international journal of mechanical engineering and...

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –

6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 1, January- April (2012), © IAEME

204

SIMULATION AND OPTIMIZATION OF MATERIAL

FLOW FORGING DEFECTS IN AUTOMOBILE COMPONENT

AND REMEDIAL MEASURES USING DEFORM SOFTWARE

Piyush Gulati1, Rajesh Kanda

2, JaiInder Preet Singh

3, Manjinder Bajwa

4

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: [email protected]

ABSTRACT

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

INTERNATIONAL JOURNAL OF MECHANICAL

ENGINEERING AND TECHNOLOGY (IJMET)

ISSN 0976 – 6340 (Print)

ISSN 0976 – 6359 (Online)

Volume 3, Issue 1, January- April (2012), pp. 204-216

© IAEME: www.iaeme.com/ijmet.html

Journal Impact Factor (2011): 1.2083 (Calculated by GISI)

www.jifactor.com

IJMET

© I A E M E



205

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 two-

stage 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.



206

Fig. 1 Methodology of the process followed in the research

Continue Simulation

Viewing of results in the

post-processor module of

DEFORM 3D software

A

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.

Modeling of dies in Pro-e 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.

No

Yes

Is material

flow uniform?

A



207

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

DEFORMTM

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

12000 elements.

Fig. 2 Simulation of the forging process Fig. 3 Meshing of the billet



208

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



209

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 154.5 mm length. The volume of the workpiece is 407507 mm3. 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.

4.1.1 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 Fig. 7 Simulation of the end plate

within the Range (X= -195mm, Y=170mm) Range (X= -195mm, Y=175mm)



210


within the Range (X= -190mm, Y=170mm) Range (X= -190mm, Y=175mm)


Range (X= -190mm, Y=180mm) 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:



211

Fig. 12 Simulation of the end plate beyond the Fig. 13 Simulation of the end plate

beyond Range (X= -190mm, Y=180mm) 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

S No.

Position along axis (mm)

Observation X Y

1. -195 170 Completely Filled








9. -175 165 Partially Unfilled

10. -200 165 Partially Unfilled



212

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



213

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

1. 1200 Completely Filled

2. 1000 Partially Unfilled

3. 900 Partially Unfilled

4.1.3 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) -195 -190 -185

Y (mm) 170 175 180

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)

1. -195 170

2. -195 175

3. -195 180

4. -190 170

5. -190 175

6. -190 180

7. -185 170

8. -185 175

9. -185 180



214

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

4.55813*107 2 22790700 8.58 10.1040523

2. B: Position

along

Y-axis

1.58353*108 2 79176400 29.82 35.1022679

3. Interaction 1.99399*108 4 49849700 18.78 44.2009758

4. Error 4.77864*107 18 2654800 10.592859

5. Total 4.51119*108 26 100

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

Yu = b0 + Σ bi xi + Σ bii xi2 + Σ bij xi xj (1)

i=1 i=1 i < j

where Xi (i = 1,2,--------k) are coded levels of K quantitative variables and b0, b1------,

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^

= (XTX)

T X

TY (2)



215

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

Coefficients

b0 b1 b2 b11 b22 b12

Value 1.3622*104 0.2915*10

4 0.1496*10

4 -0.0952*10

4 0.0938*10

4 -0.2219*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 = 1.3622*104 + 0.2915*10

4 x1 + 0.1496*10

4 x2 - 0.0952*10

4 x12 + 0.0938*10

4 x22 -

0.2219*104 x1x2 (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.

Fig 17 Comparison of results of simulation and Mathematical Model

5. CONCLUSION

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



216

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) 1425–1433. 3. 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, DEFORMTM

3D Version 6.1.

6. Harshil Parikh, Bhavin Mehta, Jay Gunasekera, Forging process Analysis and

Preform Design, Ohio University Athens 45701 Rob Mayer, President Queen City

Forging Company, Cincinnati, Ohio 45226.

7. 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. 2007.

11. 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 2007.

international journal of mechanical engineering and...

Documents