Finite element analysis and experimental validation of flashless cold forging of propeller hubs and blade of autonomous underwater vehicle

Finite element analysis and experimental validationof flashless cold forging of propeller hubs andblade of autonomous underwater vehicleH M T Khaleed1*, Z Samad1, A R Othman1, M A Mujeebu1, M R Arshad2, A R Ab-Kadir1, M Ab Hussaini1, and

A B Abdullah1

1School of Mechanical Engineering, Universiti Sains Malaysia, Penang, Malaysia2School of Electrical and Electronics Engineering, Universiti Sains Malaysia, Penang, Malaysia

The manuscript was received on 1 October 2009 and was accepted after revision for publication on 9 December 2009.

DOI: 10.1243/09544054JEM1845

Abstract: In this paper three-dimensional finite element method analysis and experimentalflashless cold forging of aluminium front and back hubs, and the blade of an autonomousunderwater vehicle propeller are presented. The rigid–plastic finite element simulation is per-formed using Deform F-3V6.0, to estimate the optimum load required for the flashless coldforging. The complex profiles of the hubs and blade are modelled using Solidworks SP4.02007,which is also used for the modelling of the workpiece and die-punch assembly. The workpieceused is of AISI AL6061 and the die material is die steel (AISI D2). The process is optimized toform the propeller back and front hubs, and the blade. For all the models, three workpieces withdifferent specifications are selected and investigated to obtain the optimum workpiece thatgives flashless cold forging with no underfilling. Based on the simulation results, the flashlesscold forging is successfully done on a 100 tonne C-type machine. The experimental forgedsamples conform well with the simulated models.

Keywords: cold forging, underfilling, workpiece, flash, underwater vehicle, propeller, hub,blade

1 INTRODUCTION

In the area of cold forging die design and optimiza-tion, substantial investigations have been carried outby many researchers using various tools and techni-ques such as finite element method (FEM), artificialneural network (ANN), genetic algorithm (GA), andother computer-aided design (CAD) techniques. Adetailed review of all those works is beyond the scopeof the present paper. However, works related to thecurrent study are reviewed and presented here.

A computer-aided system called ‘Forming’ fordesigning the forming sequence for multistage for-ging of round parts was presented by Badawy et al.[1]. Natsume et al. [2] performed experimental andFEM studies to understand the dimensional differ-

ence between forging tools and forged components.Oh et al. [3] discussed some issues related to thesimulation of cold forging operations and presentedfew examples to demonstrate the capability of theDeform system in handling cold forging problems.

Meidert et al. [4] presented a finite element (FE)-based numerical modelling and physical modellingwith plasticine, for the process design of cold forging.A strategy was developed to allow successful two-dimensional (2D) FE modelling of bevel gear forgingand the results from the process simulation are usedas load input data for a punch stress analysis.

Petersen and Frederiksen [5] presented a 2D finiteelement analysis with special emphasis on the effectsof plasticity. The geometry treated concerned a diewith rather sharp fillets and the main issue was toexamine stress concentration and propagation of theplastic zone in the fillet area according to the appliedforging pressure. An automatic mesh generationroutine was used to investigate different fillet designsand results of an optimization study were presented.

*Corresponding author: Mechanical Department, University

Sains Malaysia, Sari Ampang, Nibon Tebal, Pulau Pinang

14300, Malaysia.

email: [email protected]

JEM1845 Proc. IMechE Vol. 224 Part B: J. Engineering Manufacture

CASE STUDY 1455

Duggirala et al. [6] introduced a method for designoptimization of process variables in cold forgingsequences. To minimize the possibility of the initia-tion of tensile fracture in the outer race preform of aconstant velocity joint manufactured by cold formingoperations, an adaptive microgenetic algorithm wasimplemented. Kim et al. [7] used three-layer neuralnetwork trained by the back-propagation algorithmto determine the initial billet geometry for the forgedproducts using a function approximation. Xu and Rao[8] carried out an analysis of isothermal axisymmetricspike-forging using an integrated FEM code.

