Artificial neural networkâbased and response surface ...

Original Article

Artificial neural network–based and response surface methodology–based predictive models for material removal rate and surface roughness during electro-discharge diamond grinding of Inconel 718

Proc IMechE Part B: J Engineering Manufacture 1–10 Ó IMechE 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0954405415619347 pib.sagepub.com

Deepak Rajendra Unune and Harlal Singh Mali

Abstract Hybrid machining processes growing popularity in the processing of difficult-to-cut materials due to their distinct merits over individual machining processes attributed by an amalgamation of two or more machining mechanisms simultaneously. This research study deals with the response surface methodology and artificial neural network with backpropagation algorithm–based mathematical modeling of electro-discharge diamond grinding of Inconel 718 superalloy. The matrix experiments were designed based on central composite design. The wheel speed, current, pulse-on-time, and duty factor were chosen as control factors, while material removal rate and average surface roughness (Ra) were chosen as performance parameters. The analysis of variance test shows that the wheel speed is the major factor influencing both the material removal rate and the Ra and contributes 89.03% and 79.10% on material removal rate and Ra, respectively, followed by current which contributes 4.43% and 8.38% on material removal rate and Ra, respectively. The modeling and predictive abilities of developed artificial neural network model (4-24-2) were related to the response surface methodology model using root mean square error and absolute standard deviation. The predicted values of material removal rate and Ra by response surface methodology and artificial neural network are in close agreement with the actual experimental results.

Keywords Electro-discharge diamond grinding, hybrid machining, artificial neural network, response surface method, Inconel 718

Date received: 7 May 2015; accepted: 27 October 2015

Introduction Inconel 718 is extensively used superalloy in aerospace industries due to its high-temperature strength, excellent fatigue, and corrosion resistance. Extremely, though, and thermally resistant nature of Inconel 718 makes it one of the difficult-to-cut materials. Hybrid machining processes (HMPs) can be used to overcome problems of machining of difficult-to-cut materials.1 HMPs amalgamate two or more machining mechanisms together to take combined advantages and minimize the potential disadvantages found in machining process when applied individually.2 As compared to the individual process, the vital improvements in performance characteristics can be observed in a hybrid process.3

Electro-discharge machining (EDM) is noncontactable material subtraction process suitable for machining of difficult-to-machine materials. Although in EDM, discharge energy considerably influences the surface integrity of workpiece. Microcracks induce residual stresses and change the microhardness of the subsurface, which may occur due to thermal action in EDM.4 The electro-discharge diamond grinding Department of Mechanical Engineering, Malaviya National Institute of Technology, Jaipur, India Corresponding author: Deepak Rajendra Unune, Department of Mechanical Engineering, Malaviya National Institute of Technology, Jaipur 302017, Rajasthan, India. Email: [email protected]

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Proc IMechE Part B: J Engineering Manufacture

Figure 1. Mechanism of material removal in EDDG process.8. Figure 2. Photograph of EDDG setup.

(EDDG) is a compound type HMP and is proposed by Koshy et al.5,6 to overcome surface integrity allied issues in EDM. The EDDG combines electro-discharge erosion action of EDM and mechanical abrasion of diamond grinding (see Figure 1) and significantly improves material removal rate (MRR) as compared to the EDM process.7 Koshy et al.5,6 reported that electrode rotations and efficient flushing attribute to noteworthy enhancement in MRR and surface finish. Furthermore, it is reported that the deposition of recast layer and surface modification during the process may increase the wear resistance of workpiece at low discharge power. Kozak9 reported the use of copper-bonded diamond wheel in EDDG of polycrystalline diamond and aluminum carbide and evaluated the effects of speed of the wheel, pulse current, and pulse-on-time on MRR and Ra. In this study, he reported self-dressing phenomena of grinding wheel where the grinding wheel is being dressed continuously without the need of interrupting the machining process. Yadav et al.10 used copper-bonded diamond wheel and experimentally evaluated the effects of wheel speed, pulse-current, and pulse-on-time on MRR in EDDG of high-speed steel (HSS) and reported that wheel speed and pulse-on-time are significant parameters affecting MRR. Singh et al.7,8,11 in their recent studies reported bronze-bonded diamond wheel in EDDG of HSS and WC–Co composite. They studied the effects of pulse current, pulse-on-time, duty factor, and wheel revolutions per minute (RPM) on MRR, Ra, and wheel wear rate (WWR) and shown that small current and less wheel speed were not advisable for the EDDG process in machining of the WC–Co composite. They observed that wheel glazing at low current and high wheel speed slumped the MRR capability of the diamond wheel. Kumar and Choudhury12 applied the artificial neural network (ANN) technique to predict wheel wear and Ra during EDDG of HSS and compared predicted results with regression models. They reported that the prediction ability of the regression model was poorer than that of the ANN model. Yadav and Yadava13

