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

CHAPTER 16

Manas Shirgaokar Gracious Ngaile Gangshu Shen

16.1 Introduction c. Reducing rejects and improving material yield Development of finite-element (FE) process ● Predict forging load and energy as well as simulation in forging started in the late 1970s. tool stresses and temperatures so that: At that time, automatic remeshing was not avail- a. Premature tool failure can be avoided. able, and therefore, a considerable amount of b. The appropriate forging machines can be time was needed to complete a simple FE simu- selected for a given application. lation [Ngaile et al., 2002]. However, the devel- Process modeling of closed-die forging using opment of remeshing methods and the advances finite-element modeling (FEM) has been applied in computational technology have made the in- in aerospace forging for a couple of decades dustrial application of FE simulation practical. [Howson et al., 1989, and Oh, 1982]. The goal Commercial FE simulation software is gaining of using computer modeling in closed-die forg- wide acceptance in the forging industry and is ing is rapid development of right-the-first-time fast becoming an integral part of the forging de- processes and to enhance the performance of sign and development process. components through better process understand- The main objectives of the numerical process ing and control. In its earlier application, process design in forging are to [Vasquez et al., 1999]: modeling helped die design engineers to pre- view the metal flow and possible defect forma- ● Develop adequate die design and establish tion in a forging. After the forging simulation is process parameters by: done, the contours of state variables, such as ef- a. Process simulation to assure die fill fective strain, effective strain rate, and tempera- b. Preventing flow-induced defects such as ture at any instant of time during a forging, can laps and cold shuts be generated. The thermomechanical histories of c. Predicting processing limits that should selected individual locations within a forging not be exceeded so that internal and sur- can also be tracked [Shen et al., 1993]. These face defects are avoided functions of process modeling provided an in- d. Predicting temperatures so that part prop- sight into the forging process that was not avail- erties, friction conditions, and die wear able in the old days. Integrated with the process can be controlled modeling, microstructure modeling is a new area ● Improve part quality and complexity while that has a bright future [Sellars, 1990, and Shen reducing manufacturing costs by: et al., 2000]. Microstructure modeling allows the a. Predicting and improving grain flow and right-the-first-time optimum metallurgical fea- microstructure tures of the forging to be previewed on the com- b. Reducing die tryouts and lead times puter. Metallurgical aspects of forging, such as © 2005 ASM International. All Rights Reserved. www.asminternational.org Cold and Hot Forging: Fundamentals and Applications (#05104G)

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grain size and precipitation, can be predicted Hopefully, at this stage little or no modification with reasonable accuracy using computational will be necessary, since process modeling is ex- tools prior to committing the forging to shop tri- pected to be accurate and sufficient to make all als. Some of the proven practical applications of the necessary changes before manufacturing the process simulation in closed-die forging include: dies. Information flow in process modeling is ● Design of forging sequences in cold, warm, shown schematically in Fig. 16.1 [Shen et al., and hot forging, including the prediction of 2001]. The input of the geometric parameters, forming forces, die stresses, and preform process parameters, and material parameters sets shapes up a unique case of a closed-die forging. The ● Prediction and optimization of flash dimen- modeling is then performed to provide infor- sions in hot forging from billet or powder mation on the metal flow and thermomechanical metallurgy preforms history of the forging, the distribution of the ● Prediction of die stresses, fracture, and die state variables at any stage of the forging, and wear; improvement in process variables and the equipment response during forging. The his- die design to reduce die failure tories of the state variables, such as strain, strain ● Prediction and elimination of failures, sur- rate, temperature, etc., are then input to the mi- face folds, or fractures as well as internal crostructure model for microstructural feature fractures prediction. All of the information generated is ● Investigation of the effect of friction on used for judging the closed-die forging case. The metal flow nonsatisfaction in any of these areas will require ● Prediction of microstructure and properties, a new model with a set of modified process pa- elastic recovery, and residual stresses rameters until the satisfied results are obtained. Then, the optimum process is selected for shop practice. 16.2 Information Flow in Process Modeling 16.3 Process Modeling Input