The influence of different geometric parameters,processing variables, and interfacial conditions onthe instantaneous spike height was studied. Hsu andLee [9] proposed an ANN-based cold forging processdesign method suitable for the shop floor to decidethe cold forging process parameters for producing asound product within the required minimum quan-tity of the die set. Falk et al. [10] analysed theapplicability of different failure concepts for a closedcold forging die. The critical, process-dependent loadwas quantified and localized by using FEM.

Im et al. [11] introduced a process design techni-que, based on a forging simulator and commercialCAD software together with its related design systemfor the cold-formed forging of ball joints. Lee et al.[12] developed a CAD system using Auto-Lisp andthree die-design modules – forward extrusion,upsetting, and combined extrusion – were presented.

The hot forging of aerofoil blades was performedby Hu et al. [13] who modelled smooth Bezier sur-faces using Abaqus/Explicit FE software. Qin et al.[14] worked to combine coupled thermomechanicalFE plastic simulation and heat transfer analysis todefine heat-flux-density functions across die/work-piece interfaces. Flashless cold forging of an alumi-nium connecting rod was studied by Vazquez andAltan [15] using Deform-FEM package. Ishikawa et al.[16] studied analytically the effects of forming stres-ses and generated heat on the dimensional changeof punch die and workpiece during forging. Hussainet al. [17] presented a used numerical study on theforming of a clutch-hub using CAD simulation toolCAMPform. Simulations for S10C steel using variousdie and workpiece geometries were carried out todetermine the most suitable forming condition forproduction of the clutch-hub.

Lee et al. [18] evaluated the characteristics ofelastic deformation at a forming tool for a cold forgedalloyed steel by experimental and FEM analysis. Kimet al. [19] used rigid–plastic finite element simulationto analyse the deformation characteristic of thewhole impeller hub forming processes and to opti-mize the process. Ohashi et al. [20] developed a CADsystem to design forging sequences and die profilesby considering forging as a procedure for adding

features to a raw material, process planning as theinverse procedure, and each step of the forging pro-cess as a combination of feature-eliminating pro-cesses. The system designed the forging sequencesand die profiles from the product to its raw materialby eliminating features. Castro et al. [21] made anattempt to obtain optimal design in forging using GA.The design problem was formulated as an inverseproblem incorporating an FE thermal analysis modeland an optimization technique conducted on thebasis of an evolutionary strategy. A rigid viscoplasticflow-type formulation was adopted, valid for bothhot and cold processes. The chosen design variableswere workpiece preform shape and workpiecetemperature.

The process design for closed-die forging of a bevelgear used in automobile transmission system wasmade by Song and Im [22] using three-dimensional(3D) FE simulations. Process variables were thepressing type, punch location, and billet diameter.Based on the simulation results, appropriate processdesign without causing underfilling and foldingdefect was determined. Few more related works havealso been reported in the recent literature [23, 24].

Even though many researchers have focused onvarious issues in cold and hot forging, study of coldforging of complex geometries such as propeller hubsand blades has not been reported so far. Hence, inthis work, the focus was on FEM and experimentalanalysis for flashless cold forging of back and fronthubs and blade of an AUV propeller. Three-dimen-sional simulations are performed by Deform F3 V 6.0and geometrical modelling for the die and the work-piece is performed by Solidworks 2007 SP4.0. Thesimulation results are successfully validated byexperiments conducted on a C-type machine.

2 MATERIALS AND METHODS

2.1 Material properties for die, punch, andmountings

The material properties chosen for the current studyare shown in Table 1. Aluminium is selected for the

Table 1 Material properties of workpiece die, punch, andother mountings

Parameter Workpiece Die PunchDieinsert Plates

Material type AISI 6061 AISI D2 AISI D2 SW41 ASI 1045

Young’smodulus (GPa)

70 210 210 210 210

Yield (MPa) 386 2200 2200 1900 951

Poisson ratio 0.35 0.33 0.33 0.33 0.33

Hardness HRC-24 HRC-62 HRC-62 HRC-55 HRC-30

Proc. IMechE Vol. 224 Part B: J. Engineering Manufacture JEM1845

1456 H M T Khaleed, Z Samad, A R Othman, M A Mujeebu, M R Arshad, et al.

workpiece owing to its suitability for corrosiveenvironment, which is significant for underwaterapplications and its good formability. The yieldstrength values given are all in compression. Thehardness values of the components are chosen basedon the working stresses.