reported the application of slotted EDDG in face grinding mode to overcome drawbacks in EDDG such as the gathering of debris into the gap, aberrant arcing, and large wheel wear. A joint approach of ANN and non-dominated sorting genetic algorithm-II optimizes the process parameters of surface electro-discharge diamond grinding (S-EDDG) of metal matrix composite proposed by Yadav and Yadava.14 ANN, genetic algorithm, and grey relational analysis methods have been suggested by Shrivastava and Dubey15 for multiobjective optimization of the EDDG process performance during grinding of the copper–iron–graphite composite. From the literature review, it is observed that no plausible studies were conducted on machining of Inconel 718 superalloy using EDDG process. In this study, response surface methodology (RSM) and ANN models were established to predict the effect of several machining parameters on MRR and Ra using experimental results obtained based on the central composite design (CCD) design. Analysis of variance (ANOVA) test performed to evaluate significant factors in EDDG of Inconel 718. The effectiveness of developed models in predicting machining performance is compared at last.

Experimentation Surface grinding setup was fabricated on ENC-35 EDM machine that was used to perform experiments. Figure 2 shows actual photograph in-house fabricated EDDG setup. This setup consists of bronze–diamond abrasive grinding wheel, direct current motor (HP: 2, RPM: 4600 max), horizontal shaft (diameter, 32 mm), V-pulley (diameter, 32 mm) and bearing, and alternating current reversible synchronous motor (RPM: 60, 50 Hz). The relative motion between grinding wheel and material to be machined has been attained by


0.099 0.185 0.699 0.001 0.2 3.04 0.054 4.2 23.5 0.004 0.031 0.105 0.034 0.16 10.8 Percentage (%)

0.19

Al, 308 Ti, 337 B, 182 W, 429 Nb, 405 V, 411 Fe, 406 Cr, 267 P, 185 Mn, 403 Si, 251 C, 165 Al

reciprocating the machine bed with the help of an automatic feed arrangement consisting of a reversible AC synchronous motor control circuit, a relay switch, two limit switches, and regulated power supply. The workpiece was fixed on machine bed using the fixture and then dipped in the dielectric fluid (EDM oil) which majorly consists of paraffinic hydrocarbon oil. The bronze–diamond wheel was attached to the ram of the machine through special attachment, and the servo system of the Z-axis numeric controlled (ZNC) EDM machine was used to uphold the predetermined gap between the two electrodes, that is, the diamond wheel and the flat workpiece. The details of metal-bonded diamond wheel are given in Table 1. The surface grinding on workpiece was achieved by rotating the grinding wheel about the horizontal axis perpendicular to the downward motion of servo system of EDM machine with the help of variable speed permanent magnet direct current (PMDC) motor through a V-belt and pulley arrangement. The speed of the PMDC motor has been controlled with the help of a DC drive. All the experiments were carried on the flat rectangular surface (60 mm 3 10 mm) of Inconel 718 in surface grinding mode of EDDG setup. The composition of Inconel 718 has been found after conducting the optical emission scanning (OES) test and is shown in Table 2.The wheel speed (S), pulse-current (C), pulseon-time (P), and duty factor (DF) were selected as control factors, while MRR and Ra were taken as performance parameters. The effective range of each factor was chosen on the basis of pilot experimentation. Table 3 shows the actual and coded values of control factors selected for the experiments. In the present case, three levels of four control factors with seven central point of standard CCD matrix have been selected, which require total 31 experimental runs with 2p factorial runs, 2p axial runs, and seven center points (where p is the number of control factor). This design is usually used for fitting the second-order model. It is essential to second-order model to deliver good prediction throughout the region of interest. The experimental matrix (coded variables) and experimental results are shown in Table 4. High precision electronic digital balance (WENSAR HPB-310 Model) with the readability of 1 mg has been used to observe the weight difference in workpiece before and after machining. The MRR was calculated by the difference between initial weight

Co

100 mm 10 mm Diamond 32 mm Bronze 75% 80/100 3 mm

Mo

Diamond wheel diameter Thickness Abrasive used Bore diameter Bonding material Concentration Grit size Depth of abrasive

Element

Specification

Table 2. Chemical composition of Inconel 718 based on OES test.

