A study on surface roughness in abrasive waterjet

j o u r n a l o f m a t e r i a l s p r o c e s s i n g t e c h n o l o g y 202(2008)

574–582

j o u r n a l h o m e p a g e :w w w.e l s e v i e r.c o m /l o c a t e /j m a t p r o t e

A study on surface roughness in abrasive waterjet machining process using arti?cial neural networks and regression analysis method

Ulas ?C ?aydas ??,Ahmet Hasc ?al?k

University of Firat,Technical Education Faculty,Department of Manufacturing,Elazig,Turkey

a r t i c l e

i n f o Article history:

Received 11June 2007Received in revised form 5September 2007Accepted 1October 2007

Keywords:

Abrasive waterjet machining Surface roughness Arti?cial neural network Regression analysis

a b s t r a c t

In the present study,arti?cial neural network (ANN)and regression model were developed to predict surface roughness in abrasive waterjet machining (AWJ)process.In the devel-opment of predictive models,machining parameters of traverse speed,waterjet pressure,standoff distance,abrasive grit size and abrasive ?ow rate were considered as model vari-ables.For this purpose,Taguchi’s design of experiments was carried out in order to collect surface roughness values.A feed forward neural network based on back propagation was made up of 13input neurons,22hidden neurons and one output neuron.The 13sets of

data were randomly selected from orthogonal array for training and residuals were used to check the performance.Analysis of variance (ANOVA)and F -test were used to check the validity of regression model and to determine the signi?cant parameter affecting the surface roughness.The statistical analysis showed that the waterjet pressure was an utmost param-eter on surface roughness.The microstructures of machined surfaces were also studied by scanning electron microscopy (SEM).The SEM investigations revealed that AWJ machining produced three distinct zones along the cut surface of AA 7075aluminium alloy and surface striations and waviness were increased signi?cantly with jet pressure.

1.Introduction

Manufacturing industry is becoming ever more time-conscious with regard to the global economy,and the need for rapid prototyping and small production batches is increas-ing.These trends have placed a premium on the use of new and advanced technologies for quickly turning raw materials into usable goods;with no time being required for tooling (https://www.360docs.net/doc/3714762552.html,/0-87849-918-0.html ).Abrasive waterjet (AWJ)machining technology has been found to be one of the most recent developed advanced non-traditional methods used in industry for material processing with the distinct advantages of no thermal distortion,high machining

Corresponding author .Tel.:+904242370000/4229;fax:+904242184674.E-mail address:ucaydas@?https://www.360docs.net/doc/3714762552.html,.tr (U.C ?aydas ?).

versatility,high ?exibility and small cutting forces (Hasc ?alik et al.,2007).Because of these capabilities,it makes an important contribution to machining materials with higher performance and more cost-effective than traditional and some non-traditional machining processes.AWJ is widely used in the machining of materials such as titanium,steel,brass,alu-minium,stone,inconel,any kind of glass and composites (Akkurt et al.,2004).The intensity and the ef?ciency of the machining process depend on several AWJ process parame-ters (Momber and Kovacevic,1998;Hashish,1991).They are classi?ed as hydraulic,abrasive,work material and cutting parameters.Surface roughness,which is used to determine and to evaluate the quality of a product,is one of the major

j o u r n a l o f m a t e r i a l s p r o c e s s i n g t e c h n o l o g y202(2008)574–582575