2.2 FEM analysis

The components are modelled by Solidworks 2007SP4.0 and assembled using assembly module andsaved as STL file. Then all the STL files are importedto Deform-F3 3D software, the simulation is run, andthe results are observed. If flash occurs in the work-piece, the next step is to optimize the workpiece toavoid the flash. Accordingly simulations are repeateduntil no flash is found. For the no-flash workpiece, ifeffective stresses in the die are found more than theyield strength, then the parameters are changed untilno plastic element is found. The inertia effect causedby the mass matrix is negligible even if the forgingspeed or the density of the material is increased toreduce the computational time [13]. The velocity ofthe punch is 250mm/s, the friction coefficient is 0.15,and the initial temperature of the workpiece, punchand die is 25 �C. The simulation models are shown inFig. 1 (a, b, and c).

For front and back hubs, the punch stroke is14.4mm, the number of steps are 100, and the stepincrement is 10. For both hubs and blades, the meshsize ratio of the die is 3, the interpolation force tol-erance is 0.0001, the bottom surface of the die isconstrained in X, Y, and Z directions, the starting stepnumber is 1, the number of the simulation step is 1,step increment is 1, and maximum elapsed processtime is 1 s. The tetrahedron elements are used formeshing; in the case of hubs, the number of elementsused for workpiece is 2000, for punch 50 500, and fordie 50 500. For the blade, the number of elements forthe workpiece is 37 000 except for the first stage which

is 32 000, for punch 79 500, and for die 79 500. Com-pared with the other stages, the geometry for the firststage was simple and flat, hence a smaller number ofelements was sufficient. The punch stroke is 3mm inthe Y direction for the first and second stages and 2.5,2, and 8.8mm for stages 3, 4, and 5 respectively.

Owing to the geometrical complexity, the blade isformed in five stages. In the first stage an aluminiumsheet of 3mm thickness is cut into two preforms:preform 1 and preform 2. In the second stage eachpreform is forged to form a blade having ahy-drodynamic profile; the maximum height is 3mmand minimum is 0.3mm. The flash is trimmed in thethird stage, by shearing operation. In the fourth stage,a pin is formed to fix the blade in between the hubs.In the fifth and final stage the blade is placed on thedie and punch, which have a twisted shape, and isforged to obtain the final blade profile. The variousstages are shown in Fig. 2.

2.3 Optimization of the workpiece

The optimum workpiece specifications are obtainedbased on the assumption that the volume of theworkpiece is equal to the volume of the cavity to fillor the volume of the final forged product [15]. As therequired dimensions of the final product are known,its volume can be easily obtained from Solidworks2007 SP4.0. Next, the aim is to obtain the optimumspecifications of the workpieces. For the hubs, acylindrical workpiece is considered and its initiallength and radius, L1 and r1 respectively, are assumedsuch that its volume is equal to the final volume ofthe product. Then by equating the volumes of theworkpiece and the final product, the followingempirical relations are obtained.

For the front hub

r1 ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi15 308

pL1

rmm ð1Þ

Fig. 1 Simulation models of die and punch assembly for the hubs and blade


Flashless cold forging of propeller hubs and blade 1457

For the back hub

r1 ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi9772:2

pL1

rmm ð2Þ

Now, by performing a sufficient number of iterationson equations (1) and (2) with various combinationsof L1 and r1, the optimum values of the respectivelengths and radii can be obtained. These iterationsare performed by the combined Solidworks 2007SP4.0. and Deform 3D V 6.1 environment. Each timethe modelling is performed with a set of L1 and r1,

transferred to Deform 3D V 6.1, and checked forunderfilling and flash. If underfilling is observed, theprocedure is repeated with various sets of L1 and r1

until no underfilling with minimum flash is observed.At this stage the dimensions of the workpiece for thefront hub can be finalised and the forging operationcan proceed.

However, for the back hub, since a capsule-shapedhole is involved, a preform is made by drilling thehole and further iterations are performed to arrive atthe optimum diameters of the hole and workpiece.The optimized dimensions of the front and back hubsare shown in Fig. 3.

