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CHAPTER 16

Process Modeling in Impression-DieForging Using Finite-Element Analysis

Manas ShirgaokarGracious NgaileGangshu Shen

16.1 Introduction

Development of finite-element (FE) processsimulation in forging started in the late 1970s.At that time, automatic remeshing was not avail-able, and therefore, a considerable amount oftime was needed to complete a simple FE simu-lation [Ngaile et al., 2002]. However, the devel-opment of remeshing methods and the advancesin computational technology have made the in-dustrial application of FE simulation practical.Commercial FE simulation software is gainingwide acceptance in the forging industry and isfast becoming an integral part of the forging de-sign and development process.

The main objectives of the numerical processdesign in forging are to [Vasquez et al., 1999]:

Develop adequate die design and establishprocess parameters by:a. Process simulation to assure die fillb. Preventing flow-induced defects such as

laps and cold shutsc. Predicting processing limits that should

not be exceeded so that internal and sur-face defects are avoided

d. Predicting temperatures so that part prop-

erties, friction conditions, and die wearcan be controlled

Improve part quality and complexity whilereducing manufacturing costs by:a. Predicting and improving grain flow and

microstructureb. Reducing die tryouts and lead times

c. Reducing rejects and improving materialyield

Predict forging load and energy as well astool stresses and temperatures so that:a. Premature tool failure can be avoided.b. The appropriate forging machines can be

selected for a given application.

Process modeling of closed-die forging usingfinite-element modeling (FEM) has been appliedin aerospace forging for a couple of decades[Howson et al., 1989, and Oh, 1982]. The goalof using computer modeling in closed-die forg-ing is rapid development of right-the-first-timeprocesses and to enhance the performance ofcomponents through better process understand-ing and control. In its earlier application, processmodeling helped die design engineers to pre-view the metal flow and possible defect forma-tion in a forging. After the forging simulation isdone, the contours of state variables, such as ef-fective strain, effective strain rate, and tempera-ture at any instant of time during a forging, canbe generated. The thermomechanical histories ofselected individual locations within a forgingcan also be tracked [Shen et al., 1993]. Thesefunctions of process modeling provided an in-sight into the forging process that was not avail-

able in the old days. Integrated with the processmodeling, microstructure modeling is a new areathat has a bright future [Sellars, 1990, and Shenet al., 2000]. Microstructure modeling allows theright-the-first-time optimum metallurgical fea-tures of the forging to be previewed on the com-puter. Metallurgical aspects of forging, such as

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194 / Cold and Hot Forging: Fundamentals and Applications

grain size and precipitation, can be predictedwith reasonable accuracy using computationaltools prior to committing the forging to shop tri-als. Some of the proven practical applications ofprocess simulation in closed-die forging include:

Design of forging sequences in cold, warm,and hot forging, including the prediction offorming forces, die stresses, and preformshapes

Prediction and optimization of flash dimen-sions in hot forging from billet or powdermetallurgy preforms

Prediction of die stresses, fracture, and diewear; improvement in process variables anddie design to reduce die failure

Prediction and elimination of failures, sur-face folds, or fractures as well as internalfractures

Investigation of the effect of friction onmetal flow

Prediction of microstructure and properties,elastic recovery, and residual stresses

16.2 Information Flow inProcess Modeling

It is a well-known fact that product designactivity represents only a small portion, 5 to15%, of the total production costs of a part.However, decisions made at the design stage de-termine the overall manufacturing, maintenance,

and support costs associated with the specificproduct. Once the part is designed for a specificprocess, the following steps lead to a rationalprocess design:

1. Establish a preliminary die design and selectprocess parameters by using experience-based knowledge.

2. Verify the initial design and process condi-tions using process modeling. For this pur-pose it is appropriate to use well-establishedcommercially available computer codes.

3. Modify die design and initial selection ofprocess variables, as needed, based on the re-

sults of process simulation.4. Complete the die design phase and manufac-

ture the dies.5. Conduct die tryouts on production equip-

ment.6. Modify die design and process conditions, if

necessary, to produce quality parts.

