Artificial Intelligence-Based Surface Roughness Estimation

Research ArticleArtificial Intelligence-Based Surface Roughness EstimationModelling for Milling of AA6061 Alloy

Aykut Eser 1 Elmas Askar Ayyıldız 2 Mustafa Ayyıldız 3 and Fuat Kara 3

1Department of Manufacturing Engineering Institute of Science Duzce University Duzce Turkey2Department of Mechanical Engineering Institute of Science Duzce University Duzce Turkey3Department of Mechanical Engineering Duzce University Duzce Turkey

Correspondence should be addressed to Fuat Kara fuatkaraduzceedutr

Received 6 January 2021 Revised 28 January 2021 Accepted 2 February 2021 Published 13 February 2021

Academic Editor Jinyang Xu

Copyright copy 2021 Aykut Eser et al )is is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

)is study introduces the improvement of mathematical and predictive models of surface roughness parameter (Ra) in millingAA6061 alloy using carbide cutting tools coated with CVD-TiCN in dry condition An experimental model has been improved forestimating the surface roughness using artificial neural networks (ANN) and response surface methodology (RSM) For thesemodels cutting speed depth of cut and feed rate were evaluated as input parameters for experimental design For the ANNmodelling the standard backpropagation algorithm was established to be the optimum selection for training the model In theforming of the network construction five different learning algorithms were used the conjugate gradient backpropagationLevenbergndashMarquardt scaled conjugate gradient quasi-Newton backpropagation and resilient backpropagation )e bestconsequent with single hidden layers for the surface roughness was obtained by 3-8-1 network structures )e statistical analysiswas performed with RSM-based second-order mathematics model )e influences of the cutting parameters on surface roughnesswere defined by using analysis of variance (ANOVA) )e ANOVA results show that the depth of cut is the most effectiveparameter on surface roughness Prediction models developed using ANN and RSM were compared in terms of predictionaccuracy R2 MEP and RMSE )e data estimated from ANN and RSM were realized to be very close to the data acquired fromexperimental studies )e value R2 of RSM model was higher than the values of the ANNmodel which demonstrated the stabilityand sturdiness of the RSM method

1 Introduction

)e most important factors in the machining of workpiecesare the cutting speed feed rate depth of cut and choice ofthe appropriate cutting tool When workpieces are processedat low cutting speeds the processing time is increased andthe time loss is greater When machining at high cuttingspeeds the cutting tool wears quickly and finishes the toollife faster For these reasons parameters such as cutting tooltype cutting speed feed rate and depth of cut affect surfaceroughness [1 2]

In the milling process the surface roughness is one of theimportant properties indicating workpiece quality Modelsused in the prediction of surface roughness include themultiple regression technique fuzzy set-based technique

and artificial neural networks (ANNs) [3ndash7] )e predictionof surface roughness (Ra) values in Al alloy 7075-T7351 inthe face milling machining process for different ANNs wasdeveloped by Munoz-Escalona and Maropoulo [8]

Sanjeevi et al [9] recommended the methodology of theidentified surface roughness accuracy rate in Al6061 millingwith ANN An ANN model for estimating the surfaceroughness performance in the machining process by con-sidering the ANN as the requisite technique for measuringsurface roughness was presented by Zain et al [10] A novelmethod to define the optimum cutting parameters to obtainminimum surface roughness in the face milling of X20Cr13stainless steel by coupling ANNs and the harmony searchalgorithm was presented by Razfar et al [11] )e harmonysearch algorithm was used to find optimum cutting

HindawiAdvances in Materials Science and EngineeringVolume 2021 Article ID 5576600 10 pageshttpsdoiorg10115520215576600

parameters leading to minimum surface roughness In an-other work Saric et al investigated and compared the in-fluence of different algorithms of neural networkarchitectures on the error of the roughness predictionmodel for a steel surface machined by face milling [12]Karabulut [13] in the milling of AA7039 and Al2O3 rein-forced composites analyzed the effect of parameters onsurface roughness and cutting force under finish machiningconditions with the depth of cut limited to under 15mm Hedeveloped analytical models for the estimation of surfaceroughness and cutting force using regression analysis andANNs )e development of a multilayer perceptron andradial basis function neural network model for estimatingsurface roughness in the machining of 2024-T351 alumin-ium alloy was described by Fang et al )e results showedthat compared with the radial basis function model themultilayer perceptron model offered a significantly higheraccuracy of estimation for machined surface roughnessespecially for maximum roughness height [14] In anotherwork Markopoulos et al [15] tested the estimation ofsurface roughness in end-milling using various trainingalgorithms for backpropagation and radial basis functionnetworks A general dynamic surface roughness monitoringsystem for milling operations using an ANN was developedby Khorasani and Yazdi [16] Jeyakumar et al [17] used RSMto determine the performance of milling process of AA6061)ey tested the adequacy of the model improved by usingexperimental values Kilickap et al [18] investigated therelationship between cutting parameters such as cuttingspeeds feed rate and depth of cut using optimizationtechniques such as RSM and ANN Sahare et al [19] op-timized the end milling process for Al2024-T4 workpiecematerial by using the ANN combined with Taguchi methodYeganefar et al [20] predicted and optimized the surfaceroughness and cutting forces in milling of Al7075-T6 byusing regression analysis support vector regression artificialneural network and multiobjective genetic algorithm