For the purpose of iteration the aspect ratios for thefront and back hubs are defined as

Following the procedure similar to that of the hubs,the corresponding empirical relation for the blade isobtained as

Awp ¼ 891:97

twpmm2 ð5Þ

where Awp is the cross-sectional area of the preformand twp is its thickness.

In this case, the iterations are performed on Awp

and twp, instead of L1 and r1. For instance, keepingAwp constant, vary twp until no underfilling isobserved. At this stage if flash are objectionable, theprocedure is repeated for reduced values of Awp untilthe flash is reduced to the acceptable limit. Thedimensions of the optimized workpiece, obtainedthrough this procedure, are shown in Fig. 4.

2.4 Finite element formulation

In cold forging elastic deformation can be neglectedand the material is considered as rigid plastic [19,25]. Therefore in this study, the rigid–plastic FEM isapplied for the analysis of deformation. The basicequations of the rigid–plastic finite element are asfollows [19]

Equilibrium equation

sij;j ¼ 0 ð6ÞCompatibility and incompressibility condition

_«ij ¼ 1

2uij þ uji

� �: _«v ¼ uii ¼ 0 ð7Þ

Constitutive equations

s0ij ¼

2�s

3 _«_«ij: �s ¼

ffiffiffi3

2

rs0ij s

0ij

� �: _�« _«ij _«ij

� � ð8Þ

Aspect ratio for front hub, (AR)F¼Diameter of workpiece for front hub

Height of workpiece for front hubð3Þ

Aspect ratio for back hub, (AR)B¼Diameter of workpiece for back hub

Diameter of hole for back hubð4Þ

Fig. 2 Simulation stages of blade formation



Boundary conditions

sijni ¼ Fj on SF; ui ¼ Ui on SU ð9Þwhere sij and _«ij are the stress and the strain velocity,respectively. �s and _« are the effective stress and theeffective strain velocity, respectively. Fj denotes theforce on the boundary surface of SF andUi denotes thedeformation velocity on the boundary surface of SU.

The basic mathematical equations are as follows[19]

Y¼

ð�s _�«dV �

ðsp

tividS ð10Þ

where �s is the effective stress, _�« is effective strain rate,ti is the traction specified on the boundary SF, and viis the velocity component.

The incompressibility constraint on admissiblevelocity fields may be removed by introducing apenalty constant K and modifying the functional(equation (10)). Then, the solution of the originalboundary-value problem is obtained from the solu-tion of the dual variational problem, where the first-order variation of the functional vanishes [25]

dY

¼ðv

�sd _�«dV �ðsF

tidvidS þ K

ðv

_«vddV ¼ 0 ð11Þ

where dvi is the arbitrary variation and d _�«; d _«v are thevariations in strain rate from dvi.

Equation (11) can be converted to non-linearalgebraic equation by using FE discretization. Using anumerical technique such as that of New-ton–Raphson, the solution for non-linear simulta-neous equations can be obtained. In this studyDeform 3D V 6.1 is used for metal forming simula-tion.

3 SIMULATION RESULTS AND DISCUSSION

3.1 Underfilling and flash

3.1.1 Front hub

The underfilling and flash are observed for thefront hub for three different aspect ratios (AR)F 0.961,1.038, and 1.115 (cases I, II and III respectively).Figures 5, 6, and 7 show sectional views of the corre-sponding forged models. For case I and case II, slightunderfilling is observed at the corners of the top andbottom hole cavities. However, for case I, underfillingis observed at right and left walls of the die insert too,as evident from Fig. 2. For the case III no underfillingis found but a flash of 1318.51mm3 is observed at theflash zone as shown in Fig. 7. The underfillings at thecorners of the bottom holes and the top hole aredesirable to act as fillets. Hence it is concluded thatcase II is the optimum workpiece. The results aresummarized in Table 2.

3.1.2 Back hub

The underfilling and flash are observed for the backhub for three different aspect ratios (AR)B 5.80, 5.31,and 5.90 (cases I, II, and III respectively as shown inTable 3). Figures 8, 9, and 10 show sectional views ofthe corresponding forged models. For all the casesunderfilling is observed at the bottom corners and forcase I, at the side walls of die insert as well; but theseare well within the tolerable limit. Since we areinterested in flashless forging, case I seems the best

Fig. 3 The dimensions of optimized workpieces for the hubs (all dimensions inmm)

Fig. 4 Schematic of the blade preform, showing optimizeddimensions



option as it has the minimum flash as evident fromFig. 8 and Table 3. Moreover no extra trimmingoperation is needed to remove this flash because theforged back hub has to be drilled at the location ofthe flash.