Hopefully, at this stage little or no modificationwill be necessary, since process modeling is ex-pected to be accurate and sufficient to make allthe necessary changes before manufacturing thedies.

Information flow in process modeling is

shown schematically in Fig. 16.1 [Shen et al.,2001]. The input of the geometric parameters,process parameters, and material parameters setsup a unique case of a closed-die forging. Themodeling is then performed to provide infor-mation on the metal flow and thermomechanicalhistory of the forging, the distribution of thestate variables at any stage of the forging, andthe equipment response during forging. The his-tories of the state variables, such as strain, strainrate, temperature, etc., are then input to the mi-crostructure model for microstructural featureprediction. All of the information generated isused for judging the closed-die forging case. The

nonsatisfaction in any of these areas will requirea new model with a set of modified process pa-rameters until the satisfied results are obtained.Then, the optimum process is selected for shoppractice.

16.3 Process Modeling Input

Preparing correct input for process modelingis very important. There is a saying in computermodeling: garbage in and garbage out. Some-times, a time-consuming process modeling isuseless because of a small error in input prepa-

ration. Process modeling input is discussed interms of geometric parameters, process param-eters, and material parameters [SFTC, 2002].

16.3.1 Geometric Parameters

The starting workpiece geometry and the diegeometry need to be defined in a closed-die forg-ing modeling. Depending on its geometricalcomplexity, a forging process can be simulatedeither as a two-dimensional, axisymmetric orplane-strain, or a three-dimensional problem. Ifthe process involves multiple stations, the diegeometry of each station needs to be provided.

A typical starting workpiece geometry for aclosed-die forging is a cylinder with or withoutchamfers. The diameter and the height of thecylinder are defined in the preprocessing stage.A lot of closed-die forgings are axisymmetric,which need a two-dimensional geometry han-dling. Boundary conditions on specific segments



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Process Modeling in Impression-Die Forging Using Finite-Element Analysis / 195

of the workpiece and dies that relate to defor-mation and heat transfer need to be defined. Forexample, for an axisymmetric cylinder to beforged in a pair of axisymmetric dies, the nodalvelocity in the direction perpendicular to thecenterline should be defined as zero, and the heat

flux in that direction should also be defined aszero.

16.3.2 Process Parameters

The typical process parameters to be consid-ered in a closed-die forging include [SFTC,2002]:

The environment temperature The workpiece temperature The die temperatures The coefficients of heat transfer between the

dies and the billet and the billet and the at-mosphere

The time used to transfer the workpiece fromthe furnace to the dies

The time needed to have the workpiece rest-ing on the bottom die

The workpiece and die interface heat-trans-fer coefficient during free resting

The workpiece and die interface heat-trans-fer coefficient during deformation

The workpiece and die interface friction, etc.

The die velocity is a very important parameterto be defined in the modeling of a closed-dieforging. If a hydraulic press is used, depending

on the actual die speed profiles, the die velocitycan be defined as a constant or series of veloc-ities that decrease during deformation. The ac-tual die speed recorded from the forging can alsobe used to define the die velocity profile. If amechanical press is used, the rpm of the fly-wheel, the press stroke, and the distance fromthe bottom dead center when the upper dietouches the part need to be defined. If a screwpress is used, the total energy, the efficiency, andthe ram displacement need to be defined. If ahammer is used, the blow energy, the blow ef-ficiency, the mass of the moving ram and die,the number of blows, and the time interval be-

tween blows must be defined. Forgings per-formed in different machines, with unique ve-locity versus stroke characteristics, have beensimulated successfully using the commercial FEsoftware DEFORM (Scientific Forming Tech-nologies Corp.) [SFTC, 2002].

Fig. 16.1 Flow chart of modeling of closed-die forging [Shen et al., 2001]



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16.3.3 Tool and WorkpieceMaterial Properties

In order to accurately predict the metal flowand forming loads, it is necessary to use reliableinput data. The stress-strain relation or flowcurve is generally obtained from a compression

test. However, the test is limited in achievablestrains. In order to obtain the flow stress at largestrains and strain rates, the torsion test can beused or, alternatively, the compression data isextrapolated with care.