Description

Zr, 339

Table 1. Details of metal-bonded grinding wheel.

57

3 Ni, 216

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Table 3. Experimental input parameter and their levels. Input factors

Coded level

Description

Unit

Symbol

21

0

1

Wheel speed Current Pulse-on-time Duty factor

RPM A ms %

S C P DF

600 3 50 0.58

900 6 100 0.63

1200 9 150 0.70

Table 4. Experimental layout as per CCD showing coded input parameters and responses along with comparison of actual and predicted values by ANN and RSM of MRR and Ra. Run order

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 27 28 29 30 31

S

21 0 21 21 0 1 1 21 0 1 0 1 1 21 0 0 0 0 21 0 0 1 0 21 1 0 1 21 1 0 21

C

0 0 1 21 0 21 21 21 0 21 0 1 1 21 0 0 0 0 1 0 1 1 0 1 1 21 21 21 0 0 1

P

0 21 1 1 0 21 1 21 0 1 0 21 1 1 0 0 0 1 21 0 0 21 0 21 1 0 21 21 0 0 1

DF

0 0 21 21 1 1 1 21 0 21 0 21 21 1 0 0 0 0 21 0 0 1 21 1 1 0 21 1 0 0 1

MRR (mm3/min)

Ra (mm)

Experimental

ANN

RSM

Experimental

ANN

RSM

5.64 11.54 8.25 5.03 10.86 18.95 19.25 4.27 10.95 19.45 12.25 22.37 21.45 4.87 11.75 11.65 11.24 11.54 9.09 11.16 12.09 20.24 10.43 6.64 22.75 12.83 15.74 4.53 16.54 10.76 8.73

6.18 12.04 8.68 5.10 11.89 15.95 16.58 4.47 12.35 17.43 12.35 20.39 21.02 4.24 12.35 12.35 12.35 12.66 8.06 12.35 14.14 19.54 12.74 7.20 20.16 10.56 16.81 3.62 18.52 12.35 7.83

4.61 11.25 7.97 5.01 11.25 18.31 18.31 5.01 11.25 18.31 11.25 21.27 21.27 5.01 11.25 11.25 11.25 11.25 7.97 11.25 14.62 21.27 11.25 7.97 21.27 11.66 18.31 5.01 17.90 11.25 7.97

2.82 6.91 5.21 2.52 5.43 8.20 7.09 2.14 5.98 9.73 6.13 11.19 10.24 2.44 6.10 5.98 5.88 5.77 4.55 5.98 5.35 8.78 5.22 3.32 9.42 6.42 6.94 2.27 7.25 5.87 5.37

3.36 5.67 5.00 3.00 5.74 7.05 7.80 2.25 6.04 8.37 6.04 9.62 10.37 2.42 6.04 6.04 6.04 6.42 4.25 6.04 7.04 9.05 6.31 3.67 9.80 5.04 7.62 1.67 8.73 6.04 4.42

3.34 6.01 4.21 2.46 6.01 7.82 7.82 2.46 6.01 7.82 6.01 9.56 9.56 2.46 6.01 6.01 6.01 6.01 4.21 6.01 6.89 9.56 6.01 4.21 9.56 5.14 7.82 2.46 8.69 6.01 4.21

ANN: artificial neural network; RSM: response surface methodology; MRR: material removal rate.

and final weight of the workpiece over a period of machining time. To measure the Ra value of machined surface after each experiment surface roughness tester (Taylor Hobson Surtronic-25) has been used. Surface roughness is measured perpendicular to grinding direction with 4-mm evaluation length. The grinding wheel speed was measured by a digital tachometer in RPM.

