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Vol. 8. Issue 5.
Pages 4354-4362 (September - October 2019)
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Vol. 8. Issue 5.
Pages 4354-4362 (September - October 2019)
Original Article
DOI: 10.1016/j.jmrt.2019.07.046
Open Access
Experimental investigation of machined hole and optimization of machining parameters using electrochemical machining
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T. Sathish
Vesta Research Institute, Aranthangi, Pudukkottai, Tamil Nadu, India
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Figures (13)
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Tables (20)
Table 1. Compositional of SS 304.
Table 2. Orthogonal array layout.
Table 3. Variation of parameters.
Table 4. Value of response parameters.
Table 5. Average diameter.
Table 6. Response parameters of voltage study.
Table 7. Signal to noise ratio of response parameters.
Table 8. Response table of S/N ratio for MRR.
Table 9. Response table for mean for MRR.
Table 10. Statistical regression analysis.
Table 11a. Analysis of variance for SN ratios.
Table 11b. Response table for mean for overcut.
Table 12a. Statistical regression analysis for overcut vs voltage, feed rate, duty ratio.
Table 12b. Response table of S/N ratio for overcut.
Table 13. Analysis of variance for SN ratios.
Table 14. Response table for S/N ratio for conicity.
Table 15. Response table for mean for conicity.
Table 16. Statistical regression analysis for Conicity Vs Voltage, Feed Rate, Duty Ratio.
Table 17. Analysis of Variance for SN ratios.
Table 18. Optimum parameters for SS304.
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Abstract

The electrochemical micromachining in non-conventional machining which suffers from process control in micro level. In addition, the work material i.e. stainless steel provides high strength, high toughness and adherence to tool material, hence machining of such material is arduous. However, the use of stainless steel offers its availability in the field of aerospace fuel injection and orthodontic application, since it possess superior qualities. In this paper, the optimization of micro drilling process is carried out in stainless steel by considering certain metrics like feed rate, voltage and duty ratio. The geometric characteristics to drill the tool depends on response parameters like overcut, removal rate of material and conicity. Such parameter determines both the geometric and machining characteristics of the drill bit. The study is evaluated to observe the effect of response and duty ratio parameters such as material removal rate (MRR), machining time and overcut. Further, the conicity is analyzed using VMS images. Finally, the proposed work establishes duty cycle in pulsed electrochemical micromachining domain of hard materials and tests its performance.

Keywords:
Micro drilling process
Material removal rate
Stainless steel
Unconventional manufacturing process
Full Text
1Introduction

Unconventional manufacturing process is a process assembly, which eliminates additional material using mechanical, electrical, thermal, or chemical methods. The utilization of sharp cutting tool is avoided strictly by the conventional manufacturing or non-traditional machining process.

The reverse process of electrochemical plating is the electro-chemical machining, which is a controlled anodic electrochemical atomic level dissolution process. The stainless steel work-piece is conductive electrically using a shape tool, which experiences high current flow using an electrolyte (basic or acidic), which offers low potential difference. The anodic dissolution inside electrolyte cell is used to remove the work-piece metal (anode) in a controlled manner using a tool (cathode). The cell passes the current, when the electrolyte is pumped between the tool through the gap, which dissolves the work-piece metal. The electrochemical plating is used widely in machining jobs and machining hard materials.

Various electrochemical micromachining studies involving the drilling of hole in titanium grade 2 metal [1], stainless steel SS-316L and copper alloy CA-173 are removed with closed control loop and high frequency voltage pulse [2]. Stainless steel [3,4], die steel [5], Inconel grade 718, titanium grade 2 [6], titanium grade 5 [7] (Ningsong, 2012), and stainless-steel grade 304 through polydimethylsiloxane mask (Chen et al., 2015). Other studies involves the use of 3D micro features to produce internal grooved barrel shaped holes [8], flat electrolyte jet machining process [9], electrode side insulation and pulse on time diameter size (Park et al., 2008), magnetic field (Shi et al., 2002) using the action of Lorentz force and electrical field force to form curvilinear motion result in electric current density distribution, ultra-shot pulse [3] and finally mathematical model [10].

