Experimental Studies on Optimization of Process Parameters

ORIGINAL ARTICLE

Experimental studies on optimization of process parametersand finite element analysis of temperature and stressdistribution on joining of Al–Al and Al–Al2O3

using ultrasonic welding

Elangovan Sooriyamoorthy & Shenton Ponnayya John Henry &

Prakasan Kalakkath

Received: 16 June 2010 /Accepted: 22 November 2010 /Published online: 8 December 2010# Springer-Verlag London Limited 2010

Abstract This study is carried out to optimize the processparameters like weld time, weld pressure, and amplitude ofvibration to maximize the weld strength in Al–Al weldingusing Taguchi’s design of experiments methodology.Experiments are conducted using 0.3-mm thick pieces ofaluminum, and the temperature generated at the weld interfaceand the weld strength for all the specimens are measured.Also, a finite element model is developed that is capable ofpredicting the interface temperature and stress distributionduring welding. Further, a preliminary study on the joining ofalumina to aluminum is also carried out, and the finite elementmodels of temperature and stress distribution during weldingare simulated. Results of experimental work and FEM studiesare compared and found to be in good agreement.

Keywords Ultrasonic welding . Finite element method .

Weld strength . Taguchi design . Temperature distribution

1 Introduction

Material joining is one of the major manufacturingprocesses used to assemble metallic and non-metallic partsfor several applications. The automotive industry is activelyconsidering a number of alternate welding technologies thatwould enable the increased use of lightweight and high-performance materials. Many of the alternate processesinvolve innovative solid-state joining in which metallurgi-cal bonding between the similar or dissimilar materials can

be created without melting. One of the solid-state-basedjoining processes is ultrasonic welding.

In this process, high-frequency vibrations are combinedwith pressure to join the parts together without producingsignificant amount of heat. Ultrasonic welding is used tojoin thin metallic components as the process is a solid-statejoining process. Ultrasonic metal welding is practiced forover 50 years (though in a crude form) and is being used inindustry for many years. It is a process in which two metalsare joined by the application of ultrasonic vibrations (shearforce) under moderate pressure (normal force) in which thevibrations are applied parallel to the interface between theparts. The high frequency relative motion between the partsdisperses oxides and contaminants and brings in anincreasing area of pure metal contact. The progressiveshearing and plastic deformation of asperities result in thebonding of the adjacent surfaces. These forces determinethe weld quality and the power that is required to producethe weld. With the knowledge of the forces that act at theinterface, it is possible to control weld strength.

2 Literature review

Matsuoka [1] conducted experiments on ultrasonic weldingof ceramics and metals using inserts. It is found that ultrasonicwelding has made it possible to weld various ceramics such asAl2O3, SiC, Si3N4, and AlN to metals at room temperaturequickly compared to other welding methods.

IMAI and Matsuoka [2] conducted experimental studiesto find out the optimum parameters for welding ceramicsand metals. It was found that ultrasonic vibration is easyand fast for welding ceramics such as ZrO2, SiC, and Si3N4

with metals such as aluminum, magnesium, and copper.

E. Sooriyamoorthy (*) : S. P. John Henry : P. KalakkathDepartment of Production Engineering,P.S.G College of Technology,Coimbatore 641004, Indiae-mail: [email protected]

Int J Adv Manuf Technol (2011) 55:631–640DOI 10.1007/s00170-010-3059-7

Flood [3] discussed methods to weld copper to alumi-num using USW. It describes the process and parametersthat influence the weld strength. This work also listed thecopper to aluminum welding applications in the electrical,electronics, and automobile industry.

Jeng and Horng [4] investigated the effects of appliedload, surface roughness, welding power, and welding time onbonding strength. The experiments revealed that a decrease inload or ultrasonic power produces a larger weldable range. Itwas found that the contact temperature plays an important rolein bonding strength in the initial phase of welding, and surfaceroughness is the dominant factor in the final stages. Themaximum bonding strength occurs in the initial period fordifferent loads and surface roughness values.