In this study it is aimed to estimate the surfaceroughness of AA6061 aluminum alloy by using artificialneural network and multiple regression method In theneural network different learning algorithms are used withthe multilayer perception (MLP) architecture Analysis ofvariance (ANOVA) was used in the multiple regressionmodel By comparing the performances of the predictedmodels with statistical methods it has been proven that theycan be used effectively to estimate the surface roughness inthe milling process

2 Materials and Methods

)is study aimed to define the optimal machining conditionsby estimating the effect of the cutting parameters on thesurface roughness in themilling of AA6061 aluminium alloy)e aluminium alloy used in the tests was manufactured inworkpiece dimensions of 20times 50times 300mm and its chemicalcomposition is shown in Table 1

)e Delta Seiki CNC 1050 A milling machine with a11KWdrive motor was used in the experiments)e carbidecutting tools coated with CVD-TiCN were obtained from

Korloy )e Mahr Marsurf PS 10 surface roughness testerwas used for measuring the surface roughness of the ma-chined surfaces )e surface roughness (Ra) value was de-termined as the average of three measurements taken fromthe machined surfaces )e three input and output pa-rameters used in determining the surface roughness of thealuminium machining included the cutting speed (Vc) (mmin) feed rate (f) (mmrev) and depth of cut (ap) (mm) Inorder to maintain constant cutting conditions each ex-periment was run with new tools )e milling tests werecarried out without a coolant and a total of 27 experimentswere performed Table 2 shows the experimental data for theAA6061 aluminium alloy

)e studied outputs are chosen in order to analyze andstudy the effect of the distinct cutting parameters on ma-chinability and for estimation using the artificial neuralnetwork (ANN) and response surface methodology (RSM))e ANN and RSM approaches are compared in terms of theestimated data and correlation coefficient (R2) mean errorpercentage (MEP) and root mean square error (RMSE)values Equations (1)ndash(3) give the formulae for R2 MEP andRMSE [21]

R2

1 minus1113936j tj minus oj1113872 1113873

2

1113936j o2j1113872 1113873

⎛⎝ ⎞⎠ (1)

MEP() 1113936J tj minus oj1113872 1113873tj1113872 1113873x 100

p (2)

RMSE 1p

1113888 11138891113944j

tj minus oj

11138681113868111386811138681113868

111386811138681113868111386811138682

⎛⎝ ⎞⎠

12

(3)

where p is the number of samples t is the goal value and o isthe output value

3 Modelling Methods

31 Artificial Neural Networks )e ANN imitates somebasic aspects of the brain functions [21] A neuron is thefundamental element of the neural networks which arecomposed of neurons weights inputs activation functionsummation function and output )e net input of theneuron is calculated by the summation function as follows

NETi 1113944

n

j1wijxj + wbi (4)

where wbi is the weight of the biases between the layers xj isthe output of the jth processing element wij is the weight ofthe connections between the ith and jth processing elementsn is the number of processing elements in the previous layeri and j are the processing elements and NETi is the weightedsum of the input to the ith processing element)e output ofthe neuron is defined by the activation function)e sigmoidfunction is usually used for the transfer function and gen-erates a value between 0 and 1 for each value of the net input[22 23] )e logistic transfer function of the ANN modelimproved in this study is given as follows

2 Advances in Materials Science and Engineering

f NETi( 1113857 1

1 + eminus NETi

(5)

)e optimum learning algorithm and network structuremust be defined to acquire the output values closest to theexperimental values For this aim the number of neurons inthe hidden layer was boosted step by step (ie from five tofifteen) and quasi-Newton backpropagation (BFGS) con-jugate gradient backpropagation (CGP) Lev-enbergndashMarquardt (LM) resilient backpropagation (RP)and scaled conjugate gradient (SCG) learning algorithmswere used to describe the optimum network structure andlearning algorithm [23]

)ere were three input parameters in the networkcutting speed (Vc) depth of cut (ap) and feed rate (f ) )esurface roughness was the one output parameter in thenetwork )e network structure is shown in Figure 1 As anoutcome of the tests the experimental data acquired (27 foreach input and output) were arranged for the training andtesting sets of the ANN )e ratio for training and testingdata was selected as approximately 80 20 ie sets of 22 and5 arbitrarily selected from the experimental data as thetraining and testing data respectively )e output and inputvalues were normalized between 0 and 1 to obtain better

estimations )e surface roughness values predicted afterANN training were checked against the experimental data

32 Response Surface Methodology RSM is a series of ex-periments procedure for optimizing the output value is afunction of several independent variables )e main ap-proach is to move in the direction of maximum increase ordecrease to reach the local minimum or maximum value(optimum point) [24] In this study the RSM-based second-order mathematical model is provided as follows

y β0 + 1113944k

i1βiXi + 1113944

k

ij

βijXiXj + 1113944k

i1βiiX

2I (6)

where β is the regression coefficient and X is the inputparameter [25] From (7) the relationship is identifiedbetween the studied output and the milling parameters asfollows

y β0 + β1Vc + β2f + β3ap + β4Vcf + β5Vcap

+ β6fap + β7Vc2

+ β8f2

+ β9ap2

(7)

Table 1 Chemical composition of workpiece

Component Al Cr Cu Fe Mg Mn Si Ti ZnWt 958ndash986 004ndash035 015ndash04 Max 07 08ndash12 Max 015 04ndash08 Max 015 Max 025

