Optimisation of friction-stir welding processusing vibro-acoustic signal analysis

Mario Sanchez Orozco1, Emilio Jimenez Macıas2, Angel Sanchez Roca*1,Hipolito Carvajal Fals1 and Julio Blanco Fernandez3

This paper presents a mathematical model to predict the tensile strength (TS) of friction-stir

welded (FSW) AA1050 aluminium alloy joint. This model has been obtained from the optimisation

of the parameters of models developed from vibro-acoustic signals produced during the process.

The multiple response method was used to obtain the optimisation model. During experimental

development, the process parameters selected to be assessed were rotation speed (RS), travel

speed, and tool profile (TP). The TS of the welded joint and the root mean square (RMS) value of

the vibro-acoustic signal in the frequency range of 3?2–6?4 kHz were analysed as response

variables. In order to validate the model, the optimal values of the factors and responses are

presented, showing their agreement with the experimental values obtained. This model can be

used to develop and optimise the parameters of the automatic control of the FSW process, based

on the vibro-acoustic signal generated.

Keywords: Friction-stir welding, Optimisation, Vibro-acoustic signal, Signal processing, Wavelet transform

IntroductionFriction-stir welding (FSW) is a solid-state weldingprocess that is still emerging. The process was developedby the Welding Institute (TWI) in the United Kingdomin 1991.1 Friction-stir welding is a versatile, energy-efficient, and environmentally friendly joining technique,which uses a non-consumable rotating tool harder thanthe base metal.2

The introduction of the FSW process has diversifiedthe applications of the aluminium alloys consideredimpossible or difficult to weld by conventional fusionwelding processes, as in the case of the aluminium alloyAA1050 H24. The main advantage of this technologylies in the possibility of obtaining defect-free joints andthe excellent mechanical properties of materials thatusually present welding problems when they are weldedby conventional fusion methods.3 The FSW process hasbeen mainly applied in a variety of aluminium alloyswith applications in aeronautics, manufacturing of shipsor fuel tanks, and in the food industry, among others.3

The FSW process involves complex material flow andplastic deformation. Welding parameters such as toolrotation speed, travel speed, tool profile (TP), and jointdesign, exert a significant effect on the flow patterns ofthe material and temperature distribution, influencingalso the micro-structural evolution of the material.4

In the FSW process, as the rotation speed (RS)increases, increases the heat generation. Therefore, thetool rotation speed should be optimised for maximummechanical properties of the resulting welded joint. Onthe other hand, as the welding speed (WS) increases, thewidth of the stirring zone and the maximum deforma-tion values decrease and the location of the greatestdeformations gradually move from the advancing sidetowards the retreating side of the joint. It is also notedthat the tensile strength (TS) significantly decreaseswhen the WS increases;5 therefore, this variable must beoptimised in order to obtain the best properties of TS inthe FSW joints.

During the last decade, the combination of increas-ingly more advanced numerical simulation software withhigh computational power has resulted in models forFSW. As a result, an optimisation of certain processparameters has been possible, which has led to betterperforming FSW products. However, most of theseoptimisation studies do not go well beyond manualiterations or limited automation.6

One of the most frequently used methods is the well-known finite element method (FEM)7–9 however, withthe use of these methods it is difficult to calculate someparameters like momentum, force, and temperature. Toavoid these difficulties or troublesome processes, someauthors have adopted the particle method that uses theLagrangian approach to calculate the mentioned termsautomatically.7

Monitoring vibro-acoustic signals is a non-destructiveevaluation technique for real-time applications. It ischaracterised by a more accurate representation of thebehaviour of the phenomenon, but it requires complexanalysis. The scientific literature shows the application

1Faculty of Mechanical Engineering. University of Oriente, Ave. LasAmericas s/n; CP 90900, Santiago de Cuba, Cuba2Electrical Engineering Department, University of La Rioja, Spain3Mechanical Engineering Department, University of La Rioja, Spain

*Corresponding author, email asroca@yahoo.com

� 2013 Institute of Materials, Minerals and MiningPublished by Maney on behalf of the InstituteReceived 12 March 2013; accepted 30 April 2013DOI 10.1179/1362171813Y.0000000134 Science and Technology of Welding and Joining 2013 VOL 18 NO 6 532

of non-destructive techniques in mechanical and man-ufacturing processes.10–15

Soundararajan et al.10 determined the correlationbetween the loss of contact of the tool with the materialbased on the characteristics of the acoustic emission(AE) signal using the fast fourier transform, the shortterm fourier transform, and the discreet wavelet trans-form (DWT). The authors investigated the possibility ofapplying AE techniques in monitoring the FSW process.They also showed that in the frequency domain, the AEsignal is sensitive to changes in the tool penetrationdepth.