Jahn et al. [5] studied the formation of ultrasonic spotwelds of AA6111-T4 using a single transducer unidirec-tional wedge-reed welder. The evolution of weld micro-structures and weld strength due to anvil cap geometry andwelding energy were studied. It was found that thevariations in lap shear failure load and weld microstructuresas a function of welding energy were only slightlyinfluenced by the changes in the anvil cap geometry. Weldfailure in lap shear tensile tests occurred due to interfacefracture for low-energy welds and by button formation forhigh-energy welds. Initially, microwelds or weld islands ofseveral microns in diameter were generated presumably atasperities of the two pieces being joined. Micro porositywas observed at the periphery of growing weld islands andalong the flow lines. The study showed the effect ofvariations in anvil cap size and the knurl patterns inultrasonic spot welding of AA6111-T4 aluminum.

Ding et al. [6] analyzed the deformation and stressdistributions in the wire and bond pad during the ultrasonicwire bonding using the 2D and 3D finite element methods.It was found that the maximum energy intensity occurred atthe periphery of the contact interface, where weld ispreferentially made as shown by experimental evidence.The total frictional energy increased linearly with bondforce, but the high frictional energy intensity obtained at theperiphery of the interface did not show a similar increase.

Watanabe et al. [7] analyzed the atomic interaction(chemical bonding) across the interface between thealuminum and alumina ceramic, which was ultrasonicallybonded, using an ultrasonic pulse that lasted for a shorttime of 1.5 s by means of Auger electron microscopy inorder to clarify whether chemical bonding can be achievedacross the interface. The results from the Auger spectraanalyses suggested that chemical bonding exists betweenAl and oxygen across the aluminum/alumina interfacebonded ultrasonically for a short duration of 1.5 s, and thataluminum was chemically bonded to alumina. Tsujino et al.[8] investigated the welding characteristics of aluminum,aluminum alloys, and stainless steel by welding specimens

of 6.0-mm thickness by a 15-kHz ultrasonic butt weldingsystem.

Tarng et al. [9] presented an application of fuzzy logicusing the Taguchi method for the optimization of sub-merged arc welding process with multiple performancecharacteristics. The performance characteristics such asdeposition rate and dilution were simultaneously consideredand improved through this approach instead of usingengineering judgment. A novel and efficient approach forquality optimization of manufacturing systems with aconsideration of multiple performance characteristics hasbeen proposed in this study.

The work carried out by Saurav et al. [10] onoptimization of bead geometry in submerged arc bead-on-plate welding uses the Taguchi method. Taguchi’s L25orthogonal array design and the concept of signal to noiseratio were used to derive objective functions to beoptimized within the experimental domain. The objectivefunctions have been selected in relation to parameters ofbead geometry viz. bead width, bead reinforcement, depthof penetration, and depth of HAZ. The Taguchi approachfollowed by Grey relational analysis has been applied tosolve this multiresponse optimization problem. The signif-icance of the factors on the overall output feature of theweldment has also been evaluated quantitatively byanalysis of variance method (ANOVA).

Tarng and Yang [11] described an application of theTaguchi method for the optimization of the weld beadgeometry in the gas tungsten arc welding process. It hasbeen shown that the Taguchi method provides a systematicand efficient methodology for searching the weldingprocess parameters with optimal weld bead geometry.Through ANOVA, it is seen that welding speed, weldingcurrent, and polarity ratio are the important welding processparameters for the determination of weld bead geometry.Also, the confirmation experiments were conducted toverify the optimal welding process parameters.

Chein et al. [12] constructed a feedback linearization andneural network control algorithm, which globally solves thetracking problem with almost disturbance decouplingperformance for a nonlinear system. The new approachdeveloped by the author enables the designer to determinethe interconnect structure among the layers needed tostabilize the overall system without any learning oradaptive algorithms. In order to demonstrate the practicalapplicability, a famous ball-and-beam system has beeninvestigated. Simulation results exploited the fact that theproposed methodology is successfully applied to thefeedback linearization and neural network fields.

Chen et al. [13] studied the feasibility of applyingadaptive fuzzy sliding mode control strategies to reduce thedynamic responses of bridges constructed using a leadrubber bearing (LRB) isolation hybrid protective system.