Table 2 Experimental data for AA6061 aluminium alloy

Test no Vc (mmin) f (mmrev) ap (mm) Ra (microm)1 100 01 1 0492 100 01 15 05913 100 01 2 0624 100 015 1 05275 100 015 15 05526 100 015 2 06187 100 02 1 05538 100 02 15 0589 100 02 2 060610 150 01 1 042211 150 01 15 05612 150 01 2 060213 150 015 1 051414 150 015 15 054215 150 015 2 056616 150 02 1 054717 150 02 15 056818 150 02 2 059219 200 01 1 039120 200 01 15 042821 200 01 2 04822 200 015 1 043923 200 015 15 053324 200 015 2 054125 200 02 1 053926 200 02 15 055427 200 02 2 0589

Advances in Materials Science and Engineering 3

)e analysis of variance (ANOVA) is an analysis that isespecially used in multiparameter and multilevel models andprovides a total interpretation of the test results )e sig-nificance level (significance level) obtained from the analysisof variance determines the probability of the accuracy of thedecision given as a result of the tests )is possibility usuallydenoted by α is usually determined prior to receipt ofsamples )us it is ensured that the results obtained are notaffected by the selection In practice significance levels of005 and 001 are used [26]

4 Results and Discussion

41 Results of ANN In this study a computer program wasdeveloped in a MATLAB platform to estimate the surfaceroughness )e cutting speed depth of cut and feed ratewere used in the input layer while the surface roughness wasused in the output layer of the ANN)e single hidden layerwith eight neurons was used for the purpose of obtain ac-curate results )e trials conducted in this study indicatedthat the CGP learning algorithm is the best learning algo-rithm for the surface roughness )e determination of thebest learning algorithm and optimum number of neuronsfor the surface roughness are given in Table 3

)e matching of the experimental and ANN values forthe training and the testing sets of the surface roughness areshown in Figures 2 and 3 respectively )e most dramaticpoint here is that the prediction values are similar to theexperimental values and the estimative ability reached bythe network for surface roughness was deemed to be fa-vorable Accordingly the selection of the three input pa-rameters as affecting factors for prediction of surfaceroughness was shown to be acceptable

)e estimation performance of both the surfaceroughness testing and training sets demonstrated that theaccuracy of the CGP learning algorithm was satisfactory(plusmn5)With the ANNmodel developed for the estimation ofthe surface roughness the MEP values for surface roughnesswere found to be 114152 and 144418 for the testing andtraining data respectively )e RMSE values were calculatedas 01181 and 00774 for the testing and training data re-spectively )e R2 values were calculated as 956 and 986for the testing and training data respectively )e surfaceroughness can be completely calculated by the followingformula

Ra 1

1 + eminus (10193xF1minus 08875xF2minus 15325xF3+05470xF4+07890xF5minus 12810xF6+14103xF7+02420F8+01312)

1113888 1113889 (8)

where Fi (i 1 2 5) can be calculated according to thefollowing

Fi 1

1 + eminus Ei

1113888 1113889 (9)

Ei is calculated via the equation in Table 4 )e weightvalues for the hidden and input layers are also given inTable 4

42 Results of RSM )e analysis of variance works with onparameters namely F statistics the sum of squares (SS) andP value)e F statistics which is derived by dividing the termmean square (MS) of a factor with MS of error explains therelative importance of the factors)e SS defines the deviationfrom the mean which is derived from that factor )e P valueshows the statistical significance to a confidence interval of95 (significance level α 005) If the P value is lower than αthen that factor is significant [25] )e ANOVA analysis wasoperated with Minitab 18 From Table 5 the ANOVA resultsshow that the depth of cut (ap) is the most effective parameter

on surface roughness with a contribution of 3548 )ecutting speed impacts the surface roughness with a contri-bution of 2338 after by the feed rate (f) with a contributionof 1674 )e results are similar to the results of the studiesconducted by the researchers [25 27]

)e quadratic models of surface roughness have beenimproved by using the response surface methodology )eregression model is shown as follows with R2 of 999

y 0318 minus 000097xVc minus 019xf + 0350xap

minus 0000004xVc2

+ 107xf2

+ 004xap2

+ 00115xVcxf minus 000011xVcxap minus 0837xfxap

(10)

Figure 4 shows the differences between the measuredand estimated parameter of Ra Figures 5ndash7 show the 3Dsurface plot of the surface roughness according to cuttingspeed and feed rate (ap 15mm) cutting speed and depthof cut (f 015mmrev) and feed rate and depth of cut(Vc 150mmin) Figure 5 indicates that Ra increases when