quality attributes of an AWJ machining product.The use of arti?cial neural networks(ANNs)in machining research has been extensive and multifaceted.The literature is rich with the relevant investigations on choosing best machining param-eters for low surface roughness during different machining processes.Lee et al.(1998)used abructive network model-ing for drilling process for predicting the surface roughness. Ozc?elik et al.(2005)have used a statistical three-level full factorial experimental design with81runs to optimize the surface roughness in end milling Inconel718.A predictive model of surface roughness was created using a feed forward ANN exploiting experimental data.Spedding and Wang(1997) compared the response surface model with neural network by using machining parameters on surface roughness during wire electrical discharge machining.Erzurumlu and Oktem (2007)have developed an ANN and response surface model to predict surface roughness in milling mould parts.A statistical design consisting of243experiments was adopted to collect the Ra measurement data.An effort has been made to pre-dict surface roughness in end milling process by using ANN model based on design experiments Oktem et al.(in press). Ilhan et al.(1992)have used a[28.3]factorial design with a total number of718experiments to establish the different objective functions corresponding to surface roughness of the elec-trochemical surface grinding process.Nabil and Ridha(2006) developed an approach combining the design of experiments (DOE)and the ANN methods to establish accurate models for ground surface roughness parameters prediction.Risbood et al.(2003)utilized a neural network to predict surface rough-ness and dimensional deviation based on the cutting forces and vibrations in turning of rolled steel bars containing about 0.35%carbon.Kumar and Choudhury(in press)compared experimental investigations and modeling of wheel wear and surface roughness during electro-discharge diamond grind-ing process using DOE and ANN.31experiment were carried out based on the central composite rotatable design to obtain surface roughness for different combination of machining parameters.Analysis of the data indicated that to achieve bet-ter surface?nish of workpiece(HSS)pulse current,duty ratio and grain size should be chosen from the lower ranges while higher value of wheel speed should be selected.Chien and Chou(2001)presented an ANN approach to predict surface roughness of the AISI304stainless steel,the cutting forces and the tool life.Then the genetic algorithm was introduced to ?nd the optimum cutting conditions for the maximum mate-rial removal rate under the constraints of the expected surface roughness.

The literature reveals that ANNs?nd many applications to predict surface?nish through different machining pro-cesses,but very little effort is reported on the use of ANNs in AWJ machining process.Additionally,applied experimen-tal methods are requiring large number of trials when number of machining parameters increase.On the other hand,the regression analysis method has successfully been used for obtaining the machining performance by many researchers. However for obtaining the suitable mathematical form,a great deal of experimental data is necessary.The Taguchi method, which is one of the fractional factorial designs,uses a spe-cial design of orthogonal arrays to study the entire parameter space with small number of experiments only(Ko et al.,1999).Jegaraj and Babu(2007)attempted to make use of Taguchi’s approach and analysis of variance(ANOVA)using minimum number of experiments for studying the in?uence of param-eters on cutting performance in AWJ machining considering the ori?ce and focusing tube bore variations to develop empir-ical models.Thus,Taguchi approach was applied to this study.Data for teaching the ANNs can be selected from the orthogonal array table with a small number of trial and the characteristics as output data of the ANN can be trans-formed by the Taguchi method for obtaining more accurate performance.Therefore,ANNs having properties as learning capability and adaptation,working property with a few data and high speed working have been used in this paper.At the end of study,both the ANN and regression analysis results were compared with experimental data.

2.Surface roughness

Surface roughness is a measure of the technological quality of a product and a factor that greatly in?uences manufac-turing cost.It describes the geometry and surface textures of the machined parts(Nalbant et al.,2007).There are several ways to describe surface roughness,such as rough-ness average(R a),root-mean-square(rms)roughness(R q) and maximum peak-to-valley roughness(R y or R max),etc. (https://www.360docs.net/doc/3714762552.html,/smg/parameters.html).Ra is de?ned as the arithmetic value of the pro?le from centreline along the sampling length.It can be expressed by the following mathe-matical relationship(Ozc?elik et al.,2005):

R a=

|y(x)||dx|(1)

where L is the sampling length,y is pro?le curve and x is the pro?le direction.The average surface roughness R a is mea-sured within L=0.8mm.

3.Arti?cial neural networks(ANNs)

Neural networks,as used in arti?cial intelligence,have tradi-tionally been viewed as simpli?ed models of neural processing in the human brain.It is accepted by the most scientists that the human brain is a type of computer.The origins of neural networks are based on efforts to model information process-ing in biological systems,which may rely largely on parallel processing as well as implicit instructions based on recog-nition of patterns of“sensory”input from external sources (https://www.360docs.net/doc/3714762552.html,/wiki/Neural network).