632 Int J Adv Manuf Technol (2011) 55:631–640

The performance and robustness of these proposed controlmethods are verified by numerical simulation and thatresults demonstrate the viability of presented methods. Theattractive control strategy derived therefrom is applied toseismically excited bridges using LRB isolation.

Chen et al. [14] proposed a Takagi–Sugeno fuzzy modelapproach combined with a parallel distributed compensationscheme for time delay control of the response of a tension legplatform system subjected to an external wave force. Theauthor is given a simulation example to show the feasibility ofthe proposed fuzzy control approach. The example shows thatthe concept of half-circle fuzzy number can be used in fuzzycontrol and the proposed control method can be employed inpractical engineering problems of oceanic structure.

Thus, from literature reviews it is observed that researchactivities have been reported in the field of ultrasonicwelding of ceramics with metals/alloys and metal withmetal/alloys, but these reports do not provide the method tofind out the best suited parameters for joining. Theindustries using ultrasonic welding select the optimumparameters for their products to be welded only by trial anderror and doing so leads to a waste of time and energy.Thus, an approach to find out the best levels of parametersfrom a commonly used range of values of parameters isneeded for industrial application. Also, the finite elementanalysis of these cases seems to be not reported. Therefore,a systematic study in which experimental investigations,followed by finite element modeling, and simulation ofceramics to metal joining and metal to metal joining aretaken up with the following objectives.

1. To find out the optimum levels of parameters that willyield maximum weld strength in the case of aluminumto aluminum welding by conducting experimentsaccording to Taguchi’s design of experiments

2. To carry out preliminary studies on the joining ofalumina (Al2O3) with aluminum using ultrasonic weld-ing and establish the feasibility; later, estimate therange of parameters that can be used in this case

3. To simulate the temperature and stress fields during thejoining of aluminum to aluminum

4. To simulate the temperature and stress fields during thejoining of alumina (Al2O3) to aluminum

5. Compare the experimental observation in either case withthe simulated results and provide adequate explanations

3 Experimental study

3.1 Experimental setup

The welding was carried out using a conventional ultra-sonic metal welding machine (2,500 W, 20 kHz) which is

shown in Fig. 1 with the data acquisition system. A hornmade of titanium alloy was used for this study and an anvilmade of steel with serrations at the top surface. Theparameters that can be varied in this setup are the weldpressure, weld time, and the amplitude of vibration. Thesefactors are selected as the variables for this study. The area ofhorn that comes into contact has serrations similar to the topsurface of the anvil for gripping the workpiece well. Thespecimen (0.3 mm Aluminum sheet) was prepared as perstandard [15] for testing shear strengths of the joint by tensileloading. A universal testing machine was used to determinethe weld strengths. For aluminum to alumina welding, a yarnguide used in the textile machinery made of alumina wasselected as it is an intended application for this study.

The temperature at the interface of the specimen wasmonitored in real time using a data acquisition system.The data acquisition system includes sensors (thermo-couple), a terminal block, DAQ card, and an analyzingsoftware. An SWG 36 Alumal–Cromal (type K) thermo-couple is used in this study which can measure temper-atures from −180°C to +1,300°C. It has a high accuracyof 1.5°C on each side from −40°C to +375°C. Figure 2shows the ultrasonically welded samples of aluminum toaluminum and aluminum to ceramic (Al2O3).

3.2 Design of experiments

Taguchi’s design of experiments methodology was adoptedfor aluminum to aluminum welding to determine theminimum number of experiments that will be required fordetermining the combination of process parameters thatmaximize weld strength. The traditional experimentaldesign methods are too complex and difficult to use. Also,when a large number of parameters have to be investigatedfor significance, a large number of experiments are to be

Fig. 1 Experimental setup of ultrasonic metal welding

Int J Adv Manuf Technol (2011) 55:631–640 633

conducted that are time consuming and costly. Taguchidesigned certain standard orthogonal arrays using whichsimultaneous and independent evaluation of two or moreparameters for their ability to affect the variability of aparticular process can be done using a minimum number oftests.

In this experiment, the objective is to maximize the weldstrength and hence it is the-higher-the-better-type characteris-tic. Regardless of the performance characteristics, greatersignal to noise ratio corresponds to better performance withminimum variation. Therefore, the optimal level of theprocess parameters is the combination of individual parame-ters with levels having the highest signal to noise ratio.

