Accuracy and quality of micro-holes in vibration assisted micro-electro-discharge drilling of Inconel 718

Accepted Manuscript Accuracy and Quality of Micro-holes in Vibration Assisted Micro-Electro-Discharge Drilling of Inconel 718 Deepak Rajendra Unune, Chandrakant Kumar Nirala, Harlal Singh Mali PII: DOI: Reference:

S0263-2241(18)31123-0 https://doi.org/10.1016/j.measurement.2018.11.067 MEASUR 6110

To appear in:

Measurement

Received Date: Revised Date: Accepted Date:

26 September 2018 18 November 2018 21 November 2018

Please cite this article as: D.R. Unune, C.K. Nirala, H.S. Mali, Accuracy and Quality of Micro-holes in Vibration Assisted Micro-Electro-Discharge Drilling of Inconel 718, Measurement (2018), doi: https://doi.org/10.1016/ j.measurement.2018.11.067

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Accuracy and Quality of Micro-holes in Vibration Assisted Micro-Electro-Discharge Drilling of Inconel 718 Deepak Rajendra Unune1*, Chandrakant Kumar Nirala2, Harlal Singh Mali3 1

Department of Mechanical-Mechatronics Engineering, The LNM Institute of Information Technology Jaipur, Jaipur-302031, Rajasthan, India

2

School of Mechanical, Materials and Energy Engineering, Indian Institute of Technology Ropar, Ropar-140001, Punjab, India.

3

Department of Mechanical Engineering, Malaviya National Institute of Technology Jaipur, Jaipur-302017, Rajasthan, India

*corresponding author Email: [email protected] Orcid ID: http://orcid.org/0000-0002-9341-1962

ABSTRACT Achieving micro-features on superalloy like Inconel 718, with dimensionally high accuracy, has never been easy using a conventional machining technique. Micro-electro-discharge drilling (µEDD) has emerged as one of the key machining technique for such superalloys. Although the process has the proven potentials, quality and accuracy of micro-holes deteriorate due to high tool wear and process become unproductive due to the impasse of debris and unsteady machining conditions, especially for high aspect ratio hole drilling. To overcome such issue, anticipating a better performance, a low-frequency vibration assisted µEDD is proposed for machining of Inconel 718. Performance measures, such as material removal rate (

), electrode wear ratio (

), overcut, and taper angle are analyzed as

results of the µEDD operation performed by choosing gap-voltage, capacitance, electrode rotation speed (ERS), and vibrational frequency (VF) as control factors. The Box–Behnken design was used to plan the experiments. The effect of low-frequency vibration on accuracy and surface quality of fabricated micro-holes was also discussed and compared with those achieved by without vibration assistance to the process. An enhancement in the performance of low-frequency vibration assisted µEDD due to improved flushing, debris evacuation, and stable machining conditions is noticed. Keywords: micro electro discharge drilling, low-frequency vibration, micro-holes, accuracy, surface quality. 1

1 Introduction Nickel-based superalloys, like Inconel 718, have captured the attention of researchers in the micromachining domain due to the material’s high resistivity to corrosion and temperature. Inconel 718 has diversified applications in numerous engineering areas like aerospace, automobile, medical, chemical, etc. [1]. It is extensively used in aerospace industries in an elevated temperature environment as turbine blades, guide vanes, afterburners, etc. [2]. These parts require high-aspect-ratio miniaturized holes for enhanced cooling resulting in excellent operating efficiency. However, fabrication of micro-holes in Inconel 718 using conventional drilling is very challenging as the elevated temperature at tooltip results in built-up edge formation during drilling. The micro-electro-discharge drilling (µEDD) is one of the most efficient and competent unconventional machining technology having the capability to produce holes from few tens of microns to few mm regardless of the hardness of the material [3]. The studies based on the fabrication of micro-holes in aerospace alloys like Inconel 718 are very vital from the aircraft and aerospace industries perspective. There numerous studies on µEDD available in the literature investigating properties of fabricated micro-holes related the effects of type of generators (i.e. transistor and RC-type), effects of input process parameter like pulse current, voltage, pulse-on-time (POT), duty factor, electrode rotational speed (ERS), capacitance, feed rate etc., effects of electrode materials, effects of dielectric fluid properties, and so on. Kuppan et al. [2] experimentally examined the deep hole drilling capability of Inconel 718 using the tubular copper electrodes of Ø3mm in electro-discharge machining (EDM). They developed empirical models for MRR and surface roughness (SR) revealing that the current, duty factor, and ERS are significant for MRR while SR governed by current and POT.