In most simulations, the tools are consideredrigid; thus, die deformation and stresses are ne-glected. However, in precision forging opera-tions, the relatively small elastic deformations ofthe dies may influence the thermal and mechan-ical loading conditions and the contact stress dis-tribution at the die/workpiece interface. Thus,die stress analysis is a crucial part of processsimulation to verify the die design and the forg-

ing process parameters.

16.3.4 Interface Conditions(Friction and Heat Transfer)

The friction and heat-transfer conditions atthe interface between the die and the billet havea significant effect on the metal flow and theloads required to produce the part. In forgingsimulations, due to the high contact stresses atthe interface between the workpiece and the die,the constant shear friction factor gives better re-sults than the coulomb friction coefficient.

The most common way to determine the shear

friction factor in forging is to perform ring com-pression tests. From these tests, it is possible toestimate the heat-transfer coefficient, flow stressand friction as a function of temperature, strainrate, strain, and forming pressure, as discussedin Chapter 6, Temperatures and Heat Transfer.

Friction factors measured with the ring com-pression test, however, are not valid for preci-sion forging processes (hot, warm, and cold)where the interface pressure is very high and thesurface generation is large. The friction condi-tions change during the process due to changesin the lubricant and the temperature at the die/workpiece interface. In such applications, thedouble cup extrusion test is recommended forestimation of the friction factor, as discussed inChapter 7, Friction and Lubrication.

16.3.5 Material Parameters

The closed-die hot forging modeling is a cou-pled heat-transfer and deformation simulation.

Material parameters that relate to both heattransfer and deformation need to be defined. Thematerial parameters commonly used for heat-transfer modeling are the thermal conductivity,heat capacity, and emissivity of the workpieceand die materials. These parameters are usually

defined as a function of temperature, The flowstress of the workpiece material is very impor-tant for the correct prediction of metal flow be-havior. It is usually defined as a function ofstrain, strain rate, temperature, and possiblestarting microstructures. The Youngs modulus,the Poissons ratio as a function of temperature,and the thermal expansion of the die materialsare important parameters for die stress analysis.

16.4 Characteristics ofthe Simulation Code

16.4.1 Mesh Generation andAutomatic Remeshing

In forging processes, the workpiece generallyundergoes large plastic deformation, and therelative motion between the deforming materialand the die surface is significant. In the simu-lation of such processes, the starting mesh iswell defined and can have the desired mesh den-sity distribution. As the simulation progresses,the mesh tends to get distorted significantly.Hence, it is necessary to generate a new meshand interpolate the simulation data from the oldmesh to the new one to obtain accurate results.

Automated mesh generation (AMG) schemeshave been incorporated in commercial FE codesfor metal forming simulations. In DEFORM,there are two tasks in AMG: 1) determination ofoptimal mesh density distribution and 2) gen-eration of the FE mesh based on the given den-sity. The mesh density should conform to thegeometrical features of the workpiece at eachstep of deformation [Wu et al., 1992]. In orderto maximize the geometric conformity, it is nec-essary to consider mesh densities that take intoaccount the boundary curvature and local thick-ness.

In DEFORM, two-dimensional (2-D) simu-

lations use quadrilateral elements, whereasthree-dimensional (3-D) simulations use tetra-hedral elements for meshing and automatic re-meshing [Wu et al., 1996]. With this automaticremeshing capability, it is possible to set up asimulation model and run it to the end with verylittle interaction with the user.



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duces defects in the forging. In real closed-dieforging, it is necessary to wait until the forgingis finished to see the forged part and the defect,if there is one. The advantage of computer simu-lation of forging is that the entire forging processis stored in a database file in the computer and

can be tracked. Whether there is a defect formedand how it is formed can be previewed beforethe actual forging. Figure 16.2 shows the lap for-mation for a rejected process in the design stage.The lap formation can be eliminated by chang-ing the workpiece geometry (the billet or pre-form), or the die geometry, or both. The com-puter modeling can again indicate if thecorrective measure works or not.