Suresha C. N. et al.11 confirmed the application ofvibro-acoustic signals in the analysis of the FSWprocess. The authors developed a conceptual model tocorrelate the FSW process parameters, the TP, thematerial flow pattern, the microstructures, and themechanical properties.

The literature consulted focuses on obtaining opti-mum mechanical properties of the welded joint from theoptimisation of the FSW process parameters.16,17 Thereare no publications reporting the application of optimi-sation methods from the characteristics of the vibro-acoustic signal emitted by the FSW process.

The present paper introduces the multiple responseoptimisation method, which is applied to develop amathematical model in order to obtain the optimalparameters of the FSW process for aluminium alloysAA1050H24, from the characteristics of vibro-acousticsignals and mechanical properties of the welded joint.

Materials and methodsFor the development of the experiments, AA1050 H24aluminium sheets were used. This alloy is known for itselectrical conductivity and is commonly used in the carindustry, in the manufacture of equipment for chemicaland food industries, light reflectors, and heat exchangedevices. Their chemical composition and mechanicalproperties are shown in Table 1. Plates of 3 mmthickness were used with the following dimensions:200 mm (L) and 100 mm (W).

The FSW process was performed using a conventionalCNC machine with three-axis position control. Thealuminium sheets were butt welded in a flat position,affixing the sheets in position with clamps, with thewelding direction normal to the rolling direction. Theexperiments were performed according to the experi-mental setup shown in Fig. 1.

The tool material used was steel AISI D2, thermallyimproved. The experiment was performed with twodifferent TPs: one with conventional grooved cylindricalprobe (Tool 1) and the other with the cylindrical probewith horizontal flutes (Tool 2). The shoulder profile forthese tools was concave and similar for both designs.Probe diameters and shoulders were 3 and 10 mm,respectively (Fig. 2).

An on-line monitoring system was implemented,comprising of two piezoelectric accelerometers placedperpendicularly on the upper surface (Z-axis) and thelateral surface (Y-axis) of the support plate (Fig. 1). Thesensors were fixed to the support plate with the purposeof making the tests as similar as possible to realindustrial conditions. A threaded coupling was usedwith the aim of ensuring accurate vibro-acoustictransduction between the support plate and the sensor.

The acceleration values of the vibro-acoustic signal inthe Z and Y directions were taken using the NI USB-9234 instrument connected to a computer. All signalswere sampled at 51?2 kHz and processed using softwarespecifically designed for this purpose.

According to the literature consulted,4,19,20 factorswith a predominant influence on the TS of the FSW ofaluminium alloys are: tool probe profile, tool rotationspeed, travel speed, and normal force. In this work, thenormal force was constant in all experimental condi-tions. Friction-stir welding process parameters used tojoin the material are shown in Table 2.

To determine the working range of the factors listedabove, preliminary tests were developed; as a result,practical operating limits of the FSW process para-meters were selected. The experiment design used wasfactorial. Table 3 shows the 10 sets of coded conditionsused to design the matrix. To reduce experimental error,

Table 1 Chemical composition (% weight) and mechanical properties of the base metal

Elements Si Fe Cu Mn Mg Zn Ti Al Others0?25 0?40 0?05 0?05 0?05 0?07 0?05 99?05 0?03

Mechanical properties Tensile strength (MPa) Elongation (%) Vickers hardness (HV)145 5 33

1 Schematics of experimental setup

Orozco et al. Vibro-acoustic signal for the optimization of the friction-stir welding process

Science and Technology of Welding and Joining 2013 VOL 18 NO 6 533

three replicas were performed for each experiment with thesame conditions. The average of the four runs of resultsobtained for the dependent variables is also shown.

The test specimen for the TS tests were madeaccording to the standards of the American Welding

Society (AWS D17?3/D17?3M: 200X), ‘Specification forFriction Stir Welding of Aluminium Alloys forAerospace Hardware’. Figure 3 shows a schematic ofthe method to obtain test specimen from the applicationof this standard.