Human body consists of trillions of cells.A portion of them is the nerve cells called“neurons”.These neurons have different shapes and sizes(Tosun and Ozler,2002).A neuron collects signals from others through?ne structures called dendrites.The neuron sends out spikes of electrical activity through a long,thin stand known as axon,which splits into thousands of branches.At the end of each branch, a structure called a synapse converts the activity from the axon into electrical effects that inhibit or excite activity in the connected neurons.When a neuron receives excitatory input

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that is suf?ciently large compared with its inhibitory input,

it sends a spike of electrical activity down its axon.Learn-

ing occurs by changing the effectiveness of the synapses

so that the in?uence of one neuron on another changes

(https://www.360docs.net/doc/3714762552.html,/nd/surprise96/journal/vol4/csll/

report.html).

3.1.Backpropagation algorithm

Even though several learning methods have been developed,

the back propagation(BP)method has been proven to be suc-

cessful in applications related to surface?nish prediction(Tsai

et al.,1999;Azouzi and Guillot,1999;Zouaghi et al.,1996).In

this study,BP learning algorithm,which has a unique learning

principle,generally called delta rule,is used.Fig.1depicts a

schematic illustration of BP networks.The three layer of the

network architecture include the input layer,hidden layer and

output https://www.360docs.net/doc/3714762552.html,yers include several processing units known

as neurons.They are connected with each other by variable

weights to be determined.In the network,the input layer

receives information from external source and passes this

information to the network for processing.The hidden layer

receives from the input layer,and does all information pro-

cessing.The output layer receives processed information from

the network,and sends the results to an external receptor

(Singh et al.,2006).In the network,each neuron receives total

input from all of the neurons in the proceeding layer as:

net j=

i W(n)

X i(n?1)(2)

where net j is the total or net input,X j(n)is the output of the

node j in the n th layer,and W(n)

ji represents the weights from

node i in the(n?1)th layer to node j in the n th layer.

A neuron in the network produces its input by processing the net input through an activation(transfer)function which is usually nonlinear.There are several types of activation func-tions used for BP.However,the sigmoidal activation function is most utilized.Three types of sigmoid functions are usually used,as follows(Liu et al.,2006):

f(x)=

1+e?x

range(0,1)(3) f(x)=

1+e?x

?1range(?1,1)(4) f(x)=

e x?e?x

e x+e?x

range(?1,1)(5)

the weights are dynamically updated using the BP algorithm. The difference between the target output and actual output (learning error)for a sample p is(Tosun and Ozler,2002)

E p=

k=1

(d pk?o pk)2(6)

where d pk and o pk are the desired and calculated output for k th output,respectively.K denotes the number of neuron in output of network.The average error for whole system is obtained by:

E p=

p=1

k=1

(d pk?o pk)2(7)

where P is the total number of instances.For the purpose of minimizing E p,the weights of the inter-connections are adjusted during the training procedure until the expected error is achieved.To adjust the weights of the networks,the process starts at the output neuron and works backward to the hidden layer.The weights in BP based on the delta learning rule can be expressed as follows:

w new

=w old

+ w ij

(8) Fig.1–Schematic illustration of arti?cial neural network model for the surface roughness.

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Table1–Chemical composition of Al7075alloy

Al91.02

Cu 1.65

Mg 2.0

Cr0.23

Zn5

Mn0.1

w ij=?á?E p

?w ij

out j(9)

where out j the j th neuron output.áis the learning rate parameter controlling stability and rate of convergence of the network,which is a constant between0and1.Once the weights of all the links of the network are decided,the decision mechanism is then developed.

4.Experimental studies

4.1.Materials choice

In this study,AA7075-T6wrought alloy(AlZnMgCu1.5)with an ultimate strength of ult=675MPa and yield strength of ult=610MPa was selected.Because of its low speci?c weight and high strength to weight ratio and also its high electri-cal and thermal conductance,this alloy is widely used in industry and in particular in aircraft structure and pressure vessels(Majzoobi and Jaleh,2007).The material stock was pro-vided from ETIALUMINYUM(T urkey).The stock was milled to100mm×100mm×100mm in dimensions under the same machining conditions.The chemical composition of the used material is given in Table1.