2

Yilmaz and Okka [4] claimed that single channel brass, as well as copper electrodes, attributes in higher MRR and low EWR as compared to multichannel electrodes. However, multichannel electrodes yield superior surfaces of drilled micro-holes in Inconel 718 and Ti– 6Al–4V. Recently, Bassoli et al. [5] presented the effects of electrode shape and geometry on µEDD process performance. They claimed that electrode shape and geometry significantly affect the gap flushing efficiency. Ay et al. [6] suggested optimum µEDD parameters for better taper ratio and hole dilation for Inconel 718 using grey relational optimization. They suggested that low values of discharge current and pulse duration attributed to reduced crack and damage characteristics of micro-holes. Performance of different electrode materials in µEDD of Inconel 718 and stainless steel was investigated by Unune et al. [7] and D’Urso et al. [8] and D’Urso et al. [9], respectively. These three studies suggested that the tool electrodes with high electrical conductivity are suitable for higher MRR while the tool electrodes with low thermal conductivity are suitable for lower EWR. The response surface methodology (RSM) has been extensively used, in recent years, to develop empirical models, investigate individual as well as interaction effects of input process parameters on the responses, and for optimization of the performance of the machining processes [10-13]. The application of RSM in modelling, investigating and optimization of µEDD is also available in the literature. Mondol et al. [14] developed empirical models for MRR, EWR, overcut and taper angle for µEDD of titanium alloy (Ti– 6Al–4 V). They investigated the individual and interaction effects of voltage, capacitance and threshold on the responses and then optimized the performance of the process. Barman et al. [15] used RSM for modelling, investigation and optimization of surface finish parameters in µEDD of Ti-6Al-4V. Though the micro-electro-discharge machining (µEDM) processes are being used in micromachining applications, the few limitations such as low MRR, high tool wear, poor surface 3

quality, etc. limit the wide applications of these processes. To enhance the process capabilities, hybridization of micro-electro-discharge machining (µEDM) has been presented in several studies, e.g. ultrasonic vibration assisted µEDM [16], low-frequency vibration assisted µEDM [17, 18], magnetic assisted µEDM[19], etc. These studies have demonstrated distinct benefits due to hybridization of the µEDM process. Hybridization allows the elimination or reduction of potential disadvantages of the micro-EDM such as low machining rate, tool wear, micro-cracks on the machined workpiece, dimensional inaccuracy, etc. Unune and Mali [20] have presented classification, benefits and uses of hybrid machining processes in their review paper and concluded that these processes would drive miniature fabrication technology in the coming years. Application of vibration assistance in µEDM to form a hybrid ‘assisted type µEDM’ have been first reported by Gao and Liu [21]. They presented a quantitative analysis presenting the benefits of ultrasonic vibration in µEDM on increased MRR. They reported that due to enhanced flushing ability due to vibration assistance the MRR increases to eight times higher as compared to typical µEDM. However, generations of ultrasonic vibrations are very complex and costlier. Therefore, the investigations on low-frequency workpiece vibration in µEDD performed by several authors to improve flushing conditions and unstable machining in deep hole drilling. Jahan et al. [18] assessed low workpiece vibration assistance in µEDD of tungsten carbide using an analytical model and compared results with experimental results. It is claimed that low-frequency vibration reduces the machining time due to better removal of debris from the machining zone. Lee et al. [22] claimed that low-frequency vibration (1070Hz) reduces the machining time by 70% as compared to with at non-vibration assisted machining without tool rotation. The benefits, including improved MRR and reduced tool wear, of low-frequency workpiece vibration in micro-EDM milling and micro-wire EDM have been demonstrated recently by the authors in [23] and [24], respectively. In both studies, 4

it is found that that use of low-frequency workpiece vibration facilitates effective flow of the dielectric and removal of debris from IEG. From the literature, it was concluded that the µEDD is an efficient process for fabrication of micro-holes in Inconel 718 superalloy. The low-frequency vibration can enhance the performance of the µEDD significantly. However, very few studies are available on lowfrequency assisted µEDD, and no study has been found reporting statistical significance of low-frequency vibration in µEDD. Also, rare work is available reporting the interaction effects of the vibrational frequency with other µEDD parameters on the response variable. Nevertheless, it is important to investigate, the effects of a process parameter of a lowfrequency vibration assisted µEDD on accuracy and quality of drilled holes in Inconel 718. Therefore, the aim of this work is set to establish empirical models for MRR, EWR, overcut and taper angle in low-frequency vibration assisted µEDD of Inconel 718 and to investigate the individual and interaction effect of the vibrational frequency with other process parameters on the MRR, EWR, overcut and taper angle.

2 Experimental details Machining conditions, the technique of design of experiments, input process parameters and process responses are discussed in this section and briefed in Table 1. Table 1 Machining conditions Tool (cathode)

Workpiece (anode)

Materials

Pure Tungsten

Inconel 718

Dimensions

Ø300 µm

30mm X 10mm X 1.5mm

Dielectric Material

TOTAL DIEL7500IN (commercially available)

Number of experiments

29, based on Box–Behnken experimental design matrix (24 at side points, and 5 runs at center position)

Control factors

Voltage ( ), capacitance (

), ERS (RPM), and vibrational 5

frequency (

)

MRR (mm3/min), EWR (%), overcut (mm) and taper angle

Responses

(degree) 2.1 Workpiece A workpiece specimen (30mm X 10mm X 1.5mm) was prepared cut from Inconel 718 using a high-speed diamond cutter (Isomet-4000, Buehler USA). The workpiece specimen after polishing and grounding using a polishing machine (MetaServ-250, Buehler USA) used to perform experiments. The composition (Table 2) of the prepared Inconel 718 specimen was acquired by a glow discharge spectrometer (GDS500A; LECO Instruments UK). Table 2 Chemical composition of Inconel 718 Component Ni Weight %