Results and discussion ANOVA ANOVA technique was performed to test the adequacy of the established models, and the outcomes of response

surface model fitting are given in Tables 5 and 6. The test for significance of the regression models, the test for significance on individual model coefficients, and the lack-of-fit test were performed using the Minitab 16 software package. The backward elimination was performed to eliminate the insignificant model terms, and the resulting ANOVA (Tables 5 and 6) for the response surface quadratic models summarizes the ANOVA of each response and shows the significant model terms. The adequacy measures R2, adjusted R2, and predicted R2 are also shown in the same tables. The goodness of fit of the model can be perceived by the coefficient of determination R2. In this case, all the values of coefficient of determination R2 are closer to 1. Clearly, we


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Table 5. ANOVA results for MRR model (only significant terms). Source

Degree of freedom (df)

Model 3 Linear 2 S 1 C 1 Square 1 C3C 1 Residual error 27 Lack of fit 5 Pure error 22 Total 30 Standard deviation = 1.08 PRESS = 43.10

Sum of squares (SS)

Mean square (MS)

F value

p value probability . F

Percentage contribution

862.30 835.45 795.87 39.58 26.85 26.85 31.61 12.09 19.52 893.91

287.43 417.72 795.87 39.58 26.85 26.85 1.17 2.418 0.887

245.51 356.80 679.80 33.80 22.93 22.93

0.00 0.00 0.00 0.00 0.00 0.00

96.46 93.46 89.03 4.43 3.00 3.00

2.72

0.046

R2 = 96.46% Adjusted R2 = 96.07% Predicted R2 = 95.18%

PRESS: prediction error sum of squares.

Table 6. ANOVA results for Ra model (only significant terms). Source

Degree of freedom (df)

Model 2.00 Linear 2.00 S 1.00 C 1.00 Residual error 28.00 Lack of fit 6.00 Pure error 22.00 Total 30.00 Standard deviation = 0.854111 PRESS = 26.2583

Sum of squares (SS)

Mean square (MS)

F value

p value probability . F

Percentage contribution

142.84 142.84 129.15 13.69 20.43 7.69 12.73 163.27

71.42 71.42 129.15 13.69 0.73 1.28 0.58

97.90 97.90 177.04 18.77

0.00 0.00 0.00 0.00

87.49 87.49 79.10 8.38

2.22

0.08

R2 = 87.49% Adjusted R2 = 86.6% Predicted R2 = 83.92%

PRESS: prediction error sum of squares.

must have 0 4 R2 4 1, with larger values being more desirable. The adjusted coefficient of determination R2 is a variation of the ordinary R2 statistic that reflects the number of factors in the model. The entire adequacy measures are closer to 1, which is in reasonable agreement and indicate adequate models. The value of the determination coefficient (R2 = 96.46%)MRR and (R2 = 87:49%)Ra indicates that only less than 3.54% and 12.51% of the total variations in MRR and Ra, respectively, are not clarified by the models. The large value of the adjusted determination and coefficient (adjusted R2 = 96.07%)MRR (R2 = 86:6%)Ra assures significance of the models. Predicted R2 is also in conformity with the adjusted R2 in both cases. Lack of fit is non-significant as required. The normal distribution of residuals (see Figure 3) confirms there are no obvious patterns indicating an outstanding adequacy of the regression model. The ANOVA (Tables 5 and 6) also shows the model terms std. dev. and PRESS. Standard deviation (‘‘Std. Dev.’’) is a square root of the error mean square, and ‘‘PRESS’’ stands for ‘‘prediction error sum of squares,’’ and it is a measure of how well the model for the

experiment is likely to predict the responses in a new experiment. Small values of PRESS are desirable. In all the cases, the values of PRESS are considerably low. ANOVA test recognizes the significant control factors that affect the MRR and Ra of Inconel 718 in EDDG in surface grinding mode at 95% confidence level. Statistically, the F test delivers a choice at a 95% confidence level to assess the significant parameters on deciding the performance characteristics. The results of ANOVA after backward elimination method for the machining performance are presented in Tables 5 and 6. From the results of ANOVA, it is observed that the wheel speed (S) is the major affecting factor to decide both the MRR and the Ra and contributes 89.03% and 79.10% on MRR and Ra, respectively, whereas current (C) contributes 4.43% and 8.38% on MRR and Ra, respectively. The similar observations were also reported by Yadav et al.10 and Singh et al.7 Pulse-ontime (P) and duty factor (DF) have less significance on the MRR and Ra of Inconel 718 in EDDG process. In EDDG, the wheel speed is observed to be major factor influencing both the MRR and the Ra due to the fact that due to rotation of grinding wheel, the number