Various parametric influential study involves the use of precision based electrochemical micromachining (De Silva et al., 1998) to develop narrow inter-gap electrodes. Hocheng et al. [11] proposed precision based electrochemical machining to erode hundreds of μm on metal surface. Jain et al. [12] proposed temperature, conductivity and current density based electrochemical machining. Further, electrochemical micro machining studies on electrolyte flow rate, electrolyte concentration and feed rate [7], wired electrochemical machining internal channels (Ningsong, 2012) and Taguchi L9 orthogonal array [13]. The pulsed electrochemical machining involves prediction of minimum machining allowance [14], nickel plates [15] and nickel based super alloy [16], very short pulses (Hyun et al., 2004), impact of pulse period and voltage [17].

From the above review, it is found that extensive work is been applied to dissolve stainless-steel grade 2 micro drilling. Further this requires higher amount of work in micro range, which is difficult to control the electrode gap and it further deviates the machining area distribution. The fabrication electrodes is another challenge in terms of conicity, overcut, MRR and machining time consumption to drill micro hole. The most work fails to address the area of variation in duty cycle and drilling of micro hole using pulse current and this paper address this problem.

This paper involves the investigation of electrochemical micro hole drilling in titanium using parametric optimization and studying the duty cycle influence. The main aim involves microhole drilling in stainless steel with tungsten electrode using acidified electrolyte. It involves input parametric optimization to drill most accurate hole and studying the effect of material removal rate (MRR), conicity, circularity, roughness of hole surface in the work piece. Further, the duty cycle effect is studied in terms of its machining and geometric characteristics.

2Materials and methods2.1Selection of workpiece material

The workpiece is selected from pure stainless steel 304 metal and testing work piece is SS 304. The percentage of composition of SS 304 is shown in Table 1. The sodium nitrate (NaNO3) dissolved in distilled water is chosen as electrolyte placed in a still bath and hence during machining process, electrolyte flow is not measured. The tool (electrode) used for drilling holes in the stainless steel sheet is copper wire. The electrode acts as cathode during machining process with 0.5mm diameter available as wound spool wire been cut into small lengths and fixed in a copper tool holder.

Table 1.

Compositional of SS 304.

Composition  Percentage (%) 
Carbon  0.08 max 
Manganese  2.00 max 
Phosphorus  0.045 max 
Sulphur  0.030 max 
Silicon  0.75 max 
Chromium  18–20 
Nickel  8–12 
Nitrogen  0.10 max 
Iron  Balance 

The electrochemical machining is carried out in the following setup with three subsystems, namely, electrolytic bath and circulation system, tool feed system and inter electrode gap control system, shown in Fig. 1.

Fig. 1.

Electrochemical machine setup.

(0.12MB).

The methodology for this proposed investigation is shown in Fig. 2.

  • Input parameters include pulsating power supply for ECM and inter electrode gap of 15–20μm diameter.

  • Response parameters include conicity, MRR and overcut, where

    • Conicity=((DENTRYDEXIT)/2H)×100

      where DENTRY is the hole entry diameter (μm), DEXIT is the hole exit diameter (μm) and H is the workpiece thickness.

    • MRR=(WBWA)/t

      where WB is the weight before work piece machining, WA is the weight after work piece machining and t is the machining time.

    • Overcut is the difference between mean diameter of hole at entry point and the diameter of tool.

    • Duty ratio=TON/(TON+TOFF)

      • Set 1: Duty ratio=0.6

      • Set 2: Duty ratio=0.7

      • Set 3: Duty ratio=0.8

Fig. 2.

Methodology investigation.

(0.24MB).
3Observation

Taguchi method is used for experimental design with well-defined guidelines over special set of orthogonal arrays. This stipulates the conduction of minimal experiments to attain full information affecting the performance of the parameter. The orthogonal arrays choose the combination level of input variables for each experiments, where the layout is given in Table 2.