55.4

Component V Weight %

0.085

C

Si

Mn

P

Cr

Fe

Mo

0.03

0.084

0.043

0.001

23.8

3.7

13.3

Nb

W

Co

Ti

Al

Zr

2.78%

0.259

0.259

0.814

0.194

0.078

2.2. Experimental setup and measuring apparatus The µEDD was executed on a multipurpose machine tool (DT110; Mikrotools Singapore). The commercially available dielectric fluid “TOTAL DIEL7500IN” was used as dielectric oil. The prepared specimen of Inconel 718 was fastened on the fixture perpendicular to tungsten (W) electrode of Ø300 µm. Owing to the low wear rate; tungsten electrodes produce accurate holes with smaller overcut and taper angle as compared to copper or brass electrodes due to less EWR [8, 25, 26]. After performing each experiment, the tool electrode tip was dressed using the wire-electro discharge grinding (WEDG). The WEDG has performed at electrode positive polarity to ensure more material removal from Tungsten electrode [27, 28]. The experimental setup of the vibration assisted µEDD along with the isometric view of

6

Tungsten tool electrode is shown in Fig. 1. The image of Tungsten tool electrode has been acquired using a field-emission scanning electron microscope (FE-SEM) (Gemini 500; ZEISS, Germany) at 500X magnification. The vibration device supplied along with the machine tool utilizes the electromagnetic actuation principle and is accomplished of producing low-frequency vibration (0 to 180 Hz). An accelerometer along with a data acquisition system was used for vibration amplitude and frequency measurement purpose. The initial amplitude of the workpiece was found to be 11 µm at 80 Hz and reduced to 5 µm at 160 Hz. The workpiece fixture was clammed on vibration device, and the whole system was placed in EDM tank. After performing the experiments, the fabricated micro-holes were observed under the Digital Microscope (AxioCam AX10; ZEISS Germany) using the 20X lens. To measure the radius of the hole, AxioCam Software was used. The surface quality of microholes was analysed with images taken with the FE-SEM (Nova NanoSEM 450; FEI USA) at 500X magnification.

7

Fig.1 a. Vibration assisted micro-electro discharge drilling experimental setup [7], b. An enlarged view of Tungsten tool electrode of diameter 0.3mm. The MRR, EWR, surface quality and accuracy (in terms of overcut and taper angle) of microholes were investigated to analyze the performance of Inconel 718 in the µEDD. The MRR, EWR, overcut and taper angle were calculated by subsequent equations [29].

Where,

is entry-side radius of hole,

is exit-side radius,

is plate thickness, and

is

drilling time.

Where TW is volumetric tool wear rate calculated as wear,

is frontal electrode

is electrode diameter.

The overcut (

Where

,

) was calculated as:

is average diameter calculated as,

The taper angle (θ) was determined as:

2.3 RSM and Box-Behnken design

8

The RSM is a pack of statistical and mathematical techniques which are convenient to model and analyze the problems. Box and Wilson [30] first introduced the RSM in 1951 and later, it has been extensively applied experimental design purpose. In RSM, initially the design of experiments is produced, and later a mathematical model is fitted to outcomes of experimental results, and finally, the developed model is verified using statistical techniques[31, 32]. Using RSM, a minimum number of experiments can be planned, to generate adequate response data, which is statistically acceptable as a result [33]. The RSM includes the following steps [34]: i.

Defining independent control variables and the desired responses,

ii.

Implementation of the design of experiments,

iii.

Performing regression analysis using the quadratic model of RSM,

iv.

Finding the significant control variables that influence the response using the analysis of variance (ANOVA) then,

v.

Determining the condition of the quadratic model of RSM and whether the model requires screening variables or not, and finally,

vi.

Optimization of performance characteristics.

Generally, first order models yield a lack-of-fit, whereas quadratic models are almost always sufficient for industrial uses [35]. RSM fits a polynomial model for the experimental results into the following equation: …(6) where, y is the predicted response; β0 is constant; βi is the linear coefficient; βii is the squared coefficient; βij is the cross product coefficient,

for i=1,2,…,n are the control variables and

k is the number of factors [35].

9

In the RSM, several design methods that are available including central composite design, Box–Behnken design (BBD), Doehlert matrix, Plackett–Burman design, and full or fractional factorial design (FFD). The BBD method is more efficient as compared to CCD and FFD [36]. The BBD was utilized to plan the experiments due to its less experimental runs, the simplicity of experiments eluding extreme values of control factors causing costly or unmanageable run due to physical process constraints, and allowing the study of an interaction effect between control factors in the same number of runs. 2.4 Experimental plan The voltage (A), capacitance (B), ERS (C), and vibrational frequency (D) were designated as input parameters while MRR, EWR, overcut, and taper angle were selected as response variables. For this study, the gap-voltage is termed as voltage. The input parameters and their range, shown in Table 3, were decided based on the literature, preliminary experiments, manufacturer’s handbook, and machines constraints. In general, the levels of input parameters should have a constant gap, over the considered range, for a symmetrical design of experimental variables. Therefore, capacitance values of 0.0001 µF, 0.2 µF, and 0.4 µF were chosen, considering the available values on machine tool. Though, the capacitance value 0.0001 µF produces very low discharge energy, the similar value was also used by some researchers in their work [14, 25, 37]. The Design-Expert® software was used to produce the experimental plan. Total 29 experimental runs were performed as per BoxBehnken design with four control factors each varying at three levels. Out of 29 runs, 24 runs correspond to the side points, and 5 runs replicate the center position five times. The experimental matrix along with measured responses is provided in Table 4. Table 3 Independent control factors, and their levels Level Control factors

Symbol

Unit

-1

0

1 10

Voltage Capacitance ERS Vibrational frequency

A B C D

V µF RPM Hz

80 0.0001 100 0

105 0.2 500 80

130 0.4 900 160

Table 4 Experimental matrix along with responses Run order

Voltage (V)