Cutting speed

Depth of cut

Feed rate

W1j

W2j

W3j

NETi =n

j = 1

wijxj + wbi f (NETi) = 11 + endashNETi Surface roughness

Figure 1 Network structure for the surface roughness


Table 3 Statistical data of the surface roughness for five learning algorithms

Learning algorithm Number of neuronsTraining data Testing data

MEP RMSE R2 MEP RMSE R2

BFGS 3-5-1 399963 01723 09063 203945 01601 09126BFGS 3-6-1 237686 01082 09718 185676 01269 09549BFGS 3-7-1 366470 01660 09150 177115 01613 09112BFGS 3-8-1 562828 02222 08597 186268 01687 09091BFGS 3-9-1 963075 03952 04957 145044 02214 08216BFGS 3-10-1 457621 02016 08702 195055 01570 09192BFGS 3-11-1 516061 01912 09214 184157 01140 09675BFGS 3-12-1 373415 01381 09580 175858 01398 09484BFGS 3-13-1 526299 02257 08320 162210 01623 09106BFGS 3-14-1 398147 01767 09225 138113 00949 09769BFGS 3-15-1 701297 02878 07236 185301 01582 09269CGP 3-5-1 158842 01224 09626 261493 01704 09185CGP 3-6-1 451115 01870 09055 208723 01533 09301CGP 3-7-1 434061 01920 08831 171494 01627 09062CGP 3-8-1 144418 00774 09865 114152 01181 09561CGP 3-9-1 430063 01869 08865 187517 01680 09006CGP 3-10-1 254985 01421 09392 187540 01430 09295CGP 3-11-1 442420 01743 09214 130006 01105 09648CGP 3-12-1 172771 00891 09798 270952 01632 09258CGP 3-13-1 520152 02299 08366 199879 01686 09101CGP 3-14-1 400879 01699 09141 150850 01258 09477CGP 3-15-1 556072 02560 09141 138909 01257 09510LM 3-5-1 257917 01574 09116 203034 01771 08743LM 3-6-1 420235 01680 09248 189305 01441 09376LM 3-7-1 473599 01850 09405 197645 01340 09559LM 3-8-1 442204 01779 09036 167346 01651 09004LM 3-9-1 401816 01958 08686 191302 02110 08236LM 3-10-1 447952 01808 09058 185216 01408 09396LM 3-11-1 369839 01641 09358 175039 01327 09504LM 3-12-1 365893 01719 09201 163433 01510 09253LM 3-13-1 155680 01282 09548 191682 01374 09349LM 3-14-1 248890 01262 09553 144378 01511 09153LM 3-15-1 399324 01927 08815 138734 01569 09140RP 3-5-1 472390 01920 08928 202159 01555 09248RP 3-6-1 220402 01345 09656 164952 01474 09459RP 3-7-1 245939 01311 09481 191965 01719 08795RP 3-8-1 181270 00872 09793 126631 01096 09606RP 3-9-1 345495 01694 08997 192169 01801 08706RP 3-10-1 283791 01257 09559 175293 01454 09268RP 3-11-1 464756 01807 09132 181761 01075 09695RP 3-12-1 442982 01787 08965 194537 01660 09042RP 3-13-1 369307 01538 09300 167184 01242 09507RP 3-14-1 364954 01594 09404 173533 01189 09628RP 3-15-1 343147 01602 09175 199560 01697 08996SCG 3-5-1 424115 01794 09010 199265 01498 09323SCG 3-6-1 255442 01352 09449 176713 01528 09233SCG 3-7-1 535186 02251 08338 191087 01567 09175SCG 3-8-1 251891 01554 09233 182081 02035 08313SCG 3-9-1 289926 01377 09424 190380 01651 09062SCG 3-10-1 501422 01881 09077 157635 01403 09375SCG 3-11-1 168713 01044 09724 195369 01363 09319SCG 3-12-1 458239 01691 09324 174317 01059 09710SCG 3-13-1 309181 01261 09588 178864 01417 09380SCG 3-14-1 327777 01626 09221 157285 01464 09301SCG 3-15-1 510586 02004 08974 112896 01076 09654


ap is constant and f is increases Figure 6 shows that Ra isincreased when f is constant and ap increases SimilarlyFigure 7 shows that Ra is increased when Vc is constant andap increases

43 Comparison of ANNandRSMModel Prediction modelsdeveloped using ANN and RSM were compared in terms ofprediction accuracy R2 MEP and RMSE )e experimentaldata acquired (27 for each input and output) were estimatedfor the training model of the ANN As an outcome of the

ANN estimates R2 MEP and RMSE were calculated as927 2811 and 0185 respectively Table 6 presents acomparison of the estimated values of ANN and RSM ap-proaches R2 MEP and RMSE )e RSM modelrsquos R2 MEPand RMSE values were higher than the values of the ANNmodel which demonstrated the stability and sturdiness ofthe RSM method )e results are similar to the results of thestudies conducted by the researchers [18 28] In order toshow the comparison of the values of ANN and RSMmodels experimental and estimated data of surfaceroughness are presented in Figure 8

Table 4 Weight values between the hidden and input layers for surface roughness

Ei w1x(Vc) + w2x(ap) + w3x(f) + θi

i w1 w2 w3 θi1 21675 45408 24786 minus 558052 35633 minus 39271 18727 minus 399553 36445 minus 30630 minus 28993 minus 248014 minus 48661 06005 26738 079015 minus 10260 41485 36204 minus 066096 41470 minus 16458 minus 34706 224447 minus 32287 minus 11686 44931 minus 392988 13409 52014 minus 10947 56469

Table 5 ANOVA analysis for surface roughness

Source DF Seq SS Cont () Adj MS F value P valueModel 9 0090865 9250 0010096 2330 00001Linear 3 0074258 7560 0024753 5713 00001Vc 1 0022969 2338 0022969 5302 00001F 1 0016441 1674 0016441 3795 00001Ap 1 0034848 3548 0034848 8043 00001Square 3 0001347 137 0000449 104 04016VctimesVc 1 0000704 072 0000704 163 02195Ftimes f 1 0000043 004 0000043 010 07575aptimes ap 1 0000600 061 0000600 138 02555Two-way interaction 3 0015260 1553 0005087 1174 00002Vctimes f 1 0009919 1010 0009919 2289 00002Vctimes ap 1 0000091 009 0000091 021 06530ftimes ap 1 0005250 534 0005250 1212 00029