2 Tool profiles: a Grooved cylindrical probe; b Cylindrical probe with horizontal flutes [13]

Table 2 Parameters of the friction-stir welding (FSW) process

Parameter Notation Unit

Levels

(21) (0) (z1)

Tool rotation speed Rotaion speed (RS) rev min21 450 710 1120Travel speed WS mm min21 40 63 100Tool profile TP – Tool 1 – Tool 2

Table 3 Coded matrix design and experimental results

Experiment number

Factors

Tensile strength (TS) (MPa) RMSD3 (ms22)RS WS TP

1 0 0 21 9 900 0?80082 21 z1 21 13 200 0?87763 21 21 z1 * 0?74374 z1 21 21 5 400 1?19335 z1 21 z1 9 275 1?12566 21 21 21 10 750 0?73147 z1 z1 z1 3 400 1?07538 z1 z1 21 4 550 1?17839 0 0 z1 8 075 1?0222

10 21 z1 z1 3 950 0?7216

* No welded joint was achieved

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Science and Technology of Welding and Joining 2013 VOL 18 NO 6 534

The TS of the joints welded by FSW was assessed byusing a universal testing machine, coupled to a comput-ing module where the measurable parameters arerecorded for each event.

Feature extraction of vibro-acoustic signalTo develop a proper vibro-acoustic signal processing itis necessary to select the accurate frequency range thatprovides information about the process behaviour. Inthis study, the statistical parameters of vibro-acousticsignals were used along with their DWT to obtain thedata of the mathematical model. The root mean square(RMS) value is, generally, the most used index for thestudy of mechanical processes since it is directly relatedto the signal energy.

Discreet wavelet transform has been an effective toolto decompose non-stationary signals such as vibro-acoustic signals. Discreet wavelet transform allows theuse of long time intervals when more precise informationis required in the low frequencies and small regions athigh frequencies.

Chen et al.21 demonstrated the validity of the analysisof the DWT for monitoring the AE in the FSW processof the aluminium alloy 6061. The authors showed theoccurrence of sudden significant changes in the fre-quency band in the moment when the probe waspenetrating the tool or retracting out of the weldedjoint; and also when the shoulder made contact or wasseparated from the plates.

In this paper, Daubechies wavelets of order eight havebeen employed in order to decompose signals atdifferent scales to obtain specific frequency bands usefulfor the analysis. In this case, the application of the DWTis equivalent to the result of passing vibro-acousticsignals through a filter with bandwidths of [0–100 Hz],[100–200 Hz], [200–400 Hz], [400–800 Hz], [800 Hz–1?6 kHz], [1?6–3?2 kHz], [3?2–6?4 kHz], [6?4–12?8 kHz],and [12?8–25?6 kHz].

Each of the signals obtained was characterisedstatistically in the different frequency bands resultingfrom the decomposition by the DWT. Finally, a test ofstatistical significance was conducted with the purpose ofextracting the statistical parameters that showed signifi-cant differences to be used in the mathematical model.

Different methods are used to determine whichcharacteristics of the signals would be used in theoptimisation model. In this paper, the analysis of

variance (ANOVA) was used to investigate the statis-tical parameters of the signal that are significantlyaffected by changes in the welding parameters. Theresult of applying ANOVA to all vibro-acoustic signalsshows that only two features were influenced by changesin the parameter levels. In this case, the median valuesand the RMS.

The F-test was used to determine which of the vibro-acoustic characteristics were affected by changes in the para-meters of the FSW. For the analysis, the frequency bandsassociated to noise were not taken into account. Allcalculations were performed for a confidence level of 95%.22

Development of the mathematical modelIn order to obtain a function that would group severalfactors for more than one response simultaneously, theoptimisation method of multiple responses was applied;it allows determining the configuration of the experi-mental factors for the desired characteristics of one ormore responses through the construction of the desir-ability function, based on the values of the responsevariables.23 The response function Y, which includes thevariable TS and the feature of the vibro-acoustic signalemitted by the process (RMS), will be a function of theRS, the WS/travel speed and the TP, and can beexpressed by (1)

Y (TS,RMS)~f (RS,WS,TP) (1)

In order to combine multiple responses in a singlefunction, first the desirability function is defined for eachresponse. This function expresses the desirability of aresponse value equal to Y on a scale from 0 to 1. Thisfunction takes three forms depending on whether theresponse should be maximised, minimised or reach atarget value. In this particular case, the idea is tomaximise the response of the TS; therefore, thedesirability function is defined by (2)

d1~

0 yvlow

y{low

high{low

!s

lowƒyƒhigh

1 ywhigh

8>>>><>>>>:

(2)

In this case, y is the predicted value of the responsevariable, low is the value below which the response iscompletely unacceptable, and high is the value abovewhich the desirability function is a maximum. Theparameter s defines the shape of the function. For s51,the desirability function behaves linearly with a value of0 in the low value; and 1 in the high value.