Fig. 6 Cross-section view of front hub formation for case II

Fig. 7 Cross-section view of front hub formation forcase III

Fig. 5 Cross-section view of front hub formation for case I

Table 2 Workpiece volume, flash volume, and percentageflash of front hub for various aspect ratios

Cases (AR)F

Workpiece volume(mm3) Flash volume (mm3)

I 0.961 12 437.57 No flashII 1.038 14 317.91 No flashIII 1.115 16 937.74 1318.51

Table 3 Workpiece volume, flash volume, and percentageflash of back hub for various aspect ratios

Cases (AR)B

Workpiece volume(mm3) Flash volume (mm3)

I 5.80 9789.99 570.82II 5.31 10 040.45 821.28III 5.90 10 344.17 1125

Fig. 10 Cross-section view of back hub formation forcase III

Fig. 9 Cross-section view of back hub formation for case II

Fig. 8 Cross-section view of back hub formation for case I



3.1.3 Blade

Two preforms – preform 1 and preform 1 – are testedand the underfilling and flash are observed for threedifferent values of twp (cases I, II, and III respectively)and the results are summarized in Table 4. Figure 11shows the sectional views of the respective simulated

forged models for preform 1. For case I and case II,slight underfilling is observed at the top side of cav-ities. However for case I, underfilling is observed atthe top and left side of the flash zone too as evidentfrom Fig. 11(a). For the case III no underfilling isfound but a flash of 988.2mm3 (Table 4) is observedat the flash zone as shown in Fig. 11(c). Hence it isconcluded that, to achieve no underfilling, the opti-mum thickness is 3mm. However there is flash whichis to be minimized by varying Awp as mentioned insection 2.3. For this purpose, preform 2 is made witha reduced Awp and tested for three different twp (casesI, II, and III) and the results are tabulated in Table 5.Figure 12 shows the sectional views of the simulatedmodels of preform 2 for the three cases. For the casesI and II underfilling is observed at the bottom and topand for case I, at the flash zone as well. Since we areinterested in flashless forging, case III seems to be thebest option as it has the minimum flash as evidentfrom Fig. 12(c) and Table 5. Moreover no extra trim-ming operation is needed to remove this flash.

3.2 Effect of aspect ratio on forging load

3.2.1 Front and back hubs

Tables 4 and 5 show the predicted forging loads andeffective stresses with respect to the stroke length ofthe punch under various aspect ratios for the frontand back hubs. For the front hub, the maximumforging load is found to be for case III (1.5 · 106N).The load goes on increasing with decrease in aspectratio as shown in Table 6. The required load to formthe front hub from the case I is the least (1.08· 105N)but underfilling is the worst, as already seen. Thuswhen it comes to a trade-off between forging loadand flash, case II turns out to be the best. In the caseof the back hub, the minimum value of forging loadfor case I (1.2 · 105 N), as shown in Table 7, sub-stantiates its choice in section 3.1.2 as the optimummodel.

3.2.2 Blade

Tables 8 and 9 show the predicted forging loads andeffective stresses with respect to the stroke length ofthe punch for different values of twp of the preform 1and preform 2. The load for preform 1, as shown in

Table 4 Workpiece volume, flash volume and percentageflash of preform 1 for various thicknesses (Awp¼626.72mm2)

Cases twp (mm)Workpiece volume(mm3) Flash volume (mm3)

I 2.0 1253.45 361.48II 2.5 1566.81 634.84III 3.0 1880.17 988.2

Fig. 11 Cross-section views of preform 1 for different cases

Table 5 Workpiece volume, flash volume and percentageflash of preform 2 for various thicknesses (Awp¼377.90mm2)

Cases twp (mm)Workpiece volume(mm3) Flash volume (mm3)

I 2.0 755.82 No flashII 2.5 944.77 52.8III 3.0 1095.65 202.68



Table 8, is least for the case I (5.71· 105) but under-filling is more as already seen in Fig. 11(a); so whenconsidering the underfilling, case III is the best. Butthe issue of maximum load in preform 1 case III isrectified in preform 2 case III where it is significantly

reduced to 3.39 · 105N, as shown in Table 9; hencepreform 2 case III could be the best choice.