It is a well-known fact that product design Preparing correct input for process modeling activity represents only a small portion, 5 to is very important. There is a saying in computer 15%, of the total production costs of a part. modeling: garbage in and garbage out. Some- However, decisions made at the design stage de- times, a time-consuming process modeling is termine the overall manufacturing, maintenance, useless because of a small error in input prepa- and support costs associated with the specific ration. Process modeling input is discussed in product. Once the part is designed for a specific terms of geometric parameters, process param- process, the following steps lead to a rational eters, and material parameters [SFTC, 2002]. process design: 1. Establish a preliminary die design and select 16.3.1 Geometric Parameters process parameters by using experience- The starting workpiece geometry and the die based knowledge. geometry need to be defined in a closed-die forg- 2. Verify the initial design and process condi- ing modeling. Depending on its geometrical tions using process modeling. For this pur- complexity, a forging process can be simulated pose it is appropriate to use well-established either as a two-dimensional, axisymmetric or commercially available computer codes. plane-strain, or a three-dimensional problem. If 3. Modify die design and initial selection of the process involves multiple stations, the die process variables, as needed, based on the re- geometry of each station needs to be provided. sults of process simulation. A typical starting workpiece geometry for a 4. Complete the die design phase and manufac- closed-die forging is a cylinder with or without ture the dies. chamfers. The diameter and the height of the 5. Conduct die tryouts on production equip- cylinder are defined in the preprocessing stage. ment. A lot of closed-die forgings are axisymmetric, 6. Modify die design and process conditions, if which need a two-dimensional geometry han- necessary, to produce quality parts. dling. Boundary conditions on specific segments © 2005 ASM International. All Rights Reserved. www.asminternational.org Cold and Hot Forging: Fundamentals and Applications (#05104G)

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of the workpiece and dies that relate to defor- ● The workpiece and die interface heat-trans- mation and heat transfer need to be defined. For fer coefficient during deformation example, for an axisymmetric cylinder to be ● The workpiece and die interface friction, etc. forged in a pair of axisymmetric dies, the nodal The die velocity is a very important parameter velocity in the direction perpendicular to the to be defined in the modeling of a closed-die centerline should be defined as zero, and the heat forging. If a hydraulic press is used, depending flux in that direction should also be defined as on the actual die speed profiles, the die velocity zero. can be defined as a constant or series of veloc- ities that decrease during deformation. The ac- 16.3.2 Process Parameters tual die speed recorded from the forging can also The typical process parameters to be consid- be used to define the die velocity profile. If a ered in a closed-die forging include [SFTC, mechanical press is used, the rpm of the fly- 2002]: wheel, the press stroke, and the distance from the bottom dead center when the upper die ● The environment temperature touches the part need to be defined. If a screw ● The workpiece temperature press is used, the total energy, the efficiency, and ● The die temperatures the ram displacement need to be defined. If a ● The coefficients of heat transfer between the hammer is used, the blow energy, the blow ef- dies and the billet and the billet and the at- ficiency, the mass of the moving ram and die, mosphere the number of blows, and the time interval be- ● The time used to transfer the workpiece from tween blows must be defined. Forgings per- the furnace to the dies formed in different machines, with unique ve- ● The time needed to have the workpiece rest- locity versus stroke characteristics, have been ing on the bottom die simulated successfully using the commercial FE ● The workpiece and die interface heat-trans- software DEFORM (Scientific Forming Tech- fer coefficient during free resting nologies Corp.) [SFTC, 2002].