Error 17 0007365 750 0000433Total 26 0098230 10000

0

02

04

06

08

1

12

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Surfa

ce ro

ughn

ess

Ra

Run order

ExperimentalTraining ANN

Figure 2 Matching of experimental and ANN values for surface roughness training sets


04

05

06

07

08

1 2 3 4 5Run order

Surfa

ce ro

ughn

ess

RaExperimentalTraining ANN

Figure 3 Matching of experimental and ANN values for surface roughness testing sets

03504

04505

05506

06507

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27Run order

Surfa

ce ro

ughn

ess

Ra

Ra measuredRa predicted

Figure 4 Matching of experimental and RSM values for surface roughness

060

055

050

045

Ra

100150

200Vc

020

015

010

f

Figure 5 Predicted RSM values for surface roughness on Vc and f


060

055

050

045

Ra

100150

200Vc

20

15

10

ap

Figure 6 Predicted RSM values for surface roughness on Vc and ap

060

055

050

045

Ra

100150

200f

20

15

10

ap

Figure 7 Predicted RSM values for surface roughness on f and ap

Table 6 Comparison of ANN and RSM model

ANN RSMR2 MEP RMSE R2 MEP RMSE

Ra 927 2811 0185 999 217 0016

010203040506070809

1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27Run order

Surfa

ce ro

ughn

ess

Ra

ANN predicted

Ra measuredRSM predicted

Figure 8 Experimental data versus the predicted ANN and RSM data for surface roughness


5 Conclusions

)is study compares the performance of both the artificialneural network and response surface methodology forsurface roughness )e status of the milling of AA6061 al-uminium alloy was studied )e cutting speed feed rate anddepth of cut were examined as the effective factors in thisinvestigation As a result of the study the following notes canbe deduced

(i) An ANN model was used to determine optimummachining conditions by estimating the influence of thecutting parameters on the surface roughness inmilling For training the ANN developed for determining thesurface roughness was used by the backpropagationalgorithm For the training period several algorithmsincluding BFGS CGP LM RP and SCG were usedAs an outcome of the training trials for estimation ofthe surface roughness the best result was acquiredwith the CGP learning algorithm and network ar-chitecture having eight hidden neurons

(ii) )e experimental data acquired (27 for each inputand output) were estimated for the training modelof the ANN As an outcome of the ANN estimatesR2 MEP and RMSEwere calculated as 927 2811and 0185 respectively According to RSM model-ling results R2 MEP and RMSE were calculated as999 217 and 0016 respectively

(iii) )e R2 value of the RSM model was higher than thevalues of the ANN model which demonstrated thestability and sturdiness of the RSM method

(iv) )e ANOVA results indicate that the depth of cut isthe most effective parameter on surface roughnesswith a contribution of 3548 )e cutting speedimpacts the surface roughness with a contributionof 2338 after by the feed rate with a contributionof 1674

(v) )e optimal results of minimal surface roughness(0391μm) are as follows cutting speed of 200mminfeed rate of 01mmrev and depth of cut of 10mm

Data Availability

)e data used to support the findings of this study areavailable within the article or from the corresponding authorupon request

Conflicts of Interest

)e authors declare that they have no conflicts of interest

References

[1] E Altas H Gokkaya M A Karatas and D Ozkan ldquoAnalysisof surface roughness and flank wear using the Taguchi methodin milling of NiTi shape memory alloy with uncoated toolsrdquoCoatings vol 10 no 12 pp 1ndash17 2020

[2] H Caliskan E Altas and P Panjan ldquoStudy of nanolayerAlTiNTiN coating deposition on cemented carbide and its

performance as a cutting toolrdquo Journal of Nano Researchvol 47 pp 1ndash10 2017

[3] E Askar Ayyıldız M Ayyıldız and F Kara ldquoOptimization ofsurface roughness in drilling medium-density fiberboard witha parallel robotrdquo Advances in Materials Science and Engi-neering vol 2021 Article ID 6658968 8 pages 2021

[4] I Asilturk and M Ccedilunkas ldquoModeling and prediction ofsurface roughness in turning operations using artificial neuralnetwork and multiple regression methodrdquo Expert Systemswith Applications vol 38 no 5 pp 5826ndash5832 2011

[5] C Lu ldquoStudy on prediction of surface quality in machiningprocessrdquo Journal of Materials Processing Technology vol 205no 1-3 pp 439ndash450 2008

[6] S-P Lo ldquoAn adaptive-network based fuzzy inference systemfor prediction of workpiece surface roughness in end millingrdquoJournal of Materials Processing Technology vol 142 no 3pp 665ndash675 2003

[7] S Jovic O Anicic and M Jovanovic ldquoAdaptive neuro-fuzzyfusion of multi-sensor data for monitoring of CNC ma-chiningrdquo Sensor Review vol 37 no 1 pp 78ndash81 2017

[8] P Muntildeoz-Escalona and P G Maropoulos ldquoArtificial neuralnetworks for surface roughness prediction when face millingAl 7075-T7351rdquo Journal of Materials Engineering and Per-formance vol 19 no 2 pp 185ndash193 2010

[9] R Sanjeevi R Nagaraja and B R Krishnan ldquoVision-basedsurface roughness accuracy prediction in the CNC millingprocess (Al6061) using ANNrdquo Materials Today Proceedings2020