4 THE EXPERIMENTAL SET-UP

For the experimental work, a 100 tonne C-type for-ging machine is used. Different amounts of defor-mation are obtained by altering the shut height of thepress. The machine is run at a speed of 250mm/s.The die and punch are aligned at the same axis. Themachine specifications are summarized in Table 10.To set the load on the workpiece, it is essential toknow the effective stress and strain of the material.These are obtained through FEM simulation andshown in Tables 6 to 9 for front and back hubs, andblade preforms 1 and 2, respectively. The loads areset accordingly and the corresponding hub and bladeprofiles are forged.

Fig. 12 Cross-section view of preform 2 for various cases

Table 6 Predicted load and effective stress of front hub forvarious aspect ratios

Case (AR)F Load prediction in N Effective stress, MPa

I 0.961 1.08· 105 373II 1.038 2.21· 105 421III 1.115 1.5·106 441

Table 7 Predicted load and effective stress of back hub forvarious aspect ratios

Case (AR)B Load prediction in N Effective stress, MPa

I 5.80 1.2· 105 443II 5.31 1.59· 105 461III 5.90 3.37· 105 468

Table 8 Load prediction and effective stresses of preform1 for various thicknesses (Awp¼ 626.72mm2)

Case twp (mm) Load prediction in N Effective stress, MPa

I 2.0 5.71· 105 438II 2.5 7.23· 105 515III 3.0 7.83· 105 542

Table 9 Load prediction and effective stresses of preform2 for various thicknesses (Awp¼ 377.90mm2)

Case twp (mm) Load prediction in N Effective stress, MPa

I 2.0 2.09· 105 308II 2.5 2.45· 105 315III 3.0 3.39· 105 426

Table 10 Machine specifications

Item Specification

Type Single strike, continuous strokeModel J23Capacity 1000 kNRPM 1455 r/minValve pressure 0.2�1MPaFrequency 50HzElectrical data 380V, 55A, 3� 50Hz



5 EXPERIMENTAL RESULTS

5.1 Front hub

Based on the simulation, the front and back hubshave been cold forged for different aspect ratios andthe underfilling, flash, and dimensional accuracy are

compared with the simulated models. Figure 13shows the experimental forged samples of the fronthub for cases I, II, and III. Exactly similar to the pre-dictions, case II is observed to be the best, as it hasacceptable underfilling and no flash. Table 11 showsthe comparison of the required and experimental

Fig. 13 The experimental forged samples of front hub for cases I, II, and III

Table 11 Comparison of designed and experimental dimensions of front hub

Case (AR)F

Diameter inmm Height inmm

Required Forged % error Required Forged % error

I 0.961 30 29.29 2.37 25 23.74 5.04II 1.038 30 30.18 0.60 25 23.64 5.44III 1.115 30 30.16 0.53 25 24.42 2.32



dimensions of the front hub for cases I, II, and III. It isobserved that case III has the best dimensionalaccuracy, but there is the problem of flash as alreadyseen in the simulation (Fig. 7) and experimentallyproved in Fig. 13. Hence the next option is case II,which has no flash and acceptable dimensional error.

5.2 Back hub

The required dimensions (design values) of differentparts of the back hub are illustrated in Fig. 14. Inorder to achieve this final model, the workpiece hasbeen optimized by FEM simulation and comparedwith the experiment. Figure 15 shows the experi-mental forged samples of the back hub for cases I, II,and III. Exactly similar to the predictions, case I is Fig. 14 Design dimensions of the back hub

Fig. 15 Forged samples of back hub



observed to be the best, as it has acceptable under-filling, and minimum flash and load. The dimen-sional errors for this case are also within the tolerablerange, as illustrated in Table 12. It is worth notingthat the capsule-shaped hole at the bottom of theback hub is also formed in a single operation.