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

0 0 0 -1 -1 0 1 1 0 0 0 1 0 1 0 -1 0 0 -1 -1 0 0 0 1 0 0 0 -1 1

Control factors (coded values) Capacitance Electrode Vibrational (µF) rotational frequency speed (Hz) (RPM)

0 1 -1 -1 0 0 0 0 0 0 1 0 -1 0 0 1 -1 -1 0 0 1 0 1 1 0 0 0 0 -1

0 -1 -1 0 -1 1 -1 0 -1 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 -1 0 0 1 0

0 0 0 0 0 -1 0 1 -1 0 -1 0 0 -1 0 0 -1 1 -1 1 1 1 0 0 1 0 0 0 0

Performance measures MRR EWR Overcut Taper (mm3/min) (%) (mm) angle (degree)

0.00945 0.00548 0.00108 0.00205 0.00651 0.00868 0.00153 0.00404 0.00105 0.01001 0.00749 0.00691 0.00161 0.00202 0.00899 0.00978 0.00095 0.00263 0.00484 0.00927 0.00947 0.00938 0.00976 0.00380 0.00903 0.00882 0.00985 0.00858 0.00124

0.238 0.236 0.141 0.134 0.142 0.262 0.227 0.257 0.203 0.248 0.275 0.266 0.148 0.255 0.230 0.275 0.140 0.147 0.230 0.170 0.268 0.264 0.275 0.250 0.191 0.226 0.250 0.257 0.230

0.014 0.024 0.019 0.019 0.023 0.014 0.021 0.016 0.020 0.014 0.016 0.015 0.012 0.020 0.013 0.011 0.014 0.016 0.013 0.015 0.016 0.012 0.011 0.020 0.024 0.013 0.014 0.011 0.013

2.473 2.512 2.058 1.848 2.371 2.297 2.504 2.495 2.463 2.510 2.498 2.398 2.023 2.475 2.553 2.494 2.141 1.898 2.026 2.420 2.512 2.418 2.505 2.509 2.509 2.607 2.339 2.382 2.136

3 Analysis of Variance The statistical significance of input parameters on responses was determined using Analysis of variance (ANOVA) test. 11

3.1 ANOVA for MRR, EWR, overcut, and taper angle The sequential model sum of squares, lack-of-fit test, and model summary statistics tests were performed to evaluate the adequacy of the models which suggested the adequacy of quadratic models. Therefore, quadratic models were analyzed using ANOVA to determine the significant model terms and results reveal that there are several insignificant terms (both individual and interaction terms) in the models. The insignificant terms corresponding to higher p-value have trivial contribution to the model. The backward elimination procedure was executed to eliminate such insignificant terms from models thereby improving the fit of the models. The results of ANOVA for MRR, EWR, overcut, and taper angle after backward elimination are summarized in Table 5, Table 6, Table 7 and Table 8, respectively. Table 5 The ANOVA table for MRR Source Model

Sum of Square 3.256E-004

A-Voltage

df

F Value

12

Mean Square 2.713E-005

3.851E-005

1

3.851E-005

50.23

B-Capacitance

1.093E-004

1

1.093E-004

142.56

C-Electrode rotational speed D-Vibrational Frequency AB AC BC CD A2

3.414E-005

1

3.414E-005

44.52

2.939E-005

1

2.939E-005

38.33

6.695E-006 2.736E-006 3.513E-006 1.327E-005 3.859E-005

1 1 1 1 1

6.695E-006 2.736E-006 3.513E-006 1.327E-005 3.859E-005

8.73 3.57 4.58 17.31 50.33

B2

6.211E-005

1

6.211E-005

81.01

C2 D2 Residual Lack of Fit

1.116E-005 1.280E-005 1.227E-005 1.119E-005

1 1 16 12

1.116E-005 1.280E-005 7.668E-007 9.323E-007

14.55 16.69

pvalue < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.0093 0.0771 0.0481 0.0007 < 0.0001 < 0.0001 0.0015 0.0009

3.45

0.1210

Pure Error Cor Total

1.081E-006 3.379E-004

4 28

2.702E-007

Std. Dev. Mean C.V. % PRESS

8.756E-004 6.011E-003 14.57 4.810E-005

35.39

R-Squared Adj R-Squared Pred R-Squared Adeq Precision

Percentage contribution significant 11.83 33.57 10.49 9.03 2.06 0.84 1.08 4.08 11.85 19.08 3.43 3.93 not significant

0.9637 0.9365 0.8576 17.599

12

Table 6 The ANOVA table for EWR Source

Sum of Square

df

Model

0.059

8

Mean F Value Square FOR EWR 7.385E-003 25.13

A-Voltage B-Capacitance

6.427E-003 0.034

1 1

6.427E-003 0.034

21.87 116.24

C-Electrode rotational speed D-Vibrational Frequency AB AC AD B2 Residual Lack of Fit

9.127E-003

1

9.127E-003

31.06

3.606E-004

1

3.606E-004

1.23

< 0.0001 0.0001 < 0.0001 < 0.0001 0.2811

3.674E-003 1.447E-003 9.468E-004 2.939E-003 5.877E-003 5.418E-003

1 1 1 1 20 16

3.674E-003 1.447E-003 9.468E-004 2.939E-003 2.938E-004 3.386E-004

12.50 4.92 3.22 10.00

0.0021 0.0382 0.0878 0.0049

2.95

0.1525

Pure Error Cor Total

4.594E-004 0.065

4 28

1.149E-004

Std. Dev. Mean C.V. % PRESS

0.017 0.22 7.73 0.015

R-Squared Adj R-Squared Pred R-Squared Adeq Precision

pvalue

Percentage contribution significant 10.89 57.63 15.47 0.61 6.23 2.45 1.60 4.98 not significant