[10] A M Zain H Haron and S Sharif ldquoPrediction of surfaceroughness in the end milling machining using ArtificialNeural Networkrdquo Expert Systems with Applications vol 37no 2 pp 1755ndash1768 2010

[11] M R Razfar R Farshbaf Zinati and M Haghshenas ldquoOp-timum surface roughness prediction in face milling by usingneural network and harmony search algorithmrdquo e Inter-national Journal of Advanced Manufacturing Technologyvol 52 no 5-8 pp 487ndash495 2011

[12] T Saric G Simunovic and K Simunovic ldquoUse of neuralnetworks in prediction and simulation of steel surfaceroughnessrdquo International Journal of Simulation Modellingvol 12 no 4 pp 225ndash236 2013

[13] S Karabulut ldquoOptimization of surface roughness and cuttingforce during AA7039Al2O3 metal matrix composites millingusing neural networks and Taguchi methodrdquo Measurementvol 66 pp 139ndash149 2015

[14] N Fang N Pai and N Edwards ldquoNeural network modelingand prediction of surface roughness in machining aluminumalloysrdquo Journal of Computer and Communications vol 4no 5 pp 1ndash9 2016

[15] A P Markopoulos S Georgiopoulos and D E ManolakosldquoOn the use of back propagation and radial basis functionneural networks in surface roughness predictionrdquo Journal ofIndustrial Engineering International vol 12 no 3 pp 389ndash400 2016

[16] A Khorasani and M R S Yazdi ldquoDevelopment of a dynamicsurface roughness monitoring system based on artificialneural networks (ANN) in milling operationrdquo e Interna-tional Journal of Advanced Manufacturing Technology vol 93no 1-4 pp 141ndash151 2017

[17] S Jeyakumar K Marimuthu and T Ramachandran ldquoPre-diction of cutting force tool wear and surface roughness ofAl6061SiC composite for endmilling operations using RSMrdquoJournal of Mechanical Science and Technology vol 27 no 9pp 2813ndash2822 2013


[18] E Kilickap A Yardimeden and Y H Ccedilelik ldquoMathematicalmodelling and optimization of cutting force tool wear andsurface roughness by using artificial neural network and re-sponse surface methodology in milling of Ti-6242Srdquo AppliedSciences vol 7 no 10 pp 1ndash12 2017

[19] S B Sahare S P Untawale S S Chaudhari R L Shrivastavaand P D Kamble ldquoOptimization of end milling process forAl2024-T4 aluminum by combined Taguchi and artificialneural network processrdquo Advances in Intelligent Systems andComputing vol 584 pp 525ndash535 2018

[20] A Yeganefar S A Niknam and R Asadi ldquo)e use of supportvector machine neural network and regression analysis topredict and optimize surface roughness and cutting forces inmillingrdquo e International Journal of AdvancedManufacturing Technology vol 105 no 1-4 pp 951ndash9652019

[21] Y Ccedilay A Ccediliccedilek F Kara and S Sagiroglu ldquoPrediction ofengine performance for an alternative fuel using artificialneural networkrdquo Applied ermal Engineering vol 37pp 217ndash225 2012

[22] M Ayyıldız and K Ccediletinkaya ldquoPredictive modeling of geo-metric shapes of different objects using image processing andan artificial neural networkrdquo Proceedings of the Institution ofMechanical Engineers Part E Journal of Process MechanicalEngineering vol 231 no 6 pp 1206ndash1216 2017

[23] Y O Ozgoren S Ccediletinkaya S Sarıdemir A Ccediliccedilek andF Kara ldquoArtificial neural network based modelling of per-formance of a beta-type Stirling enginerdquo Proceedings of theInstitution of Mechanical Engineers Part E Journal of ProcessMechanical Engineering vol 227 no 3 pp 166ndash177 2013

[24] R H Myers and D C Montgomery Response SurfaceMethodology Process and Product Optimisation UsingDesigned Experiments John Wiley amp Sons Inc New YorkNY USA 1995

[25] A Chabbi M A Yallese M Nouioua I MeddourT Mabrouki and F Girardin ldquoModeling and optimization ofturning process parameters during the cutting of polymer(POM C) based on RSM ANN and DF methodsrdquo e In-ternational Journal of Advanced Manufacturing Technologyvol 91 no 5-8 pp 2267ndash2290 2017

[26] T P Ryan Statistical Methods for Quality Improvement JohnWiley amp Sons Inc New York NY USA Second edition 2000

[27] M Nouioua M A Yallese R Khettabi S BelhadiM L Bouhalais and F Girardin ldquoInvestigation of the per-formance of the MQL dry and wet turning by responsesurface methodology (RSM) and artificial neural network(ANN)rdquo e International Journal of AdvancedManufacturing Technology vol 93 no 5-8 pp 2485ndash25042017