For the purposes of this paper, the idea is that the RMSresponse remains approximately equal to a target value;therefore, the desirability function is defined by (3)

d2~

0 yvlow

y{low

target{low

!s

lowƒyƒtarget

y{high

target{high

!t

targetƒyƒhigh

0 ywhigh

8>>>>>>>>>><>>>>>>>>>>:

(3)

The implied term s acts as the highest desirability functionto predict response values lower than the target value,

3 a Dimensions of tensile specimen b Schematics of

welding

Orozco et al. Vibro-acoustic signal for the optimization of the friction-stir welding process

Science and Technology of Welding and Joining 2013 VOL 18 NO 6 535

while the implied term t acts as the desirability function topredict response values higher than the target value; inthis case t51.

To combine the desirability function of n responses, asingle composite desirability function D is created. If allthe response variables are considered equally important,then the composite function is the geometric mean of thedesirability functions, calculated by (4)

D~

ffiffiffiffiffiffiffiffiffiffiffiPn

i~1di

n

r~

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffid1:d2:d3 . . . dn

np

(4)

Analysis of resultsFor the application of the optimisation method pro-posed, all vibro-acoustic signals were characterised, inorder to extract the features of these signals generated bythe FSW process and correlate them with the processparameters.

Noise CharacterisationFigure 4 shows diagrams in the time domain for thevibro-acoustic signal corresponding to condition 2(RS5450 rev min21, WS5100 mm min21 and Tool 1)for both components (axes Y and Z), and the noise

signal acquired, without contact between the tool andthe workpiece.

Noise analysis and characterisation was performed inboth axes (Y and Z). Comparing Fig. 4a and c, it isnoted that the temporal signal levels on the Y-axisacquired during the process for condition 2 (Fig. 4c)undergoes an increase in amplitude with respect to thenoise signal (Fig. 4a). For the Z-axis, the temporalsignal levels of noise and the signal acquired during theprocess, do not present significant differences, over-lapping the useful signal and noise. The low level of thesignal corresponding to the Z-axis can be associatedwith the constant force applied to the workpiece whileperforming the tests. For the remaining experiments, thesignal behaviour on both axes was similar. Given thispreliminary result, henceforth only vibro-acoustic sig-nals on the Y-axis were analysed.

Applying wavelet transform to obtain newfeatures of vibro-acoustic signalsVibro-acoustic signals generated by the FSW process arecomplex;14 therefore, mere analysis of the characteristicsof the temporal signal is insufficient to obtain therelationship between the vibro-acoustic signals and theprocess parameters. In this work, the inclusion of

4 Time diagrams: a and b noise in both axes; c and d vibro-acoustic signal in both axes for condition 2

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additional features of the vibro-acoustic signals wasconsidered, based on time–frequency properties, andusing the DWT to obtain additional statistical char-acteristics related to different frequency bands of thesignals.

The result of the DWT is a series of decomposedsignals corresponding to different frequency bands. Inthis study, the decomposition with the DWT is appliedusing eight levels and a mother wavelet Daubechies(Db5). The graphs in Fig. 5a and b represent the DWTof the vibro-acoustic signal for conditions 2 and 8respectively. In this case, the conditions 2 and 8 wereselected as they are representative conditions of weldedjoints on which the extreme values of TS were obtained,TS5132 MPa (condition 2) and TS545?5 MPa (condi-tion 8), for Tool 1.

In Fig. 5, all the frequency bands of the decomposedvibro-acoustic signals corresponding to conditions 2 and8 can be seen. A simple view of the figures shows thedifferences between the signals for the same frequencyband.

Selection of features of vibro-acoustic signalsThe statistical characterisation of vibro-acoustic signalswas developed in this piece of research. As a result of thestatistical analysis applied, a total of 10 features of thesignal were selected, all of which have significant influenceon the variation of process parameters: two extracted

from the signal in the time domain and eight from thestatistical parameters based on decomposed signals by theDWT. In this case the vibro-acoustic features selected foroptimisation analysis are shown in Table 4.