0.9095 0.8733 0.7680 19.658

Table 7 The ANOVA table for overcut Source

Sum of Square 4.160E-004

F Value

12

Mean Square 3.467E-005

A-Voltage B-Capacitance C-Electrode rotational speed D-Vibrational Frequency AB

1.408E-005 2.083E-006 2.613E-004

1 1 1

1.408E-005 2.083E-006 2.613E-004

13.32 1.97 247.17

3.333E-007

1

3.333E-007

0.32

5.625E-005

1

5.625E-005

53.20

AC AD BC CD A2 C2 D2

9.000E-006 9.000E-006 9.000E-006 9.000E-006 1.051E-005 3.795E-005 8.514E-006

1 1 1 1 1 1 16

9.000E-006 9.000E-006 9.000E-006 9.000E-006 1.051E-005 8.514E-006 1.057E-006

8.51 8.51 8.51 8.51 9.94 8.05 32.79

Residual Lack of Fit

1.692E-005 1.572E-005

12 4

1.310E-006 3.000E-007

4.37

Pure Error Cor Total

1.200E-006 4.330E-004

Model

Std. Dev. Mean C.V. % PRESS

1.028E-003 0.016 6.44 7.312E-005

df

28 12

32.79

pvalue < 0.0001 0.0022 0.1795 < 0.0001 0.5822

Percentage contribution significant 3.38 0.50 62.81 0.08

< 0.0001 0.0101 0.0101 0.0101 0.0101 0.0062 0.0119 < 0.0001 0.1327

13.52 2.16 2.16 2.16 2.16 2.53 9.12 2.05

not significant

3.467E-005

R-Squared Adj R-Squared Pred R-Squared Adeq Precision

0.9609 0.9316 0.8311 19.549

13

Table 8 The ANOVA table for taper angle Source

df

Model

Sum of Square 1.03

F Value

5

Mean Square 0.21

0.079 0.71

1 1

0.079 0.71

8.32 74.81

D-Vibrational Frequency AD B2 Residual Lack of Fit

0.010

1

0.010

1.09

pvalue < 0.0001 0.0084 < 0.0001 0.3082

A-Voltage B-Capacitance

0.035 0.19 0.22 0.18

1 1 23 19

0.035 0.19 9.536E-003 9.388E-003

3.68 20.01

0.0675 0.0002

0.92

0.6105

Pure Error Cor Total

0.041 1.25

4 28

0.010

Std. Dev. Mean C.V. % PRESS

0.098 2.36 4.14 0.43

R-Squared Adj R-Squared Pred R-Squared Adeq Precision

21.58

Percentage contribution significant 7.67 68.93 0.97 3.40 18.45 not significant

0.8243 0.7861 0.6552 14.639

The higher model F values for all model along with their p-value values less than 0.0001 (see, Table 5-8) point out that the established models are significant. By a very small chance of 0.01%, the model F value this large could arise owing to noise. The significant parameters are represented by p-value values less than 0.05. The significant model terms and their percentage contribution for established models are shown in Table 5-8. The lack-of-fit values indicate its significance or non-significance compared to pure error. The lack-of-fit values for all established models are non-significant, i.e. p-value value for lack-of-fit of all models are higher than 0.100. A non-significant lack-of-fit is essential as it permits the model to fit the experimental data and advises that there may be a trivial systematic variation that remains unaccounted for a particular model. Hence, the established models can be accepted. The determination coefficient (R2) were calculated to check the acceptability of the developed models. The R2 values for developed models found to be close to 1 representing a superior fit of models. Thus, the developed models are trustworthy representatives of

14

experimental data. The coefficient of variation (C.V.) of the model is known as the ratio of the standard deviation to the mean. The small of C.V. values show improved accuracy and reliability experiments. Further, the Adeq Precision values represent the signal-to-noise ratio, and generally, values greater than 4 are appropriate and states an adequate signal for the model. Overall, from the ANOVA results, it can be concluded that the developed models can be used to navigate the design space and estimate the values of the MRR, EWR, overcut, and taper angle within the limits of the parameter studied. 3.2 Regression equation for MRR, EWR, overcut and taper angle The empirical relation between the performance measures (i.e. MRR, EWR, overcut, and taper angle) and the control factors (actual terms) can be expressed by the following secondorder polynomial equations. The coefficients of the control factor in Eq. (7,8,9, and 10) were calculated by Design-Expert® package afterwards analysis of the experimental results shown in Table 5.

15

3.2 Validation of models The eight additional experiments were executed at different control factors settings within the range of chosen factors for validation of the established MRR, EWR, overcut and taper angle models and results are presented in Table 9. The dissimilar control factors combinations were chosen than the one used for forming the regression models. The prediction error (%) calculated using the following Eq. (11):

The predicted results found to be in good agreement with validation results with prediction errors were less than ±7% demonstrating the reliability of the developed models.

16

Table 9 Results of validation experiments for performance measures Expt. no.