5.3 Blade

Based on the simulation, the preform 1 is cold forgedfor different twp and the underfilling, flash, and

dimensional accuracy are compared with the simu-lated models. Figure 16 shows the experimentalforged samples of the preform 1 for cases I, II, and III.Exactly similar to the predictions, case III shows nounderfilling but flash. Accordingly, only case III istested experimentally for preform 2 and results arefound extremely similar to the simulated ones.However, one stage (c-trimming) as discussed in thesimulation steps, is reduced in this case since verylittle flash occurred. The required thickness (0.3mm)

Table 12 Comparison of designed and experimental dimensions of back hub

Parameter OD* a@ b@ c@ d@ E@

Required dimensions (mm) 30.00 6.00 10.00 5.00 15.00 15.00Case I (AR)B¼ 5.80 Forged 29.29 6.04 9.95 5.05 14.85 15.18

% error 2.36 0.67 0.50 1.00 1.00 1.20

Case II (AR)B¼ 5.31 Forged 29.63 6.14 10.17 5.09 14.97 15.52% error 1.20 2.30 1.70 1.80 0.20 3.40

Case III (AR)B¼ 5.90 Forged 29.89 6.01 9.94 4.96 15.07 15.60% error 0.36 0.16 0.60 0.80 0.46 4.00

* Outside diameter of the final product. @Refer Fig. 14

Fig. 16 The experimental forged samples of blade preform 1 for cases I, II, and III

Fig. 17 The experimental forged samples of blade preform 2 for case III



was impossible to obtain by using preform 1 and thisproblem was rectified by using preform 2 case III,which therefore is found to be the optimum, as evi-dent from simulation as well. Figures 17(a), (b), (c),and (d) depict the four stages to form the blade.

6 CONCLUSION

The FEM analysis and experiments for flashless coldforging of hubs and blade of an autonomous under-water vehicle propeller have been performed suc-cessfully. The simulation results are in goodagreement with the experiments. The handling ofcomplex geometries especially for cold forging, theworkpiece optimization, detailed numerical analysis,and strong experimental results are remarkable con-tributions in this work. Forging of very thin geometry(0.34mm) with hydrodynamic profile as in the case ofblade, is also worth noting. Owing to the processoptimization, one stage (the trimming process) ofblade forming could be reduced, by using preform 2.Stress analysis of puncher and ejector of hubs andblade, and optimization of die design using numer-ical and experimental methods to reduce the overallcost of production, are a few of the potential futureworks.

ACKNOWLEDGEMENTS

The authors would like to thank the Ministry ofTechnology and Innovation, Malaysia, and Under-water Robotics Research Group, School of Electricaland Electronics Engineering, Universiti Sains Malay-sia for the financial support for this research work.

� Authors 2010

REFERENCES

1 Badawy, A. A., Raghupathi, P. S., Kuhlmann, D. J., andAltan, T. Computer-aided design of multistage forgingoperations for round parts. J. Mech. Working Technol.,1985, 11(3), 259–274.

2 Natsume, Y., Miyakawa, S., and Muramatsu, T. J. Fati-gue abstract analysis on the fracture surface and coldforging die set using the X-ray fractography. J. Soc.Mater. Sci. (Jap.), 1989, 38(429), 612–616.

3 Oh, S. I., Wu, W. T., and Tang, J. P. Simulations of coldforging processes by the DEFORM system. J. Mater.Process. Technol., 1992, 35(3–4), 357–370.

4 Meidert, M., Knoerr, M., Westphal, K., and Altan, T.Numerical and physical modelling of cold forgingof bevel gears. J. Mater. Process. Technol., 1992, 33(1–2),75–93.

5 Petersen, T. and Frederiksen, P. S. Fillet design in coldforging dies. Computers and Structs, 1994, 50(3), 393–

400.6 Duggirala, R., Shivpuri, R., Kini, S., Ghosh, S., and Roy,

S. Computer aided approach for design and optimiza-

tion of cold forging sequences for automotive parts. J.

Mater. Process. Technol., 1994, 46(1–2), 185–198.7 Kim, D. J., Kim, B. M., and Choi, J. C. Determination of

the initial billet geometry for a forged product using

neural networks. J. Mater. Process. Technol., 1997, 72(1),

86–93.8 Xu, W. L. and Rao, K. P. Analysis of the deformation

characteristics of spike-forging process through FE

simulations and experiments. J. Mater. Process. Technol.,

1997, 70(1–3), 122–128.9 Hsu, Q.-C. and Lee, R.-S. Cold forging process design

based on the induction of analytical knowledge.