Table 4 shows that the RMS for both, the vibro-acoustic signal and the decompositions of DWT, issensitive to the change in parameters of the FSW,because the RMS is directly linked to the signal energy.It is also particularly important to note that the mediandoes not undergo significant changes when unexpectedchanges in the rotation and travel speed appear;however, it is sensitive to changes in the TP.

In Fig. 6, the results of the ANOVA method for theRMS can be seen along with the median based on theRS, WS and TP respectively, extracted from the DWTdecomposition.

Figure 6 shows the results of ANOVA methods forthree representative features selected of vibro-acousticsignals; in this case, specifically three decompositions byDWT. In all cases, the p-value of the F-test was less than0?05; hence, it can be asserted that there is a statisticallysignificant difference in the vibro-acoustic features meanbetween one level of the process parameter and the otherone, with a reliability of 95?0%.

Process optimisationThe coefficient of determination (r2) is used to establishhow close the experimental results are with respect to

5 Wavelet transforms, a condition 2; b condition 8

Table 4 Selected features for optimisation analysis

Vibro-acoustic features Symbol Vibro-acoustic features Symbol

1 Median of the global vibro-acoustic signal MV 6 RMS of D7 Discreet wavelet transform (DWT) RMSD7

2 RMS of the global vibro-acoustic signal RMSV 7 RMS of D5 DWT RMSD5

3 Median of A8 DWT MA8 8 RMS of D4 DWT RMSD4

4 RMS of A8 DWT RMSA8 9 RMS of D3 DWT RMSD3

5 RMS of D8 DWT RMSD8 10 RMS of D1 DWT RMSD1

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Science and Technology of Welding and Joining 2013 VOL 18 NO 6 537

those predicted by the model; in the case of models withseveral independent variables it is more suitable to usethe r2 adjusted.23

For the application of the optimisation method formultiple responses, first, the models for each responsemust be obtained; this implies that, for the variable TSand the selected feature of the vibro-acoustic signal(RMSD3), a higher value of the adjusted coefficient ofdetermination r2 was obtained.

The mathematical model (5), developed to predict thevalues of TS in the FSW of aluminium AA1050 H24 is

TS~119:24{13:51TP{0:019RS{0:0052WSz

0:051TP:RS{0:52TP:WS{

0:000505RS:WS (MPa) (5)

In this case the value of the adjusted coefficient ofdetermination r2 was 91?4%, indicating that less than 9%of total variations are not explained by this model.

In the case of the variable RMSD3 the modelexpressed in (6) was obtained.

RMSD3~0:37z0:086Hz0:00072VRz0:002VA{

0:000028H:VR{0:001H:VA{

0:0000023VR:VA (m s{2) (6)

The value of the adjusted coefficient of determination ofr2 for this model was 90?6%. This implies that this model

does not explain less than 10% of the total variations ofthe RMSD3 response, which, according to the literatureconsulted, is an acceptable value. Figure 7 shows thevalidation of the model obtained for the evaluatedresponses. As it can be appreciated in that figure, theobserved and predicted values were close to each other.

Figure 8 shows the response surface and contourgraphs for both models, evaluated for optimal tool(Tool51)

As shown in Fig. 8a, the maximum value of TS isobtained for a RS of 450 rev min21 and a travel speed of100 mm min21 (condition 2); for these same parameters,values of around 0?85 m s22 for the vibro-acousticfeature RMSD3 were obtained (Fig. 8b).

The contour graph in Fig. 8c shows that the changesin the TS are more sensitive to the variation of RS than totravel speed. It can also be seen how the TS decreases withincrease in the RS, obtaining maximum values of TS forthe RS range between 450 rev min21 and 525 rev min21

and travel speeds between 65 mm min21 and100 mm min21. Figure 8d shows a similar behaviour forthe variable RMSD3, which for the aforementionedparameter range, takes values between 0?8 and 0?85 m s22.

Figure 9 shows the macrography of the cross sectionof the welded joints, perpendicular to the weldingdirection for two representative conditions. Figure 9acorresponds to condition 2 (RS5450 rev min21, WS5

100 mm min21 and TP51); in this case, it is worthnoting that obtaining good quality welding, free of

6 Influence of the friction-stir welding (FSW) in the decomposed signal by discreet wavelet transform (DWT)a tool rota-

tion speed; b travel speed, c tool profile (TP)

7 Correlation graph a tensile strengh; b RMSD3

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Science and Technology of Welding and Joining 2013 VOL 18 NO 6 538

defects, coincides with optimal parameters predicted bythe models shown above.