Voltage (V)

Capacitance (µF)

Electrode rotational speed (RPM)

MRR (mm3/min)

Vibrational frequency (Hz)

EWR (%)

Overcut (mm)

Taper Angle (degree)

Expt. Value

Pred. value

% error

Expt. Value

Pred. value

% error

Expt. Value

Pred. value

% error

Expt. Value

Pred. value

% error

1

100

0.1

500

160

0.00712

0.00743

-4.32

0.173

0.182

-5.42

0.015

0.016

-6.67

2.156

2.294

-6.43

2

100

0.4

300

80

0.00866

0.00831

4.04

0.260

0.249

4.07

0.017

0.017

0.0

2.446

2.489

-1.72

3

120

0.1

700

0

0.00431

0.00404

6.25

0.243

0.226

7.11

0.016

0.015

2.67

2.452

2.338

4.63

4

100

0.1

300

80

0.00664

0.00642

3.42

0.165

0.175

-6.27

0.017

0.017

5.02

2.209

2.246

-1.71

5

105

0.4

700

80

0.00975

0.01033

-5.99

0.281

0.277

1.43

0.012

0.012

-2.01

2.575

2.505

2.74

6

105

0.4

700

160

0.00938

0.00958

-2.19

0.279

0.272

2.71

0.013

0.013

2.67

2.690

2.534

5.79

7

100

0.1

900

30

0.00708

0.00662

6.49

0.218

0.228

-4.38

0.012

0.012

3.80

2.259

2.216

1.94

8

120

0.1

500

0

0.00270

0.00260

3.62

0.232

0.218

6.09

0.015

0.016

-6.24

2.361

2.338

0.96

17

4 Results and discussion In the µEDD process, MRR, EWR along with the accuracy of fabricated holes are important performance measures due to their acute effects in industrial applications. In this section, the influence of individual input parameters and their interactions on the responses has been discussed using perturbation plot and three-dimensional (3D) response curves. The perturbation plots and response curves have been plotted using MATLAB® software. 4.1 Effect of input parameters and their interaction on MRR The perturbation plot, Fig.2, displays the effect of the individual parameter on MRR. The graph is plotted by varying each parameter within its range and keeping other parameters at the midpoint (coded value 0). It can be seen that the MRR is very sensitive to voltage and capacitance as compared to electrode rotational speed (ERS) and vibrational frequency (VF). The causes behind these results are deliberated in succeeding paragraphs while discussing the interactive effects.

Fig. 2 Perturbation plot for MRR

18

The interactions between the voltage and capacitance ‘AB’, voltage and ERS ‘AC’, capacitance and ERS ‘BC’ and ERS and VF ‘CD’ have considerable contribution to MRR (see, Table 5) and the 3D plots corresponding to these interactions are shown in Fig.3 (a), (b), (c), and (d) respectively. The other interaction terms have not been considered (removed using backward elimination method from the model) as they do not have a significant effect on MRR. The interaction effect of voltage and capacitance is shown in Fig.3 (a). The MRR rises with a rise in capacitance. However, MRR initially rises with a rise in voltage but after an optimal value declines with a further rise in voltage value. The voltage governs the breakdown value upon which the current streams over the gap to initiate the plasma channel. The interelectrode gap (IEG) for spark actuation predominantly governed by the voltage rather than governing the MRR. Upon reach of optimal value, the voltage leads to secondary sparking among the trapped debris in the spark gap and electrode and thus reduces the MRR [38]. The similar observation of voltage also noted in interaction voltage and ERS (Fig.3 b). The discharge energy (DE) engendered in the RC pulse circuit is a product of capacitance and voltage (

) [23, 29]. Upon reaching breakdown voltage, the capacitor releases the DE.

Thus, the amount of DE governed by the capacitance value. Therefore, higher the capacitance, higher will be the MRR [29]. Hence, the MRR rises faster at higher capacitance values (Fig.3 a).

19

(a)

(b)

(c)

(d)

Fig. 3. (a), (b), (c), and (d) shows the response graph of MRR

The response surface in Fig. 3 (b,c,d) exemplifies that an increase in ERS stimulates the rise in MRR. The higher centrifugal force generated at high-ERS aids the dielectric to evacuate the debris trapped in between IEG. An agitation effects attributed by higher ERS drives the dielectric into the discharge zone. Also, the increased ERS promotes the disturbance of dielectric due to increased tangential velocities. Therefore, along with evacuating the debris and molten metal in the discharge zone, the agitation effect results in more efficient sparks in the machining zone and attributes in increase MRR [39, 40]. The Fig. 3 (d) shows the interaction effect between the ERS and VF. It has been observed that very low MRR is achieved at lower values of ERS and VF. The adhesion, i.e. coupling 20

between the workpiece and electrode could lead to the above results. The molten metal and debris cause adhesion leading to electrical short among electrode and workpiece and constrains the insulation recovery of the machine tool. To overcome the short circuit, the machine has to retract the spindle opposite to feed direction to sustain the gap distance amid electrode and workpiece. Thus, frequent short circuits attribute to longer machining time. With higher VF, the quicker retrieval from adhesion can be attained. The higher speed of the expansion and contraction of vibration device at high VF avoids adhesion amongst electrode and workpiece and also, causes the ejection of debris and molten metal from the IEG. 4.2 Effect of input parameters and their interaction on EWR The perturbation plot for EWR is shown in Fig. 4. A steep slope for capacitance indicates that the EWR is very sensitive to capacitance as compared to other factors. The EWR upsurges with the rise in voltage, capacitance, and ERS, however, declines with the rise in VF. The reasons behind these observations have been discussed in the next paragraph.