Fig. 16.1 Flow chart of modeling of closed-die forging [Shen et al., 2001] © 2005 ASM International. All Rights Reserved. www.asminternational.org Cold and Hot Forging: Fundamentals and Applications (#05104G)

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16.3.3 Tool and Workpiece Material parameters that relate to both heat Material Properties transfer and deformation need to be defined. The material parameters commonly used for heat- In order to accurately predict the metal flow transfer modeling are the thermal conductivity, and forming loads, it is necessary to use reliable heat capacity, and emissivity of the workpiece input data. The stress-strain relation or flow and die materials. These parameters are usually curve is generally obtained from a compression defined as a function of temperature, The flow test. However, the test is limited in achievable stress of the workpiece material is very impor- strains. In order to obtain the flow stress at large tant for the correct prediction of metal flow be- strains and strain rates, the torsion test can be havior. It is usually defined as a function of used or, alternatively, the compression data is strain, strain rate, temperature, and possible extrapolated with care. In most simulations, the tools are considered starting microstructures. The Young’s modulus, rigid; thus, die deformation and stresses are ne- the Poisson’s ratio as a function of temperature, glected. However, in precision forging opera- and the thermal expansion of the die materials tions, the relatively small elastic deformations of are important parameters for die stress analysis. the dies may influence the thermal and mechanical loading conditions and the contact stress distribution at the die/workpiece interface. Thus, 16.4 Characteristics of die stress analysis is a crucial part of process the Simulation Code simulation to verify the die design and the forging process parameters. 16.4.1 Mesh Generation and Automatic Remeshing 16.3.4 Interface Conditions In forging processes, the workpiece generally (Friction and Heat Transfer) undergoes large plastic deformation, and the The friction and heat-transfer conditions at relative motion between the deforming material the interface between the die and the billet have and the die surface is significant. In the simu- a significant effect on the metal flow and the lation of such processes, the starting mesh is loads required to produce the part. In forging well defined and can have the desired mesh den- simulations, due to the high contact stresses at sity distribution. As the simulation progresses, the interface between the workpiece and the die, the mesh tends to get distorted significantly. the constant shear friction factor gives better re- Hence, it is necessary to generate a new mesh sults than the coulomb friction coefficient. and interpolate the simulation data from the old The most common way to determine the shear mesh to the new one to obtain accurate results. friction factor in forging is to perform ring com- Automated mesh generation (AMG) schemes pression tests. From these tests, it is possible to have been incorporated in commercial FE codes estimate the heat-transfer coefficient, flow stress for metal forming simulations. In DEFORM, and friction as a function of temperature, strain there are two tasks in AMG: 1) determination of rate, strain, and forming pressure, as discussed optimal mesh density distribution and 2) gen- in Chapter 6, “Temperatures and Heat Transfer.” eration of the FE mesh based on the given den- Friction factors measured with the ring com- sity. The mesh density should conform to the pression test, however, are not valid for preci- geometrical features of the workpiece at each sion forging processes (hot, warm, and cold) step of deformation [Wu et al., 1992]. In order where the interface pressure is very high and the to maximize the geometric conformity, it is nec- surface generation is large. The friction condi- essary to consider mesh densities that take into tions change during the process due to changes account the boundary curvature and local thick- in the lubricant and the temperature at the die/ ness. workpiece interface. In such applications, the In DEFORM, two-dimensional (2-D) simu- double cup extrusion test is recommended for lations use quadrilateral elements, whereas estimation of the friction factor, as discussed in Chapter 7, “Friction and Lubrication.” three-dimensional (3-D) simulations use tetra- hedral elements for meshing and automatic remeshing [Wu et al., 1996]. With this automatic 16.3.5 Material Parameters remeshing capability, it is possible to set up a The closed-die hot forging modeling is a cou- simulation model and run it to the end with very pled heat-transfer and deformation simulation. little interaction with the user. © 2005 ASM International. All Rights Reserved. www.asminternational.org Cold and Hot Forging: Fundamentals and Applications (#05104G)