Table 2.

Orthogonal array layout.

L9 (33) orthogonal array
  Independent variablesPerformance parameter value 
Experiments  Variable 1  Variable 2  Variable 3   
P1 
P2 
P3 
P4 
P5 
P6 
P7 
P8 
P9 
3.1Experimental design

The parametric study combines Taguchi method with Grey relational method. Here, voltage, duty ratio and feed rate is used as control variable. The parametric variation at L9 orthogonal array is shown in Table 3

Table 3.

Variation of parameters.

Parameter  Description  Level 1  Level 2  Level 3 
Voltage (V)  16  17  18 
Feed rate (mm/min)  0.5  0.6  0.7 
Duty ratio  0.6  0.7  0.8 

The response parameters is measured and shown in Table 4. The average diameter of the hole at entry and exit places are averaged and shown in Table 5.

Table 4.

Value of response parameters.

Experiment no.  Voltage (V)  Feed rate (mm/min)  Duty ratio  MRR (g/min)  Overcut (mm) 
L1  16  0.5  0.6  0.000002  0.565 
L2  16  0.6  0.7  0.00000115  0.47 
L3  16  0.7  0.8  0.00000173  0.403 
L4  17  0.5  0.7  0.000001009  0.394 
L5  17  0.6  0.8  0.00000171  0.375 
L6  17  0.7  0.6  0.00000174  0.441 
L7  18  0.5  0.8  0.00000139  0.3795 
L8  18  0.6  0.6  0.00000137  0.5005 
L9  18  0.7  0.7  0.00000117  0.402 
Table 5.

Average diameter.

Diameter at entry (mm)  Diameter at exit (mm)  Average diameter (mm)  Conicity (mm) 
1.065  0.9395  1.00225  10.45 
0.97  0.7515  0.86075  18.20 
0.903  0.68  0.7915  18.58 
0.894  0.502  0.698  32.66 
0.875  0.6605  0.76775  17.87 
0.941  0.856  0.8985  7.08 
0.8795  0.743  0.81125  11.37 
1.0005  0.734  0.86725  22.20 
0.902  0.843  0.8725  5.9 
3.2Trend analysis

The trend analysis for parametric optimization involves following parameter, namely, achieved diameter, overcut, material removal rate and conicity. This section measures the varying parametric level with respect to voltage, duty ratio and feed rate. This machining and geometric properties are studied using 9 experiments, where the progression of each response parameter is studied.

3.2.1Trend of diameter

Here, the workpiece and tool does not contact each other and hence it is difficult to control the dissolution area. From Fig. 3, it is seen that diameter increases with increased supplied current, since it increases the dissolution rate. This is mainly due to the flushing of by products using the electrolyte, which contribute towards the tool diameter by considering electrolyzing current and inter electrode gap.

Fig. 3.

Trend of diameter.

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3.2.2Trend of overcut

The overcut phenomenon represents the material removed during electrolysis, and the results are shown in Fig. 4. This is a non-contact process and chemical machining leads to material removal in the area and around the electrode. Hence it leads to increase in micro hole diameter, which is called overcut, which increases the current at TON and the values are very low. When the rate reduces, there is a convergence of hole diameter when TOFF increase towards the tool diameter.

Fig. 4.

Trend of overcut.

(0.06MB).
3.2.3Trend of MRR

The material dissolution speed is influenced by most dominating input, shown in Fig. 5. The MRR follows nonlinear increasing trend, which has no total agreement with electrolysis of Faraday's law due to TON variation and narrow electrode gap. The efficiency of dissolution reduces at TON values and lower voltage. The material erosion is gradually declined when the material dissolution occurs with lesser value and leads to inhomogeneous conductivity distribution in electrolyte.

Fig. 5.

Trend of MRR.