Fig.4 Perturbation plot for EWR The ANOVA results (Table 6) confirm that the interactions influencing the EWR model significantly are those between voltage and capacitance ‘AB’, voltage and ERS ‘AC’, and 21

voltage and VF ‘AD’. It has been observed in Fig. 5 (a) that the EWR is increased with an increase in both voltage as well capacitance, but EWR is highly sensitive to capacitance. The charging and discharging is governed by the capacitance, and thus, it is a most significant factor in micro-EDM. At high values of capacitance, deeper craters formed in workpiece enhancing MRR as more DE acts in the gap, and thereby also increasing the relative electrode wear. The increase of EWR with an increase in discharge energy is also reported by Ay et al. [6]. It can also be noticed that from Fig. 5 (a) and (b), TWR rises with a rise in voltage. As already discussed, the more discharge energy (

) will wear more electrode at

higher voltage values. The EWR decreases to some extent with an increase in VF. The normal discharge pulses rose by 40% and short pulses reduced by 80% as compared to without vibration assisted µEDD reported by Jahan et al. [18]. This result is attributed due to effective flushing of debris and reduced adhesion between electrode and workpiece in the presence of vibration. The increased normal pulses and decreased short pulses results in stable machining and reduced EWR. The EWR increases with increase in ERS (Fig. 5 b) due to effective sparking condition attributed at higher ERS leading to more wear of electrode.

(a)

(b)

22

(c) Fig. 5 (a), (b), and (c) shows the response graph of EWR

4.3 Effect of input parameters and their interaction on overcut The perturbation plot for overcut is shown in Fig. 6. It indicates that the overcut is more sensitive to ERS as compared to other factors. The overcut decreases with increase in ERS. The overcut initially decreases up to center points of voltage, capacitance, and VF, but then increases will further increase of these factors.

Fig.6 Perturbation plot for overcut

23

The interaction effect between the voltage and capacitance ‘AB’, voltage and ERS ‘AC’, voltage and VF ‘AD’, capacitance and ERS ‘BC’, and ERS and VF ‘CD’ are significant in the overcut model and are shown in Fig. 7 (a, b, c, d, and e) respectively. The overcut influence the capability of a workpiece to attain better dimensional accuracy along with finishing. By observing, capacitance and voltage effect on overcut from Fig. 7 (a, b, and c), it can be inferred that the lower amount of discharge energy is more appropriate to yield lower overcut. The similar observation also reported by Maity et al. [37]. At low DE, lower workpiece volume will be ejected per discharge generating narrower crates and thus, resulting in lower overcut. Also, the discharge column built at higher DE continued for longer as compared to one at poor DE results in bigger ionization effect and expanded microhole.

(a)

(b)

(c)

(d)

24

(e) Fig. 7 (a), (b), (c), (d), and (e) shows the response graph of overcut

Recently, Dave et al. [41] reported that the stationary electrode condition is better for lower overcut. However, they did not report reasons behind such observation. In this study, we found that the overcut increase with an increase in ERS and high ERS values are appropriate for lower overcut while µEDD of Inconel 718. The overcut increases while drilling deep micro-holes since expelling of all debris with side flushing not effective and due to trapped debris, the increased width of discharge column along with secondary sparking results in widened overcut [42]. As discussed earlier, the higher ERS expel debris due to centrifugal force, provide fresh dielectric in IEG, and causes turbulence of dielectric and therefore, reduces the overcut. Similarly, high VF is recommended for lower overcut as high VF also attributes in effective debris evacuation and reduced workpiece-electrode adhesion (Fig. 7 e). 4.3 Effect of input parameters and their interaction on taper angle

25

Fig.8 Perturbation plot for taper angle Figure 8 shows the perturbation plot for taper angle. It indicates that the overcut is more sensitive to ERS as compared to other factors. The ANOVA result shows that only the interaction effect between voltage and VF ‘AD’ is significant in taper angle model that is shown in Fig. 9. The corner wear of the electrode results in taperness of micro-holes. From Fig. 8 and 9, it can be analyzed that taper angle increases with DE which is equal to

.

The wide and deep craters formed in workpiece due to higher DE leads to short circuits and arcing into secondary sparking [42]. From Fig. 9, it has been perceived that taper angle rises with a rise in VF. The similar observation also reported by Jahan et al. [18]. This could be due to higher VF values the debris inside the micro-holes travels from the bottom of the hole towards the top of the hole along edges of the electrode and causes the secondary sparking among debris and workpiece again, thereby, increasing diameter at the top of the hole.

26

Fig. 9 The response graph of taper angle

5 Accuracy of fabricated micro-holes The microscopic images of micro-holes fabricated at different VF, ERS and DE settings and are presented in Table 10. Each experiment is repeated for three times and the standard deviation from the expected diameter of the hole (i.e. 300 µm) also mentioned. These images were taken from the top side of the micro-holes. By observing images of the micro-holes at different DE and different frequency, it is clear that both DE and VF have considerable effects on the accuracy of micro-holes fabricated. With the increase in DE, the diameter of micro-holes found to be increasing, i.e. increase in overcut. At low DE, lower workpiece volume will be removed per discharge, thereby generating narrower crates. This result in lower overcut as compared to one at high DE. Also, the discharge column built at high DE continues for longer as compared to one at small DE resulting in bigger ionization effect and expanded micro-hole. Table 10. Accuracy of micro-holes at different machining conditions DE