16.5.2 Distribution andHistory of State Variables

The distribution of the state variables, such asthe strain, strain rate, and temperature, at anystage of a closed-die forging can be plotted fromthe database file saved for the forging simula-tion. The history of these state variables can alsobe tracked.

Figure 16.3(a) shows the effective strain dis-tribution of a closed-die forging forged in an iso-thermal press. The effective strain has a value of0.4 to 0.9 in the bore die lock region. The regionthat is in contact with the upper die has an ef-fective strain value of 0.4 to 0.9, and the regionthat is in contact with the lower die, a value of0.7 to 0.9. With an effective strain of 2.0 to 2.8,the bore rim transition region has the largeststrain. The effective strain value is approxi-mately 1.5 for both the rim and the midheight ofthe bore region. From the state variable distri-bution plot, the state variable at a specific stageof the forging is known. This specific stage,

Fig. 16.2 Lap prediction using process modeling tool

16.4.2 Reliability andComputational Time

Several FE simulation codes are commer-cially available for numerical simulation of forg-ing processes, such as DEFORM (2-D and 3-D),FORGE (2-D and 3-D) (Ternion Corp.), Qform

(2-D and 3-D), etc. In addition to a reliable FEsolver, the accurate and efficient use of metalflow simulations require [Knoerr et al., 1992]:

Interactive preprocessing to provide the userwith control over the initial geometry, meshgeneration, and input data; automatic re-meshing to allow the simulation to continuewhen the distortion of the old mesh is ex-cessive; interactive postprocessing that pro-vides more advanced data analysis, such aspoint tracking and flow line calculation

Appropriate input data describing the ther-mal and physical properties of die and billetmaterial the heat transfer and friction at thedie/workpiece interface under the processingconditions investigated, and the flow behav-ior of the deforming material at the relativelylarge strains that occur in practical forgingoperations

Analysis capabilities that are able to performthe process simulation with rigid dies to re-duce calculation time and to use contactstresses and temperature distribution esti-mated with the process simulation usingrigid dies to perform elastic-plastic die stressanalysis

The time required to run a simulation dependson the computer used and the amount of memoryand workload the computer has. However, withtodays computers, it is possible to run a 2-Dsimulation in a couple of hours, while a 3-Dsimulation can take anywhere between a day toa week, depending on the part complexity [Wuet al., 1996].

16.5 Process Modeling Output

The process modeling provides extensive in-formation of the forging process. The output ofprocess modeling can be discussed in terms ofthe metal flow, the distribution and history ofstate variables, the equipment response duringforging, and the microstructure of the forging.

16.5.1 Metal Flow

The information on metal flow is very impor-tant for die design. Improper metal flow pro-



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shown in Fig. 16.3(a), is the end of the forging.The distribution of the state variables can beplotted for any other stages of forging as well.

Figure 16.3(b) shows the effective strain ver-sus time of a material point located at midheightof the bore section of the forging, as shown in

Fig. 16.3(a). In this isothermal forging case, a20 min deformation time was used, as shown in

the figure. The final strain value, 1.5, shown inFig. 16.3(b) is in agreement with the valueshown in the distribution plot in Fig. 16.3(a).The history plot of state variables (strain, strainrate, and temperature) provides valuable infor-mation on the thermomechanical history of the

forging that determines its mechanical proper-ties.

16.5.3 Equipment Response/HammerForging

Process modeling also provides the informa-tion regarding the response of the equipment.Examples of equipment response discussed hereare forging load and ram velocity of hammerforging. The information is usually not availablein the hammer shop. However, it is useful forunderstanding the hammer response to a forgingprocess.