J. Mater. Process. Technol., 1997, 69(1–3), 264–272.10 Falk, B., Engel, U., and Geiger, M. Estimation of tool life

in bulk metal forming based on different failure con-

cepts. J. Mater. Process. Technol., 1998, 80–81, 602–607.11 Im, C. S., Suh, S. R., Lee,M. C., Kim, J. H., and Joun,M. S.

Computer aided process design in cold-former forging

using a forging simulator and a commercial CAD soft-

ware. J. Mater. Process. Technol., 1999, 95(1–3), 155–163.12 Lee, R.-S., Hsu, Q.-C., and Su, S.-L. Development of a

parametric computer-aided die design system for cold

forging. J. Mater. Process. Technol., 1999, 91(1–3), 80–89.13 Hu, Z. M., Brooks, J. W., and Dean, T. A. Three-

dimensional finite element modelling of forging of a

titanium alloy aerofoil sectioned blade. Trans. ASME, J.

Mfg Sci. Engng, 1999, 121, 366–371.14 Qin, Y., Balendra, R., and Chodnikiewicz, K. A method

for the simulation of temperature stabilisation in the

tools during multi-cycle cold-forging operations. J.

Mater. Process. Technol., 2000, 107(1–3), 252–259.15 Vazquez, V. and Altan, T. New concepts in die design –

physical and computer modelling applications. J. Mater.

Process. Technol., 2000, 98(2), 212–223.16 Ishikawa, T., Yukawa, N., Yoshida, Y., Kim, H., and

Tozawa, Y. Prediction of dimensional difference of

product from tool in cold backward extrusion. CIRP

Annls Mfg Technol., 2000, 49(1), 169–172.17 Hussain, P. B., Cheon, J. S., Kwak, D. Y., Kim, S. Y., and

Im, Y. T. Simulation of clutch-hub forging process using

CAMPform. J. Mater. Process. Technol., 2002, 123(1),

120–132.18 Lee, Y.-S., Lee, J.-H., Choi, J.-U., and Ishikawa, T.

Experimental and analytical evaluation for elastic

deformation behaviors of cold forging tool. J. Mater.

Process. Technol., 2002, 127(1), 73–82.19 Kim, Y. S., Son, H. S., and Kim, C. I. Rigid–plastic finite

element simulation for process design of impeller hub

forming. J. Mater. Process. Technol., 2003, 143–144(20),

729–734.20 Ohashi, T., Imamura, S., Shimizu, T., and Motomura,

M. Computer-aided die design for axis-symmetric cold

forging products by feature elimination. J. Mater. Pro-

cess. Technol., 2003, 137(1–3), 138–144.21 Castro, C. F., Ant�onio, C. A. C., and Sousa, L. C. Opti-

mization of shape and process parameters in metal



forging using genetic algorithms. J. Mater. Process.Technol., 2004, 146(3), 356–364.

22 Song, J.-H. and Im, Y.-T. Process design for closed-dieforging of bevel gear by finite element analyses. J. Mater.Process. Technol., 2007, 192–193, 1–7.

23 Sellier, M., Breitbach, C., Loch, H., and Siedow, N. Aniterative algorithm for optimal mould design in high-precision compression moulding. Proc. IMechE, Part B:J. Engineering Manufacture, 2007, 221(B1), 25–33. DOI:10.1243/09544054JEM606.

24 Zhang, Y. and Chou, K. A parametric study of partdistortions in fused deposition modelling using

three-dimensional finite element analysis. Proc.

IMechE, Part B: J. Engineering Manufacture, 2008,

222(B8), 959–996. DOI: 10.1243/09544054JEM990.25 Kim, D. J. and Kim, B. M. Application of neural network

and FEM for metal forming processes. Int. J. Machine

Tools Mfg, 2000, 40, 911–925.



Documents

Finite element analysis and experimental validation of flashless cold forging of propeller hubs and blade of autonomous underwater vehicle