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16.4.2 Reliability and duces defects in the forging. In real closed-die Computational Time forging, it is necessary to wait until the forging is finished to see the forged part and the defect, Several FE simulation codes are commer- if there is one. The advantage of computer simu- cially available for numerical simulation of forg- lation of forging is that the entire forging process ing processes, such as DEFORM (2-D and 3-D), is stored in a database file in the computer and FORGE (2-D and 3-D) (Ternion Corp.), Qform can be tracked. Whether there is a defect formed (2-D and 3-D), etc. In addition to a reliable FE solver, the accurate and efficient use of metal and how it is formed can be previewed before flow simulations require [Knoerr et al., 1992]: the actual forging. Figure 16.2 shows the lap formation for a rejected process in the design stage. ● Interactive preprocessing to provide the user The lap formation can be eliminated by chang- with control over the initial geometry, mesh ing the workpiece geometry (the billet or pre- generation, and input data; automatic re- form), or the die geometry, or both. The com- meshing to allow the simulation to continue puter modeling can again indicate if the when the distortion of the old mesh is ex- corrective measure works or not. cessive; interactive postprocessing that provides more advanced data analysis, such as 16.5.2 Distribution and point tracking and flow line calculation ● Appropriate input data describing the ther- History of State Variables mal and physical properties of die and billet The distribution of the state variables, such as material the heat transfer and friction at the the strain, strain rate, and temperature, at any die/workpiece interface under the processing stage of a closed-die forging can be plotted from conditions investigated, and the flow behav- the database file saved for the forging simula- ior of the deforming material at the relatively tion. The history of these state variables can also large strains that occur in practical forging be tracked. operations Figure 16.3(a) shows the effective strain dis- ● Analysis capabilities that are able to perform tribution of a closed-die forging forged in an iso- the process simulation with rigid dies to re- thermal press. The effective strain has a value of duce calculation time and to use contact 0.4 to 0.9 in the bore die lock region. The region stresses and temperature distribution esti- that is in contact with the upper die has an ef- mated with the process simulation using fective strain value of 0.4 to 0.9, and the region rigid dies to perform elastic-plastic die stress that is in contact with the lower die, a value of analysis 0.7 to 0.9. With an effective strain of 2.0 to 2.8, The time required to run a simulation depends the bore rim transition region has the largest on the computer used and the amount of memory strain. The effective strain value is approxi- and workload the computer has. However, with mately 1.5 for both the rim and the midheight of today’s computers, it is possible to run a 2-D the bore region. From the state variable distri- simulation in a couple of hours, while a 3-D bution plot, the state variable at a specific stage simulation can take anywhere between a day to of the forging is known. This specific stage, a week, depending on the part complexity [Wu et al., 1996].

16.5 Process Modeling Output

The process modeling provides extensive information of the forging process. The output of process modeling can be discussed in terms of the metal ﬂow, the distribution and history of state variables, the equipment response during forging, and the microstructure of the forging.

16.5.1 Metal Flow The information on metal ﬂow is very important for die design. Improper metal ﬂow pro- Fig. 16.2 Lap prediction using process modeling tool © 2005 ASM International. All Rights Reserved. www.asminternational.org Cold and Hot Forging: Fundamentals and Applications (#05104G)

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shown in Fig. 16.3(a), is the end of the forging. the ﬁgure. The ﬁnal strain value, 1.5, shown in The distribution of the state variables can be Fig. 16.3(b) is in agreement with the value plotted for any other stages of forging as well. shown in the distribution plot in Fig. 16.3(a). Figure 16.3(b) shows the effective strain ver- The history plot of state variables (strain, strain sus time of a material point located at midheight rate, and temperature) provides valuable infor- of the bore section of the forging, as shown in mation on the thermomechanical history of the Fig. 16.3(a). In this isothermal forging case, a forging that determines its mechanical proper- 20 min deformation time was used, as shown in ties.

16.5.3 Equipment Response/Hammer Forging Process modeling also provides the information regarding the response of the equipment. Examples of equipment response discussed here are forging load and ram velocity of hammer forging. The information is usually not available in the hammer shop. However, it is useful for understanding the hammer response to a forging process. Figure 16.4 shows the load versus stroke predicted for a hammer forging operation. The figure shows that there are eight blows in the hammer operation. Each ends with a zero load. The stroke 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 during forging. This zero position is the same for all of the eight hammer blows. With the increase in the number of blows, the load increases and the stroke per blow decreases. The last blow of the sequence has the shortest stroke. This be- havior is very real for hammer forging operations. During a hammer forging operation, the workpiece increases its contact area with the dies, which increases the forging load. The total Fig. 16.3 (a) Effective strain distribution and (b) the effective available blow energy is fixed for a hammer. strain history of the center location of a closed-die With the increase in forging load, the length of forging