[28] R Kumar and S Chauhan ldquoStudy on surface roughnessmeasurement for turning of Al 707510SiCp and Al 7075hybrid composites by using response surface methodology(RSM) and artificial neural networking (ANN)rdquo Measure-ment vol 65 pp 166ndash180 2015


parameters leading to minimum surface roughness In an-other work Saric et al investigated and compared the in-fluence of different algorithms of neural networkarchitectures on the error of the roughness predictionmodel for a steel surface machined by face milling [12]Karabulut [13] in the milling of AA7039 and Al2O3 rein-forced composites analyzed the effect of parameters onsurface roughness and cutting force under finish machiningconditions with the depth of cut limited to under 15mm Hedeveloped analytical models for the estimation of surfaceroughness and cutting force using regression analysis andANNs )e development of a multilayer perceptron andradial basis function neural network model for estimatingsurface roughness in the machining of 2024-T351 alumin-ium alloy was described by Fang et al )e results showedthat compared with the radial basis function model themultilayer perceptron model offered a significantly higheraccuracy of estimation for machined surface roughnessespecially for maximum roughness height [14] In anotherwork Markopoulos et al [15] tested the estimation ofsurface roughness in end-milling using various trainingalgorithms for backpropagation and radial basis functionnetworks A general dynamic surface roughness monitoringsystem for milling operations using an ANN was developedby Khorasani and Yazdi [16] Jeyakumar et al [17] used RSMto determine the performance of milling process of AA6061)ey tested the adequacy of the model improved by usingexperimental values Kilickap et al [18] investigated therelationship between cutting parameters such as cuttingspeeds feed rate and depth of cut using optimizationtechniques such as RSM and ANN Sahare et al [19] op-timized the end milling process for Al2024-T4 workpiecematerial by using the ANN combined with Taguchi methodYeganefar et al [20] predicted and optimized the surfaceroughness and cutting forces in milling of Al7075-T6 byusing regression analysis support vector regression artificialneural network and multiobjective genetic algorithm

In this study it is aimed to estimate the surfaceroughness of AA6061 aluminum alloy by using artificialneural network and multiple regression method In theneural network different learning algorithms are used withthe multilayer perception (MLP) architecture Analysis ofvariance (ANOVA) was used in the multiple regressionmodel By comparing the performances of the predictedmodels with statistical methods it has been proven that theycan be used effectively to estimate the surface roughness inthe milling process

2 Materials and Methods

)is study aimed to define the optimal machining conditionsby estimating the effect of the cutting parameters on thesurface roughness in themilling of AA6061 aluminium alloy)e aluminium alloy used in the tests was manufactured inworkpiece dimensions of 20times 50times 300mm and its chemicalcomposition is shown in Table 1

)e Delta Seiki CNC 1050 A milling machine with a11KWdrive motor was used in the experiments)e carbidecutting tools coated with CVD-TiCN were obtained from

Korloy )e Mahr Marsurf PS 10 surface roughness testerwas used for measuring the surface roughness of the ma-chined surfaces )e surface roughness (Ra) value was de-termined as the average of three measurements taken fromthe machined surfaces )e three input and output pa-rameters used in determining the surface roughness of thealuminium machining included the cutting speed (Vc) (mmin) feed rate (f) (mmrev) and depth of cut (ap) (mm) Inorder to maintain constant cutting conditions each ex-periment was run with new tools )e milling tests werecarried out without a coolant and a total of 27 experimentswere performed Table 2 shows the experimental data for theAA6061 aluminium alloy

)e studied outputs are chosen in order to analyze andstudy the effect of the distinct cutting parameters on ma-chinability and for estimation using the artificial neuralnetwork (ANN) and response surface methodology (RSM))e ANN and RSM approaches are compared in terms of theestimated data and correlation coefficient (R2) mean errorpercentage (MEP) and root mean square error (RMSE)values Equations (1)ndash(3) give the formulae for R2 MEP andRMSE [21]

R2

1 minus1113936j tj minus oj1113872 1113873

2

1113936j o2j1113872 1113873

⎛⎝ ⎞⎠ (1)

MEP() 1113936J tj minus oj1113872 1113873tj1113872 1113873x 100

p (2)

RMSE 1p

1113888 11138891113944j

tj minus oj

11138681113868111386811138681113868

111386811138681113868111386811138682

⎛⎝ ⎞⎠

12

(3)

where p is the number of samples t is the goal value and o isthe output value

3 Modelling Methods

31 Artificial Neural Networks )e ANN imitates somebasic aspects of the brain functions [21] A neuron is thefundamental element of the neural networks which arecomposed of neurons weights inputs activation functionsummation function and output )e net input of theneuron is calculated by the summation function as follows

NETi 1113944

n

j1wijxj + wbi (4)

where wbi is the weight of the biases between the layers xj isthe output of the jth processing element wij is the weight ofthe connections between the ith and jth processing elementsn is the number of processing elements in the previous layeri and j are the processing elements and NETi is the weightedsum of the input to the ith processing element)e output ofthe neuron is defined by the activation function)e sigmoidfunction is usually used for the transfer function and gen-erates a value between 0 and 1 for each value of the net input[22 23] )e logistic transfer function of the ANN modelimproved in this study is given as follows


f NETi( 1113857 1

1 + eminus NETi

(5)





y β0 + 1113944k

i1βiXi + 1113944

k

ij

βijXiXj + 1113944k

i1βiiX

2I (6)



+ β6fap + β7Vc2

+ β8f2

+ β9ap2

(7)











Ra 1


1113888 1113889 (8)


Fi 1

1 + eminus Ei

1113888 1113889 (9)






minus 0000004xVc2

+ 107xf2

+ 004xap2


(10)


