORM-3D.Materials Science and Engineering ZHANG Ying-jian, HUI Wei-jun, DONG Han, Hot Forging Simulation Analysis and Application of Micro alloyed Steel Crankshaft, Proceedings of Sini-Swedish Structural Materials Symposium