Vibrational Frequency & electrode rotational speed. Without vibration & 100 rpm 80 Hz & 500 rpm

160 Hz & 900 rpm

27

50 µJ

Diameter: 447 µm (Deviation: 147)

Diameter: 437 µm (Deviation: 137)

Diameter: 418 µm (Deviation: 118)

Diameter: 509 µm (Deviation: 200)

Diameter: 460 µm (Deviation: 160)

Diameter:435 µm (Deviation: 135)

Diameter:540 µm (Deviation: 240)

Diameter:468 µm (Deviation: 168)

Diameter:488 µm (Deviation: 188)

500 µJ

1280 µJ

At the rim of the hole, more burrs due to resolidification of debris and craters were observed at high DE. Therefore, it is recommended that low DE values are suitable for achieving micro-holes with better accuracy and surface quality. It can also be observed that with an increase in ERS and vibrational frequency the overcut of the fabricated micro-hole decreases, i.e. diameter of fabricated holes decreases. In EDM drilling, the debris, and molten metal get trapped in IEG at side flushing conditions and result in expanded discharge column width. Therefore, causes an increase in the diameter of micro-holes. As discussed earlier, the higher ERS and VF results in efficient expelling of debris and subsequently generating accurate

28

micro-holes due to reasons including high centrifugal force, the supply of fresh dielectric in IEG, and turbulence of dielectric, etc.

6 The surface quality of fabricated micro-holes The surface quality at the entrance of the micro-holes fabricated by the µEDD on Inconel 718 with different settings of control factors is shown in Fig. 10 (a and b). Fig. 10 (a) shows the effects of voltage, capacitance, and ERS at different combinations without low-frequency vibrations. It can be clearly witnessed that at higher DE values result in the poor surface quality of micro-holes. As already discussed, at higher DE the discharge column remains for longer resulting expanded holes, thus, it can be observed that with an increase in discharge energy the diameter at the entrance of micro-holes increased significantly. At the rim of the hole, more burrs due to resolidification of debris and craters were observed at high DE without vibration assistance. A:100 B:0.1 C:900 D:0

380 µm

A:105 B:0.2 C:500 D:380

386 µm

A:105 B:0.2 C:100 D:0

468 µm

(a) A:100 B:0.1 C:900 D:80

367 µm

A:105 B:0.4 C:500 D:0

483 µm

A:130 B:0.2 C:500 D:160

426 µm

(b)

29

Fig. 10 Surface quality of fabricated micro-holes (a) without vibration assistance, (b) with vibration assistance The surface quality of micro-holes with vibration assisted µEDD was significantly improved (Fig.10 b) as compared with that without vibration assistance. At low DE and with middle values of vibrational frequency, the best surface quality with fewer burrs at the rim of microholes perceived. The low-frequency vibration assists in expelling debris and molten metal during the µEDD process and also reduces the adhesion of workpiece and electrode. The cross-sectional view of micro-holes fabricated at same discharge energy value (500 µJ) with vibration (80 Hz) and without vibration assistance in 6 mm thick plate is presented in Fig. 11 (a and b). The presence of resolidified debris and craters attached to the inner-wall of the micro-holes can be seen for without vibration assistance. Besides, the surface appears to heat-affected and comprises of black spots (Fig. 11 (a)). Whereas, the surface with vibrationassisted μEDD at similar condition is smoother (Fig.11 (b)).

(a)

30

(b) Fig. 11 Cross-sectional view of the micro-holes (a) without vibration assistance (b) with vibration assistance

7 Conclusion In this study, low-frequency vibration assisted µEDD was performed workpiece vibration to examine the effects of vibrational frequency and other control factors viz. voltage, capacitance, and electrode rotational speed. Effects are analyzed on MRR, EWR, overcut, and taper angle by performing experiments for micro-holes on Inconel 718 using Box– Behnken experimental method. The quadratic models for MRR, EWR, overcut, and taper angle were established which represents the experimental results reliably with prediction errors less than ±7 %. The capacitance was found to be a most significant factor for MRR, EWR, and taper angle models contributing 33.57%, 57.63%, 68.93%, respectively. The lowfrequency vibration assistance improves the MRR and reduces the EWR, overcut and taper angle in the μEDD. It is found that the interaction of vibrational frequency with other control factors significantly affect the process performance. The application of low-frequency workpiece vibration results in oscillations of the dielectric and debris permitting the fresh dielectric supply in IEG and effective removal of debris and molten metal from IEG. It is also found the low-frequency vibration assistance yields in a better accuracy and surface quality of fabricated micro-holes using µEDD. For the overall better performance of µEDD, while 31

drilling Inconel 718, use of an optimum range of 40-50 Hz of low-frequency vibration is recommended. Finally, it can be concluded that the low-frequency vibration assisted µEDD is an efficient method for fabrication of high-aspect-ratio micro-holes.

Acknowledgement: Authors would thanks the Materials Research Centre and the Advanced Manufacturing and Mechtronics Laboratory at Malaviya National Institute of Technology, Jaipur for providing the facilities for conducting this work. Authors also thanks Indian Institute of Technology, Patna for providing Scanning Electron Microscope facility for taking tool electrode image.

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35

36

Highlights: 

Role of vibration assistance in μEDD process performance was investigated.



The quadratic models for responses of vibration assisted μEDD were established.

 Micro-holes with good quality and accuracy were obtained with vibration assistance.

37