Cutting speed

Depth of cut

Feed rate

W1j

W2j

W3j

NETi =n

j = 1

















Error 17 0007365 750 0000433Total 26 0098230 10000

0

02

04

06

08

1

12

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Surfa

ce ro

ughn

ess

Ra

Run order




04

05

06

07

08

1 2 3 4 5Run order

Surfa

ce ro

ughn

ess



03504

04505

05506

06507

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27Run order

Surfa

ce ro

ughn

ess

Ra



060

055

050

045

Ra

100150

200Vc

020

015

010

f



060

055

050

045

Ra

100150

200Vc

20

15

10

ap


060

055

050

045

Ra

100150

200f

20

15

10

ap




Ra 927 2811 0185 999 217 0016

010203040506070809

1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27Run order

Surfa

ce ro

ughn

ess

Ra

ANN predicted




5 Conclusions







Data Availability




References
































f NETi( 1113857 1

1 + eminus NETi

(5)





y β0 + 1113944k

i1βiXi + 1113944

k

ij

βijXiXj + 1113944k

i1βiiX

2I (6)



+ β6fap + β7Vc2

+ β8f2

+ β9ap2

(7)











Ra 1


1113888 1113889 (8)


Fi 1

1 + eminus Ei

1113888 1113889 (9)






minus 0000004xVc2

+ 107xf2

+ 004xap2


(10)


Cutting speed

Depth of cut

Feed rate

W1j

W2j

W3j

NETi =n

j = 1

















Error 17 0007365 750 0000433Total 26 0098230 10000

0

02

04

06

08

1

12

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Surfa

ce ro

ughn

ess

Ra

Run order




04

05

06

07

08

1 2 3 4 5Run order

Surfa

ce ro

ughn

ess



03504

04505

05506

06507

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27Run order

Surfa

ce ro

ughn

ess

Ra



060

055

050

045

Ra

100150

200Vc

020

015

010

f



060

055

050

045

Ra

100150

200Vc

20

15

10

ap


060

055

050

045

Ra

100150

200f

20

15

10

ap




Ra 927 2811 0185 999 217 0016

010203040506070809

1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27Run order

Surfa

ce ro

ughn

ess

Ra

ANN predicted




5 Conclusions







Data Availability




References





































Ra 1


1113888 1113889 (8)


Fi 1

1 + eminus Ei

1113888 1113889 (9)






minus 0000004xVc2

+ 107xf2

+ 004xap2


(10)


Cutting speed

Depth of cut

Feed rate

W1j

W2j

W3j

NETi =n

j = 1

















Error 17 0007365 750 0000433Total 26 0098230 10000

0

02

04

06

08

1

12

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Surfa

ce ro

ughn

ess

Ra

Run order




04

05

06

07

08

1 2 3 4 5Run order

Surfa

ce ro

ughn

ess



03504

04505

05506

06507

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27Run order

Surfa

ce ro

ughn

ess

Ra



060

055

050

045

Ra

100150

200Vc

020

015

010

f



060

055

050

045

Ra

100150

200Vc

20

15

10

ap


060

055

050

045

Ra

100150

200f

20

15

10

ap




Ra 927 2811 0185 999 217 0016

010203040506070809

1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27Run order

Surfa

ce ro

ughn

ess

Ra

ANN predicted




5 Conclusions







Data Availability




References













































Error 17 0007365 750 0000433Total 26 0098230 10000

0

02

04

06

08

1

12

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Surfa

ce ro

ughn

ess

Ra

Run order




04

05

06

07

08

1 2 3 4 5Run order

Surfa

ce ro

ughn

ess



03504

04505

05506

06507

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27Run order

Surfa

ce ro

ughn

ess

Ra



060

055

050

045

Ra

100150

200Vc

020

015

010

f



060

055

050

045

Ra

100150

200Vc

20

15

10

ap


060

055

050

045

Ra

100150

200f

20

15

10

ap




Ra 927 2811 0185 999 217 0016

010203040506070809

1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27Run order

Surfa

ce ro

ughn

ess

Ra

ANN predicted




5 Conclusions







Data Availability




References








































Error 17 0007365 750 0000433Total 26 0098230 10000

0

02

04

06

08

1

12

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Surfa

ce ro

ughn

ess

Ra

Run order




04

05

06

07

08

1 2 3 4 5Run order

Surfa

ce ro

ughn

ess



03504

04505

05506

06507

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27Run order

Surfa

ce ro

ughn

ess

Ra



060

055

050

045

Ra

100150

200Vc

020

015

010

f



060

055

050

045

Ra

100150

200Vc

20

15

10

ap


060

055

050

045

Ra

100150

200f

20

15

10

ap




Ra 927 2811 0185 999 217 0016

010203040506070809

1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27Run order

Surfa

ce ro

ughn

ess

Ra

ANN predicted




5 Conclusions







Data Availability




References
































04

05

06

07

08

1 2 3 4 5Run order

Surfa

ce ro

ughn

ess



03504

04505

05506

06507

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27Run order

Surfa

ce ro

ughn

ess

Ra



060

055

050

045

Ra

100150

200Vc

020

015

010

f



060

055

050

045

Ra

100150

200Vc

20

15

10

ap


060

055

050

045

Ra

100150

200f

20

15

10

ap




Ra 927 2811 0185 999 217 0016

010203040506070809

1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27Run order

Surfa

ce ro

ughn

ess

Ra

ANN predicted




5 Conclusions







Data Availability




References
































060

055

050

045

Ra

100150

200Vc

20

15

10

ap


060

055

050

045

Ra

100150

200f

20

15

10

ap




Ra 927 2811 0185 999 217 0016

010203040506070809

1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27Run order

Surfa

ce ro

ughn

ess

Ra

ANN predicted




5 Conclusions







Data Availability




References
































5 Conclusions







Data Availability




References












































Documents

Artificial Intelligence-Based Surface Roughness Estimation