4.2.AWJ cutting procedure and design of experiments

For conducting the experiments,an abrasive waterjet cutting machine(WJ-S42E,Germany)was employed.The erodent par-ticle was garnet abrasive.In the cutting tests,the jet was perpendicular to the sample surface.The ori?ce assembly con-sisted of a carbide nozzle insert1mm in diameter.In the experimental plan,the most dominant process parameters such as traverse speed(V),waterjet pressure(P),standoff dis-tance(h),abrasive grit size(d)and abrasive?ow rate were varied at three levels.The?ve process parameters and their factor levels are summarized in Table2.In order to measure the average surface roughness(Ra)of AWJ machined samples, a SJ-201portable surface roughness measurement device was used.The measurements were taken at a distance of5mm from the top of the cut surface.A LEO32scanning electron

Table3–Experimental design using L27orthogonal

array and experimental results

Experiment no.V P h d m Surface roughness,

R a(?m)

111111 2.124

211112 2.753

311113 3.352

412221 4.311

512222 4.541

612223 5.123

713331 6.789

8133327.524

9133339.123

1021231 3.575

1121232 4.457

1221233 5.628 13223117.010 ********.535 15223137.893 16231218.121 17231228.312 18231239.163

1931321 4.328

2031322 5.120

2131323 5.852

2232131 6.143

2332132 6.721 24321337.780 25332118.890 26332129.120 273321310.035

microscope(SEM)was used to investigate the machined sur-face.

Normally,one need to conduct35(243)experiments when ?ve factors,each varied at three levels considered,using full factorial experimental design.In order to save on experimen-tal cost and time,Taguchi’s orthogonal array was applied to obtain the surface roughness of the AWJ process.A L27orthog-onal array was found to be appropriate and it was chosen.The layout of the L27orthogonal array and the measured surface roughness values are shown in Table3.Among these data sets in orthogonal array,13data were selected randomly as training data of neural network,and the residuals were used to verify the predicted results.For training,a commercial Microsoft Windows-based ANN software,Matlab Version 6.1(The MathWorks,Natick,MA)was used throughout the study with a P-4personal computer.Several iterations were conducted with different numbers of nodes of hidden layer in order to determine the optimal ANN structure.During pre-trials,the minimum least mean squared error was inter-

Table2–Machining settings used in the experiments

Symbol AWJ cutting parameter Level1Level2Level3

V Traverse speed(mm/min)50100150 P Waterjet pressure(MPa)125175250 h Standoff distance(mm)1 2.54 d Abrasive grit size(?m)6090120 m Abrasive?ow rate(g/s)0.52 3.5

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estingly achieved with22hidden nodes.The learning rate and

momentum values were selected0.9and0.2,respectively.

4.3.Determination of regression analysis model for

surface roughness

Regression analysis method includes the experimental inves-

tigations,mathematical methods and statistical analysis.In

the present investigation,a whole analysis was done using the

experimental data in Table3.A multilinear stepwise regres-

sion analysis was performed to predict the surface roughness

using MINITAB14software.Specially,with a sample of n obser-

vations of the dependent variable Y(R a),the regression model

(Lin et al.,2007)can be expressed as:

R a=ˇ0+

i=1

ˇi X i+

i=1

ˇii X i2+

ˇij X i X j+εi(10)

where k is number of factors(5)ˇ0is the free term,ˇi is the linear effect,ˇii is the squared effect andˇij is the interac-tion effect.The second-order polynomial regression equation representing the surface roughness(R a)can be expressed as a function of AWJ machining parameters such as traverse speed (V),waterjet pressure(P),standoff distance(h),abrasive grit size(d)and abrasive?ow rate(m).Eq.(10)can be rewritten to build the relationship between the AWJ process parameters and surface roughness as follows:

R a=b0+b1V+b2P+b3h+b4d+b5˙m+b11V2+b22P2 +b33h2+b44d2+b55˙m2+b12VP+b13Vh+b14Vd

+b15V˙m+b23Ph+b24Pd+b25P˙m+b34hd

+b35h˙m+b45d˙m(11)

5.Results and discussions

Results of ANN and regression analysis,used to establish input–output relationships in AWJ machining process,are shown and discussed below.