3.3 Identification of control and noise factors

The Taguchi technique uses two factors—control and noisefactors—to identify the optimal process settings that areminimally sensitive to noise. Control factors are those thatcan be controlled during the manufacturing process. Noisefactors are often uncontrolled variables in a process. In thepresent work, welding pressure, welding time, and ampli-tude of the horn were considered as control factors andvaried at three levels as shown in Table 1.

Degree of freedom of weld pressure=3−1=2Degree of freedom of welding time=3−1=2

Degree of freedom of amplitude=3−1=2Total degree of freedom=6

Since the degree of freedom of the selected three levelsof orthogonal array should be greater than or equal to thetotal degree of freedom of all the factors considered, the L9orthogonal array was found to be appropriate. Also, in thepresent work no noise factors are considered. The L9orthogonal array shown in Table 2 contains results of ninetrials of various factor combinations with three repetitionsper trial.

3.4 Signal to noise ratio

In the Taguchi method, the term “signal” represents thedesirable value for the output characteristics and the “noise”represents the undesirable value for the output character-istics. The objective of determining signal to noise ratio isto develop processes that are insensitive to noise. A processparameter setting with the highest signal to noise ratioalways yields the optimum quality with minimum variance.In general, signal to noise ratio signifies the ratio of themean to the standard deviation. In the present context, thisrefers to the deviation of certain quality characteristic fromits desired target value. To calculate the signal to noiseratio, the following formula is used [16]:

S=NHB ¼ �10 log1

r

Xr

i¼1

1

yi2

!

where,

r Number of repetitions in a trialyi Weld strength for ith trial

4 Finite element analysis for temperature and stressdistribution

The finite element analysis was used to determine thetemperature and stress distribution for aluminum to aluminumand aluminum to alumina (Al2O3) welding. The contactbetween the two workpieces is established and transientanalysis is done in order to find out the temperature andstress distribution in the weld. Figure 3 shows the finiteelement model used in the welding of the Al–Al specimen.

A 3D ten-noded solid 187 element was selected forstructural analysis. It has quadratic displacement behavior.The element is defined by ten nodes having three degrees offreedom at all nodes. The element has plasticity, creep,stress stiffening, large deflection, and large strain capabil-ities. The pressure is applied on the top surface of the top

Table 1 Factors and levels for the experiments

Factors Level 1 Level 2 Level 3

Pressure (bar) 1.5 2.0 2.5

Amplitude (μm) 40 45 50

Time (s) 2.00 2.25 2.50

Fig. 2 Welded samples of Al–Al and Al–Al2O3


part on the nodes of the area only where the horn comesinto contact with the workpiece and the resulting displace-ment of the workpiece on top due to the high-frequencyvibration of the horn are fed as inputs for the structuralanalysis. The workpiece at the bottom is arrested in alldegrees of freedom. Transient analysis was carried out withtime steps of 0.1 s for 2.0, 2.25, and 2.5 s of weld time.

A 3D ten-noded solid 87 element was selected forthermal analysis. It has one degree of freedom (temperatureat each node). The element is applicable to 3D, steady-stateor transient thermal analyses. The heat generated due todeformation at the weld interface is calculated with a set ofequations taken from the work done by De Vries [17] and it

is found to be 133×106 W/m2 for the parameters in thesecond experiment. This heat flux value is used as input forthe thermal analysis. Convective boundary condition isused in the areas of the workpieces which are exposed tothe air, and heat flux value is given to the contact area ofthe two workpieces. A transient analysis was done withtime steps of 0.1 s for 2.0, 2.25, and 2.5 s of weld time.

In the case of ceramic and aluminum, both the pieces aremodeled separately and contact is established. The materialproperties considered were Young’s modulus (E), Poisson’sratio (μ), thermal conductivity (k), and density (ρ) forperforming structural and thermal analyses as shown inTable 3 [18].