Figure 16.4 shows the load versus stroke pre-dicted for a hammer forging operation. The fig-ure shows that there are eight blows in the ham-mer operation. Each ends with a zero load. Thestroke in the figure is the stroke of the ram/die.The zero stroke refers to the position of the die,where the first die/workpiece contact occurs dur-ing forging. This zero position is the same forall of the eight hammer blows. With the increasein the number of blows, the load increases andthe stroke per blow decreases. The last blow ofthe sequence has the shortest stroke. This be-havior is very real for hammer forging opera-tions. During a hammer forging operation, the

workpiece increases its contact area with thedies, which increases the forging load. The totalavailable blow energy is fixed for a hammer.With the increase in forging load, the length of

Fig. 16.4 Load versus stroke obtained from a hammer forg-ing simulation

Fig. 16.5 Ram velocity versus stroke obtained from a ham-mer forging simulation

Fig. 16.3 (a) Effective strain distribution and (b) the effectivestrain history of the center location of a closed-die

forging



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stroke is reduced. Moreover, the blow efficiency,which is the ratio between the energy used fordeformation and the total blow energy, is also

reduced with the increase in forging load. Thus,a smaller amount of energy is available towardthe end of a blow sequence and with the de-crease in the stroke per blow.

Figure 16.5 gives the ram velocity versusstroke obtained from a simulation of another

hammer forging process. There are nine blowsfor this hammer operation. The velocity of thefirst blow was smaller than the other eight blows,because a soft blow was used initially to locatethe workpiece. In a soft blow, there is only aportion of blow energy applied to the workpiece.Thus, the first blow has a smaller starting ramvelocity. After the first blow, full energy was ap-plied to the forging. Thus, the starting ram ve-

Fig. 16.8 Rene 88 experimental part out of forging press[Hardwicke et al., 2000]

Fig. 16.7 Comparisons of hot-die forging and mechanicalpress forging of an experimental part using process

modeling

Fig. 16.9 Predicted model and optically measured grainsizes in t he three developmental Rene 88DT disks

with (a) coarse, (b) medium, and (c) fine grains [Hardwicke et al.,2000]

Fig. 16.6 Prediction of the distribution of the size (lm) ofgamma prime for a Rene 88 experimental forging



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locity for the rest of the blows was the same.There is always an energy loss to surroundingsin a hammer blow. Therefore, blow efficiencyneeds to be factored in for each hammer blow.However, the blow efficiency only has an effectafter the ram/die workpiece are in contact.

Hence, blow efficiency does not influence thestarting velocity of the ram/die. It is factored induring the blow. The decay in ram velocity ineach blow is a result of both the energy con-sumption in deforming the workpiece and theenergy lost to the surroundings.

16.5.4 Microstructures in Superalloys

Microstructure and property modeling is nowthe major emphasis in advanced forging processdesign and improvement, especially in forgingaerospace alloys such as nickel and titanium su-peralloys. The development and utilization of

physical metallurgy-based microstructure mod-els and the integration of the models with finite-element analysis has allowed for microstructureprediction by computer. Two important micro-structural features of superalloy forgings are thegrain size and the gamma-prime precipitation.The grain size modeling is discussed in detail inChapter 19, Microstructure Modeling in Su-peralloy Forging. The prediction of gamma-prime distribution is discussed here. Gammaprime is a very important precipitation phase in

strengthening superalloys. The size and spacingare two features of interest in gamma-prime pre-cipitation. Figure 16.6 shows the prediction ofthe distribution of the size of gamma prime ofan experimental nickel-base superalloy forging,Rene 88, coupled with a few measurement

points. The measurement made is in the rangeof 0.07 to 0.21 lm. The model predicts a rangeof 0.08 to 0.14 lm. The fine gamma prime wascorrectly predicted and the coarser gamma primewas underpredicted, which pointed out the needfor further improvement of the gamma-primemodel. The microstructure prediction feature isuseful for the process development for closed-die forging.

16.6 Examples ofModeling Applications

One of the major concerns in the research ofmanufacturing processes is to find the optimumproduction conditions in order to reduce pro-duction costs and lead-time. In order to optimizea process, the effect of the most important pro-cess parameters has to be investigated. Con-ducting experiments can be a very time-consum-ing and expensive process. It is possible toreduce the number of necessary experiments byusing FEM-based simulation of metal formingprocesses.

Fig. 16.10 Investigation of defects in ring gear forging using FEM [Jenkins et al., 1989]



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