6


Figure 3. (a) Normal probability plot of residual for MRR and (b) normal probability plot of residual for Ra.

of diamond abrasive particle comes in contact with the workpiece, thereby abrading more volume of the workpiece. Thus, both MRR and Ra will get largely influenced by the wheel speed in comparison to current, pulse-on-time, and duty factor. The rotations of the wheel also improve the flushing conditions at interelectrode gap (IEG) and effectively ejecting the molten material and debris accumulation in IEG.14,16 At high current, more discharge energy will go into the workpiece and melting more material from the workpiece and forming larger craters.16 Thus, current is also an influencing factor on both the MRR and the Ra. Therefore, the wheel speed and current are observed to be a factor influencing the MRR and Ra.

Prediction of MRR and Ra in EDDG ANN ANN is one of the widely used dominant computational tools and used to explain complex functions in numerous applications. ANN has been efficaciously adopted for solving various problems in areas like fault diagnosis, process identification, property estimation, data smoothing and error filtering, product design and development, optimization, and estimation of activity coefficients.17 The neural network is an immensely parallel distributed processing method consisting of greatly interconnected neural computing elements having the capability to learn, obtain information, and make it available for use.18 Therefore, by appreciating the computational capability of a multilayered neural network, it has been adopted in the modeling of the EDDG process and used to predict the MRR and Ra in machining of Inconel 718. In this study, the effects of wheel speed (S), current, pulse-on-time (P), and duty factor (DF) on MRR and Ra were statistically investigated in EDDG of Inconel 718. The best ANN architecture was designed by MATLAB software. The neuron in the input layer corresponds to wheel speed (S), current, pulse-on-time (P), and duty factor (DF). The output layer corresponds

Figure 4. ANN structure 4-24-2.

with MRR and Ra. In this model, the inputs are fully connected to the hidden layer and the hidden layer neurons are fully connected to the outputs. After exhaustive trails, ANN model with one hidden layer comprising 24 neurons and 4 input and 2 output neurons as shown in Figure 4 was developed on the basis of performance of the network. The training of network was done using Levenberg–Marquardt (LM) backpropagation neural network (BPNN) algorithm for fast supervised learning, and 70% of data have been used for training, whereas 15% of data were used for testing and 15% for validation each. The details of the ANN model developed are shown in Table 7. Mean square error (MSE) determined to evaluate the performance of the network which can be calculated as MSE =

N X M 1 X dj yj M 3 N i=1 j=1

ð1Þ

where dj is the desired output of the jth neuron, yj is the predicted value of jth neuron, N is the total number of training pattern, and M is the number of output nodes.19

Response surface method RSM commonly used for establishing the relationship between control factors and response variables. To fit


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Table 7. ANN model parameters. Learning conditions Object model Input neurons Output neuron Network structure Network type

MRR, Ra S, C, P, and DF MRR, Ra

Transfer function Training function Learning function

Purelin TRAINLM LEARNGDM

Feed-forward back-propagation

Learning scheme Learning rule Hidden neurons Output neurons Sample pattern vector Learning rate Marquardt adjustment parameter Maximum number of epochs to train Goal

Supervised learning Gradient descent rule 24 Two 70% (train), 15% validation, and 15% testing a: 0.0001 Mu: 0.05 500 0.0001

MRR: material removal rate.

Figure 5. Surface plots of (a) MRR versus wheel speed (S) and current (C) and (b) surface plot of Ra versus wheel speed (S) and current (C).

the second-order polynomial equation to the experimental data and to recognize the relevant model terms for MRR and Ra, a backward elimination method was used in the Minitab 16 software. By performing the backward elimination method, the insignificant model terms were eliminated, and a reduced model for each response having only the significant model terms was obtained. The CCD matrix was used to obtain MRR and Ra responses in EDDG of Inconel 718 after 31 data measurement from machining conditions. Codings + 1 and 21 were used as the upper limit and lower limit of input factors, respectively. RSM fits polynomial models for the existing data into equation y = b0 +

k X i=1

bi xi +

k X i=1

bii x2i +

XX i

bij xi xj

ð2Þ

j

where y is the predicted response, b0 is a constant, bi is the linear coefficient, bii is the squared coefficient, bij is the cross product coefficient, and k is the number of factors.19 The final models were developed using only significant factors through the backward elimination method and were given by equations (3) and (4), respectively, as