Table 2 Experimental results

Sample no. Welding pressure (bar) Amplitude (μm) Welding time (s) Response

Interface temperature (°C) Weld strength (106 N/m2)

Trials Average Trials Average

1 2 3 1 2 3

1. 1.5 40 2.00 105 112 106 107.66 1.58 1.80 1.87 1.75

2. 1.5 45 2.25 102 106 110 106.00 2.09 2.31 2.16 2.18

3. 1.5 50 2.50 85 98 102 95.00 3.32 2.60 2.24 2.72

4. 2.0 40 2.25 115 106 112 111.00 2.09 2.45 2.09 2.21

5. 2.0 45 2.50 102 116 108 108.66 4.26 3.32 2.31 3.29

6. 2.0 50 2.00 103 105 110 106.00 1.44 1.95 2.24 1.87

7. 2.5 40 2.50 90 95 105 96.66 3.68 3.39 2.60 3.22

8. 2.5 45 2.00 117 110 102 109.66 3.46 2.81 2.02 2.76

9. 2.5 50 2.25 125 115 122 120.66 1.44 1.80 1.58 1.60

Fig. 3 Meshed model of Al–Alspecimen


5 Results and discussions

Experiments were conducted for joining aluminum work-pieces of 0.3-mm thickness as per Taguchi methodology,and the results are shown in Table 2.

Figure 4 shows the results of temperature distributionfrom finite element analysis (FEA)-based studies whilejoining aluminum to aluminum. The parameters from thefifth experiment are taken for this analysis because thiscombination of parameters yields maximum weld strength(weld time of 2.5 s, weld pressure of 2 bars, and amplitudeof 45 μm). Transient analysis is done with the actual weldtime of 2.5 s. It is inferred that the maximum temperature atthe weld interface is 105.342°C and the experimentallyobserved value is 102°C (an error of 3.17%). Thetemperature measured using thermocouple and the pre-dicted temperature using FEA are in good agreement.

Figure 5 shows the results obtained from FEA-basedstudies for stress distribution. The parameters from the fifthexperiment are taken for this analysis since it yielded thebest weld strength. It is inferred that a maximum stress of34.9×109 N/m2 is obtained at the weld interface anddecreases along its length. FEA was carried out for other

weld times also such as 2, 2.25, and 2.5 s. As the weld timeincreases, the stress levels at the interface also increases,indicating the possibility of achieving higher joint strength.This agrees well with the observation that the increase ofweld time provides improved joint strength. In this work,2.5 s was found to be the optimum weld time.

Figure 6 shows the results of temperature distributionfrom FEA-based studies while joining alumina (Al2O3) toaluminum. Initial condition of bulk temperature for aluminais taken as 250°C because the ceramic piece is preheatedbefore welding. It is inferred that the maximum temperatureat the weld interface is 358.238°C, and the experimentallyobserved value is 345°C (an error of 3.83%). FEA resultsfor Al–Al2O3 joining indicate that higher interface temper-atures are developed. This can be due to the reducedthermal conductivity and preheating of Al2O3.

Figure 7 shows the results obtained from the FEA-basedstudies for stress distribution of alumina (Al2O3) toaluminum using weld pressure of 2 bars, weld time of2 s, and amplitude of 45 μm. The stress level predicted byFEA for the joining of Al–Al2O3 reaches 7.3×109 N/m2.These values are lower than that for metal as Al2O3 isbrittle. These predictions indicate that if good welds could

Table 3 Material properties of alumina and aluminum

Material Young’s modulus (GPa) Poisson’s ratio Thermal conductivity (W/mK) Density (kg/m3)

Alumina 300 0.21 25 3,690

Aluminum 68 0.35 210 2,700

Fig. 4 Temperature distributionon the workpiece (Al–Al)


be obtained for Al–Al2O3 joints, the joint strength may bebelow that of Al–Al joints.

Figure 8 shows the graphical representation of the peaktemperature obtained for Al–Al joint at the weld interfaceduring the welding process. The result shown here is for theeighth experiment and the value recorded is 117°C.