5.1.Estimation of surface roughness by ANN

The optimal neural network architecture used in this study is indicated in Fig.1.The network consists of one input,one hidden and one output layer.Hidden layer has22neurons, whereas output layer has one neuron.13neurons with?ve features have been used as an input of ANN.Iteration number versus mean square error(MSE)is shown in Fig.2.It can be seen that training of neural networks can be achieved quickly. After331cycles of training(Epochs),the training error of net-work reaches stabilization value.The mean error is3.0072% for surface roughness.The error is lower than10%,which show that the well-rained network model takes on optimal performance and has a great accuracy in predicting surface roughness(Ozel and Karpat,2005).The results predicted from the ANN model are compared with experimental

measure-

Fig.2–Iteration number vs.mean square

error.

Fig.3–Comparison of ANN results with experimental measurements.

ments results in Table3for13check sets.Fig.3depicts the comparison of results between them.Good agreement can be seen.

5.2.Estimation of surface roughness by regression analysis

From the results(Table3),the?nal regression model for sur-face roughness obtained is as follows:

R a=?5.07976+0.08169V+0.07912P?0.34221h?0.08661d ?0.34866˙m?0.00031V2?0.00012P2+0.10575h2

+0.00041d2+0.07590˙m2?0.00008V˙m?0.00009P˙m

+0.03089h˙m+0.00513d˙m(12)

note that some interaction terms are removed from full model because of their nonsigni?cant effect.Regression statistics

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579

Table 4–ANOVA test results for regression model Source

Regression 14124.1008.86432182.190.000*Linear 5117.429 2.1593444.380.000*Square 5 5.973 1.1946724.550.000*Interaction 40.6980.17444 3.59

0.038

Residual error 120.5840.04866

Total

124.684

R 2=0.95.DF—degree of freedom.SS—sum of squares.MS—mean square.

Signi?cant.

R 2and R 2Adj are obtained equal to 99.5and 99%,respectively.

The R 2value indicates that the machining parameters explain

99.5%of variance in surface roughness.This value indicates that the presented model ?ts the data very well.The analy-sis of variance (ANOVA)for regression analysis is shown in Table 4.

The p -value shows that the model,linear terms and squared

terms are signi?cant at ?-level of 0.005,whereas inter-action terms seems to have not of signi?cant in?uence on surface roughness.The results predicted by regression model are compared with experimental measurements results in Fig.4.It can be seen from Fig.8that model prediction presents a good agreement with the experimental data.

Fig.4–Comparison of regression model results with experimental measurements.

Fig.5–The relative effect of AWJ parameters on the surface roughness.

It was also statistically studied the relative effect of each AWJ parameters on the surface roughness by using ANOVA and F -test (Appendix A )(Huang et al.,1999).Table 5shows the results of ANOVA for the surface https://www.360docs.net/doc/3714762552.html,rger F value indicates that the variation of the process parameter makes a big change on the surface roughness (Tosun and Cogun,2003)and P denotes its percent contribution on surface roughness.Fig.5shows the relative importance of AWJ parameters used in this study on the surface roughness.As clearly shown from the ?gure,the waterjet pressure has an utmost importance on the surface roughness and the effects of standoff distance and abrasive grit size on surface roughness are insigni?cant.Here,the effect of grit size on surface roughness is surprisingly.No plausible reason is available in the open literature and further investigation is needed for this.Traverse speed is the second ranking factor,whereas the abrasive ?ow rate has a statistic in?uence (7.28%).

https://www.360docs.net/doc/3714762552.html,parison of ANN and regression results for surface roughness

Results of ANN and regression analysis are compared with experiments in Table 3for 13check sets.The comparison results are depicted in Table 6.The maximum test errors for ANN and regression model are about 3.0072and 1.03%,respectively.Both the methods are suitable for estimating surface roughness in an acceptable error ranges.But,the

Table 5–Result of the ANOVA for the surface roughness Machining parameter

Degree of freedom (f )

Sum of squares (SS A )

Variance (V A )

F A 0

Contribution (%)

Traverse speed 222.2111.11 2.6017.81Waterjet pressure 288.3944.2029.2370.89Standoff distance 2 2.84 1.420.28 2.28Abrasive grit size 20.890.440.090.71Abrasive ?ow rate 29.08 4.540.94

7.28Error 24 4.39 2.195

1.03Total

127.8

100

580

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Table 6–Comparison of ANN and regression model results with experimental measurements Experiment no.