In Fig. 9b, corresponding to condition 8 (RS5

1120 rev min21, WS5100 mm?min21 and TP51), it can

be seen that the appearance of a groove or worm-holedefect, caused by high RS of the tool, causes excessiveheat input per length unit and insufficient vertical flow ofthe plasticised metal.16 The result is a welding joint of lowTS, and hence poor quality.

Finally, the procedure consists of applying theoptimisation method for multiple responses, which willallow determining the combination of the experimentalfactors, RS, WS, and TP. This simultaneously optimisesthe responses, TS and RMSD3. For this particular case,the objective is to maximise the response TS and ensurean RMSD3 target value. (7) shows the optimisedfunction that is a result of the maximum value of thecomposite desirability function.

8 a and b response surfaces; c and d contour graphs

9 Macrography of the representative conditions a condition

2 (RS5450 rev min21, WS5100 mm min-1 and TP51); b

condition 8 (RS51120 rev min21, WS5100 mm min21

and TP51)

10 Composite desirability function

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Science and Technology of Welding and Joining 2013 VOL 18 NO 6 539

fopt~max Dð Þ~maxffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiTS(standard)

:RMSD3(standard)

p� �(7)

Where:fopt: optimised functionTS(standard) -: standardised TSRMSD3(standard): standardised RMSD3

Figure 10 shows the graphic behaviour of D, eval-uated at each point of the experimental design for theoptimal tool (TP51).

Figure 10a shows the response surface for optimisedfunction. It can be seen that the maximum value of desi-rability is obtained for condition 2 (RS5450 rev min21,WS5100 mm min21 and TP51). From the contourgraph of Fig. 10b, it can be asserted that for the RSrange between 450 and 500 rev min21 and travel speedsbetween 90 and 100 mm min21, the optimal values of TSin the FSW joint of the AA 1050H24 will be obtained.The combination of factor levels that maximises thedesirability function in the specified region, that is, thecombination of factors in which the optimal overallvalues are achieved for the response variables, is shown inTable 5.

The mathematical model obtained by the method ofoptimisation for multiple responses allows knowing thevalues of the vibro-acoustic parameter RMSD3, corre-sponding to the optimal values of TS of the welded jointby FSW. Since RMSD3 is directly related to the energyproduced during the process, and therefore related tothe input parameters RS, travel speed, and TP, it can beasserted that it is possible to implement an automaticcontrol system of these parameters that will yield goodquality welding, based on the features of the vibro-acoustic signal emitted during the process.

ConclusionsThis study has evaluated the possibility of usingmathematical models of optimisation for multipleresponses to predict FSW process parameters using thevibro-acoustic signals generated. It can be concludedthat:

1. A mathematical model that considers the para-meters of the process and features of the vibro-acousticsignal has been developed, which allows predicting theTS of the FSW joint of the aluminium alloy 1050 H24,with a 95% level of confidence.

2. The desirability function was used to optimiseprocess parameters and responses (TS and valuesRMSD3). The predicted optimised values match thevalues experimentally obtained.

3. The RMS value of the vibro-acoustic signal is oneof the key factors influenced by process parameters.

4. Changes in TS are more sensitive to variation inthe RS than in travel speed; the TS significantlydecreases with the increase in the tool rotation speed.

5. The statistical characterisation in the frequencyrange of 3?2–6?4 kHz of vibro-acoustic signals providesuseful online information about the changes of the FSWprocess parameters.

6. The analysis techniques based on the WaveletTransform can be used as an effective tool for modellingthe FSW process parameters.

Acknowledgements

This paper has been partially supported by the project ofthe Spanish Government ‘DPI2011-25007. ‘Friction StirWelding of dissimilar materials. Characterization byacoustic emission techniques and artificial intelligence’.

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684; 2004, New York, John Wiley & Sons, Inc.

Table 5 Optimal values of the factors and responses

Factor Response Desirability

Tool Rotation speed (rev min21) Travel speed (mm min21) Tensile strength (MPa) RMSD3 D

1 450 100 129?2 0?8254 0?97

Orozco et al. Vibro-acoustic signal for the optimization of the friction-stir welding process

Science and Technology of Welding and Joining 2013 VOL 18 NO 6 540