MRR = 11:255 + 6:65S + 1:48C + 1:88C2

ð3Þ

Ra = 6:014 + 2:68S + 0:87C

ð4Þ

The experimental results compared with the ANN and RSM predicted results (see Table 4). It was observed that the predictions by ANN as well as RSM have a good agreement with the experimental results. Surface plots for MRR and Ra in three dimensions (3D) by varying the two factors wheel speed and current at constant pulse-on-time and duty factor for EDDG of Inconel-718 are as shown in Figure 5(a) and (b). These figures indicate that the MRR, as well as Ra, increases with the increase in wheel speed and current. Five confirmation experiments that have been executed to inquire the validation of the experimental results at diverse machining conditions are shown in Figure 6(a) and (b) keeping pulse-on-time and duty factor constant. The levels (coded) of control factors, viz., wheel speed and current chosen for confirmation experiments are shown in Figure 6(a) and (b). The experimental results and predicted values by both RSM and ANN models have less error, suggesting that established models are appropriate and reliable in the prediction of MRR and Ra.


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Figure 6. (a) Comparison of the experimental versus predicted values by ANN and RSM for MRR for validation runs. (b) Comparison of the experimental versus predicted values by ANN and RSM for Ra for validation runs.

Table 8. Comparison of prediction between RSM and ANN. Model summary and prediction error

RSM

ANN 3

Absolute standard deviation (ABSD) Percentage of root mean square error (RMSE)

MRR (mm /min)

Ra (mm)

MRR (mm3/min)

Ra (mm)

0.068 1.009

0.068 0.785

0.033 1.009

0.020 0.785

ANN: artificial neural network; RSM: response surface methodology; MRR: material removal rate.

Comparison of predicted MRR and Ra by ANN and RSM The predicted values of MRR and Ra by both ANN and RSM models are listed in Table 4. The prediction

performance of ANN and RSM models in terms of absolute standard deviation (ABSD) and percentage root mean square error (RMSE) is shown in Table 8. Figures 7 and 8 demonstrate a correlation between experimental responses and predicted results from


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9 predicting MRR and Ra of EDDG of Inconel 718 using RSM and ANN. The following vital conclusions are derived from the investigations: 1.

2. Figure 7. Comparison of experimental MRR with predicted results from RSM and ANN.

3.

4.

5. Figure 8. Comparison of experimental Ra with predicted results from RSM and ANN.

RSM and ANN. The comparative analysis demonstrates that both models implemented well offering stable responses in predicting the independent factors with respect to the response. However, as compared to RSM method, the ANN technique offered superior fitting to the output responses. Table 8 shows the percentage of RMSE and the ABSD for models developed by both methods, and again ANN modeling of EDDG process of Inconel 718 for predicting responses is highly rationalized. Both constructions of RSM and an ANN model involve less computing time and money with the aid of Minitab 16 evaluation software packages and MATLAB software, respectively. The ANN model involves some iterative computations, and correct choice of the ANN structure results in the accurate results.

Conclusion In this article, in-house fabricated EDDG experimental setup was used in surface grinding mode to evaluate the MRR and Ra in machining of Inconel 718, and CCD experimental design was employed to investigate the influence of various input factors on the performance characteristics. Two models were developed for

The experimental study was executed using the CCD design based on RSM. The developed mathematical model based on experimental data for MRR and Ra was found to give satisfactory results in prediction of the EDDG process performance, that is, these models predicted the MRR and Ra which are in close agreement with the experimentally obtained results. Also, a multi-layered feed-forward backpropagation neural network was used to construct an EDDG process model, and based on performance a model with 4-24-2 structure was selected for modeling of EDDG. The confirmation experiments performed for validation of both RSM and ANN models show that both techniques found to give appropriate and reliable predictions of MRR and Ra. From the ANOVA results, the wheel speed (S) is found to be the key factor having an effect on the MRR and Ra in EDDG of Inconel 718 and contributes 89.03% and 79.10%, respectively. The increase in the wheel speed (S) results in increased MRR and Ra. The current (C) is the next most significant factor contributing 4.43% and 8.38% on the MRR and Ra respectively. Developed ANN and RSM models found to be adequate for accurate and robust predictions of MRR and Ra during machining of Inconel 718 in EDDG process and are well in agreement with the experimental results. However, as compared to RSM method, the ANN technique offered superior fitting to the output responses.

Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The author(s) received no financial support for the research, authorship, and/or publication of this article. References 1. Feyzi T and Safavi SM. Improving machinability of Inconel 718 with a new hybrid machining technique. Int J Adv Manuf Tech 2012; 66: 1025–1030. 2. Unune DR and Mali HS. Current status and applications of hybrid micro-machining processes: a review. Proc IMechE, Part B: J Engineering Manufacture. Epub ahead of print 2 September 2014. DOI: 10.1177/ 095440541454614. 3. Lauwers B. Surface integrity in hybrid machining processes. Proc Eng 2011; 19: 241–251.


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4. Rajurkar KP, Sundaram MM and Malshe AP. Review of electrochemical and electrodischarge machining. Proc CIRP 2013; 6: 13–26. 5. Koshy P, Jain VK and Lal GK. Mechanism of material removal in electrical discharge diamond grinding. Int J Mach Tool Manu 1996; 36: 1173–1185. 6. Koshy P, Jain VK and Lal GK. Grinding of cemented carbide with electrical spark assistance. J Mater Process Tech 1997; 72: 61–68. 7. Singh GK, Yadava V and Kumar R. Diamond face grinding of WC-Co composite with spark assistance: experimental study and parameter optimization. Int J Precis Eng Man 2010; 11: 509–518. 8. Singh GK, Yadava V and Kumar R. Multiresponse optimization of electro-discharge diamond face grinding process using robust design of experiments. Mater Manuf Process 2010; 25: 851–856. 9. Kozak J. Abrasive electro discharge grinding (AEDG) of advanced materials. Arch Civil Mech Eng 2002; 2: 83– 101. 10. Yadav SKS, Yadava V and Narayana VL. Experimental study and parameter design of electro-discharge diamond grinding. Int J Adv Manuf Tech 2008; 36: 34–42. 11. Singh GK, Yadava V and Kumar R. Robust parameter design and multi-objective optimisation of electrodischarge diamond face grinding of HSS. Int J Mach Mach Mater 2012; 11: 429–433. 12. Kumar S and Choudhury SK. Prediction of wear and surface roughness in electro-discharge diamond grinding. J Mater Process Tech 2007; 191: 206–209. 13. Yadav RN and Yadava V. Machining performance of slotted-electrical discharge diamond face grinding of Al/ SiC/Gr composite. Mater Manuf Process 2014; 29: 585– 592.

14. Yadav RN and Yadava V. Multiobjective optimization of slotted electrical discharge abrasive grinding of metal matrix composite using artificial neural network and nondominated sorting genetic algorithm. Proc IMechE, Part B: J Engineering Manufacture 2013; 227: 1442–1452. 15. Shrivastava PK and Dubey AK. Intelligent modeling and multiobjective optimization of electric discharge diamond grinding. Mater Manuf Process 2012; 28: 1036–1041. 16. Yadav RN and Yadava V. Intelligent modelling and prediction of slotted-electrical discharge diamond grinding of aluminium-silicon carbide-graphite composite. Int J Abras Technol 2013; 6: 93. 17. Ranganathan S, Senthilvelan T and Sriram G. Evaluation of machining parameters of hot turning of stainless steel (type 316) by applying ANN and RSM. Mater Manuf Process 2010; 25: 1131–1141. 18. Bhattacharyya B and Pradhan BB. Modelling of microelectrodischarge machining during machining of titanium alloy Ti–6Al–4V using response surface methodology and artificial neural network algorithm. Proc IMechE, Part B: J Engineering Manufacture 2009; 223: 683–693. 19. Youssefi S, Emam-Djomeh Z and Mousavi SM. Comparison of artificial neural network (ANN) and response surface methodology (RSM) in the prediction of quality parameters of spray-dried pomegranate juice. Drying Technol 2009; 27: 910–917.

Appendix 1 Notation R2 Ra

determination coefficient average surface roughness


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