Surface roughness (?m)

Experimental measurements

Regression model

ANN model

2 2.75

3 2.62915 2.7445

4 4.311 4.00520 3.95266 5.123 5.42532 5.139287.5247.698157.842110 3.57

5 3.66819 3.877112 5.628 5.55233 5.0574147.5357.365487.4909168.1217.964557.9742189.1639.213308.645420 5.120 4.98615 5.045022 6.143 6.07837 5.8056247.7807.798157.847726

9.120

9.23448

9.3647

model generation and training procedure of ANN model took more time than regression model,which took just a couple of seconds.

6.The characteristics of machined surface

The AWJ machined AA 7075surfaces were observed under SEM after air cleaning to analyse the machined surface char-acteristics.T ypical macroscopic features of an AWJ cut surface and kerf geometry has been studied previously by Arola and Ramulu (1997).A typical AWJ machined surface has three dis-tinct zones along the kerf wall,i.e.an initial damage region (IDR),a smooth cutting region (SCR)and a rough cutting region (RCR)from the jet entry to the exit of workpiece.T yp-ical

microstructures of these regions are shown in Figs.

6–8.The IDR is the shallow dark wear tracks created by shal-low angles of attack.The SCR exists between IDR and RCR with a small area,which is created at

large angle of attack.The surface roughness gets worse through the RCR because of the jet upward de?ection.In RCR,the surfaces generally exhibit striate or wavy characteristics.Fig.9shows this surface waviness for different jet pressures.As shown,an increase

Fig.6–SEM micrograph of IDR.Fig.7–SEM micrograph of SCR.

in jet pressure led to striations or waviness with large feet on the wall of the cut surface to deteriorate the surface roughness.

Fig.8–SEM micrograph of RCR.

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Fig.9–Increase of surface waviness and surface roughness with jet pressure(a)P:125MPa,(b)P:175MPa,(c)P:250MPa.

7.Conclusion

To determine the relationship between machining parameters and surface roughness in AWJ machining process,an ANN and multiply regression analysis were carried out based on Taguchi’s orthogonal https://www.360docs.net/doc/3714762552.html,parisons were made of the above approaches,after testing their performances on13ran-domly selected test cases.The machined surfaces were also examined using scanning electron microscopy(SEM).Sum-marizing the mean features of the results,both the neural network and regression approaches were seen to be suf?cient for estimating surface roughness in AWJ machining with a very small test errors.The predicted process parameters on validation were found to be close correlation with the actual performance results.However,the regression model showed a slightly better performance compared to the ANN model. The training of developed neural networks can be achieved quickly after331epoches,which could reduce the computa-tional cost of ANN because of its iterative calculations.From this,the predictive models can be used for predicting surface roughness in AWJ process with a higher reliability.The perfor-mance can further be enhanced with large experimental data from full factorial experimentation and considering the addi-tional performance characteristics.Based on the ANOVA and F-test,the most dominant parameter on the surface rough-ness was found as waterjet pressure,while the second ranking factor was traverse speed.Abrasive?ow rate and standoff dis-tance were less effective on surface roughness,while effect of abrasive grit size can be negligible.Microstructure evaluation of cutting surfaces of samples revealed that an AWJ process produce three distinct region along the cut wall surface as:an initial damage region(IDR),a smooth cutting region(SCR)and a rough cutting region(RCR).

Appendix A.Analysis of variance and F-test

S m=

ái

,S T=

á2i?S m

S A=

áA2

?S m,S E=S T?

S A

V A=

S A

f A

,F A0=

V A

V E

where S T is the sum of squares due to total variation;S m is the sum of squares due to mean;S A is the sum of squared due to factor A(A=(V,P,h,d,m);S E is the sum of square due to error;ái is theávalue of each experiment(i=1–27);áAi is the sum of i level of factor A(i=1,2,3);N is the repeating number of each level of factor A;f A is the degree of freedom of factor A;V A is the variance of factor A and F A0is the F-test value of factor A.

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