Figure 9 shows the signal to noise ratio plot for weldstrength against the input parameters. The larger is the

better option, which was selected since the focus is tomaximize the weld strength. From the graph, it isinferred that welding pressure of 2.5 bars, amplitude of45 μm, and weld time of 2.50 s result in improvedweld strength. Thus, optimum parameters are foundfrom the selected range of parameters. The signal tonoise ratios and the predicted signal to noise ratios aregenerated using the software which is shown in Table 4,

Fig. 5 Stress distribution onthe workpiece (Al–Al)

Fig. 6 Temperature distributionacross the Al–Al2O3

workpieces


Fig. 8 Temperature distributionfor Al–Al joint (lab view data)

Fig. 7 Stress distribution atthe interface while joiningAl–Al2O3


and it was found that the maximum value of (predicted)signal to noise ratio is 11.2535. For the fifth trial, thisvalue is substituted in the signal to noise ratio equation toget the predicted weld strength by neglecting the numberof trial values. Predicted weld strength is found to be3.42×106 N/m2.

Confirmation experiments were conducted taking theparameters from the fifth experiment. The experiment isrepeated five times using the same parameters and the weldstrengths were found out. The average weld strength wasfound to be 3.30×106 N/m2. Comparing the predicted weldstrength with the actual weld strength, it is found that thepercentage deviation is 3.6%.

Since the accuracy obtained is higher than 95%, it can beconcluded that the design of experiments is a true reflectionof the actual process. The factors and their levels chosensignificantly influence the weld strength of the aluminumsheets. The weld strength can be optimized by controllingthe weld pressure, amplitude of vibrations, and the weldingtime.

6 Conclusions

After carrying out a systematic study as discussed earlier,the following inferences can be made and are presented:

1. Trials on welding of Al–Al were carried out and using adesign of experiment principles, an optimum range ofparameters (weld time, weld pressure and amplitude) isachieved. The weld time of 2.5 s, weld pressure of 2.5bars, and amplitude of 45 μm were found to be the bestvalues to achieve good weld from the selected range ofparameters.

2. Trials to join Al–Al2O3 were made and a weld pressureof 2 bars, weld time of 2 s, and amplitude of 45 μmwere used. As this work is progressing, experimentsusing DOE principles are not presented here.

3. FEA-based studies were carried out for Al–Al jointswherein the temperature and stress at the interfaces wereobtained. It is found from this study that as the weld timeincreases, the stress levels also increase indicating that thejoint obtained at increased weld time show better strength.The temperature measured using thermocouple and thepredicted temperature using FEA are in good agreement.

4. Temperature at the interface for the entire range of datawhile joining Al–Al varied between 95°C and 120°C.As there are severe vibrations in the setup, thecorrelation between the temperature at the interfaceand joint strength could not be established well. Thisstudy is progressing.

5. FEA results for Al–Al2O3 joining indicate that higherinterface temperatures are developed. This can be dueto the reduced thermal conductivity and preheating ofAl2O3.

6. The stress level predicted by FEA for the joining of Al–Al2O3 reaches 7.3×10

9 N/m2 (2 bars weld pressure, 2 sweld time, 45 μm amplitude). These values are lowerthan that for Al–Al joint as ceramics are brittle. Thesepredictions indicate that if good welds could beobtained for Al–Al2O3 joints, the joint strength may

Fig. 9 SN ratio plot for weld strength against the input parameters

Sample no. Weld strength (106 N/m2) SN ratio Predicted SN ratio

1 1.75 4.8608 5.7704

2 2.18 6.7691 6.6534

3 2.72 8.6914 7.8975

4 2.21 6.8878 6.0940

5 3.29 10.3439 11.2535

6 1.87 5.4368 5.3211

7 3.22 10.1571 10.0414

8 2.76 8.8182 8.0243

9 1.60 4.0824 4.9920

Table 4 Signal to noise ratiosand predicted signal to noiseratios

SN signal to noise


be below that of Al–Al joints. Applications whereinstrength requirements are not serious but wear resis-tance is important, these joints can be thought of forfurther studies.

Acknowledgements The authors express their sincere thanks to themanagement and principal of PSG College of Technology, Coimba-tore for providing the necessary support and infrastructure to carry outthis work. We are grateful to AICTE, New Delhi for funding thisresearch work under the research promotion scheme (F. no. 8023/BOR/RID/RPS, 136/2007-08).

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Experimental Studies on Optimization of Process Parameters