Fig. 16.4 Load versus stroke obtained from a hammer forg- Fig. 16.5 Ram velocity versus stroke obtained from a ham- ing simulation mer forging simulation © 2005 ASM International. All Rights Reserved. www.asminternational.org Cold and Hot Forging: Fundamentals and Applications (#05104G)

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stroke is reduced. Moreover, the blow efficiency, reduced with the increase in forging load. Thus, which is the ratio between the energy used for a smaller amount of energy is available toward deformation and the total blow energy, is also the end of a blow sequence and with the decrease in the stroke per blow. Figure 16.5 gives the ram velocity versus stroke obtained from a simulation of another hammer forging process. There are nine blows for this hammer operation. The velocity of the first blow was smaller than the other eight blows, because a soft blow was used initially to locate the workpiece. In a soft blow, there is only a portion of blow energy applied to the workpiece. Thus, the first blow has a smaller starting ram velocity. After the first blow, full energy was applied to the forging. Thus, the starting ram ve- Fig. 16.6 Prediction of the distribution of the size (lm) of gamma prime for a Rene 88 experimental forging

Fig. 16.7 Comparisons of hot-die forging and mechanical press forging of an experimental part using process modeling

Fig. 16.9 Predicted model and optically measured grain sizes in the three developmental Rene´ 88DT disks Fig. 16.8 Rene 88 experimental part out of forging press with (a) coarse, (b) medium, and (c) ﬁne grains [Hardwicke et al., [Hardwicke et al., 2000] 2000] © 2005 ASM International. All Rights Reserved. www.asminternational.org Cold and Hot Forging: Fundamentals and Applications (#05104G)

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locity for the rest of the blows was the same. strengthening superalloys. The size and spacing There is always an energy loss to surroundings are two features of interest in gamma-prime pre- in a hammer blow. Therefore, blow efficiency cipitation. Figure 16.6 shows the prediction of needs to be factored in for each hammer blow. the distribution of the size of gamma prime of However, the blow efficiency only has an effect an experimental nickel-base superalloy forging, after the ram/die workpiece are in contact. Rene 88, coupled with a few measurement Hence, blow efficiency does not influence the points. The measurement made is in the range starting velocity of the ram/die. It is factored in of 0.07 to 0.21 lm. The model predicts a range during the blow. The decay in ram velocity in of 0.08 to 0.14 lm. The fine gamma prime was each blow is a result of both the energy con- correctly predicted and the coarser gamma prime sumption in deforming the workpiece and the was underpredicted, which pointed out the need energy lost to the surroundings. for further improvement of the gamma-prime model. The microstructure prediction feature is 16.5.4 Microstructures in Superalloys useful for the process development for closed- die forging. Microstructure and property modeling is now the major emphasis in advanced forging process design and improvement, especially in forging 16.6 Examples of aerospace alloys such as nickel and titanium su- Modeling Applications peralloys. The development and utilization of physical metallurgy-based microstructure mod- One of the major concerns in the research of els and the integration of the models with finite- manufacturing processes is to find the optimum element analysis has allowed for microstructure production conditions in order to reduce pro- prediction by computer. Two important micro- duction costs and lead-time. In order to optimize structural features of superalloy forgings are the a process, the effect of the most important pro- grain size and the gamma-prime precipitation. cess parameters has to be investigated. Con- The grain size modeling is discussed in detail in ducting experiments can be a very time-consum- Chapter 19, “Microstructure Modeling in Su- ing and expensive process. It is possible to peralloy Forging.” The prediction of gamma- reduce the number of necessary experiments by prime distribution is discussed here. Gamma using FEM-based simulation of metal forming prime is a very important precipitation phase in processes.

Fig. 16.10 Investigation of defects in ring gear forging using FEM [Jenkins et al., 1989] ASM International is the society for materials engineers and scientists, a worldwide network dedicated to advancing industry, technology, and applications of metals and materials.

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