(0.07MB).
3.2.4Trend of conicity

The rate of removal of metal at varying hole length is conicity, which can be termed as gap resistivity. This affects the servo feed of the cathode tool and low charges are obtained at knife edged holes, and high charges are obtained at straight holes reducing the hole conicity. This has increased the removed metal ions precipitation on hole inner surface, which prevents side machining and helps in conicity reduction (Fig. 6).

Fig. 6.

Trend of conicity.

(0.06MB).
3.3Influence of study of voltage

Voltage is the influential parameter to conduct experiments by making the parameters constant and changing the voltage levels. The influence of MRR and machining time is studied by various experiments conducted at different levels (16, 17 and 18V), where the results are shown in Table 6.

Table 6.

Response parameters of voltage study.

Voltage  Feed rate (mm/min)  Duty ratio  TON  TOFF  Machining time  MRR (g/min) 
16  0.5  0.6  10  20:24:22  0.000002 
17  0.6  0.7  14  17.10.07  0.00000173 
18  0.7  0.8  20  19.30.41  0.00000117 

The erosion rate of the material from the workpiece is influenced by the current passage and increasing linearly with current. This phenomenon is due to the influence of higher current available at frontal gap between workpiece and electrode. The machining time gets reduced when the voltage increases and this is due to presence of higher voltage and earlier erosion, which leads to reduced machining time.

3.4Circularity and its trend

Circularity measures the extent of machined hole deviation from the perfect circle. When the diameter is different at entry/exit, the circularity errors are estimated at entry/exit. The circularity analyses duty ratio at the entry/exit points. The multiple factors in electrochemical machining leads to poor examination by circularity task. From the observation, the circularity rises and plunges into maximum if the pulse is on time. In terms of dissolution rate, when the drilling process starts, there is a stability in material dissolution and it gets improved when more holes are drilled.

3.5VMS circularity analysis in drilled holes

The scanning electron microscopic images is used to examine the drilled holes, shown in Fig. 7. It is seen that when the rate of dissolution is stabilizing the process gets steady and recast layer is seen in area due to poor by product flushing during machining. This layer is seen clearly in second specimen and occurred due to clogging of electrolyte with eroded SS particles and it also gets redeposited at the same time.

Fig. 7.

VMS images of machined hole

(0.16MB).
4Result and discussion

Finally, the response like overcut, MRR and conicity is measured from observed data. Finally, statistical analysis is done over the estimated results and the signal to noise ratio for all the 3 response factor is shown in Table 7.

Table 7.

Signal to noise ratio of response parameters.

Experiment no.  S/N ratio for MRR  S/N ratio for overcut  S/N ratio for conicity 
−96.00  6.027  −23.93 
−96.54  7.521  −25.72 
−104.35  8.276  −19.27 
4.1Effect of input parameters on MRR

The effect of input parameters over MRR is shown in Table 8. It is seen that the voltage obtains the first rank and has maximum effect on MRR, little effect on feed rate and least effect on duty ratio. The results are plotted in Fig. 8.

Table 8.

Response table of S/N ratio for MRR.

Levels  Voltage (V)  Feed rate (mm/min)  Duty ratio 
−96.00  −96.74  −102.14 
−96.54  −103.80  −98.84 
−104.35  −96.35  −95.91 
Delta  8.35  7.44  6.24 
Rank 
Fig. 8.

S/N ratio for MRR.

(0.09MB).

The response table of MRR is shown in Table 9. The mean ratio estimation depends on larger value of better quality characteristics.

Table 9.

Response table for mean for MRR.

Levels  Voltage (V)  Feed rate (mm/min)  Duty ratio 
0.000016  0.000015  0.000013 
0.000015  0.000010  0.000011 
0.000009  0.000015  0.000016 
Delta  0.000007  0.000005  0.000005 
Rank 

It is seen from Table 9 that the voltage obtains the first rank and has maximum effect on MRR, little effect on feed rate and least effect on duty ratio (Fig. 9).

Fig. 9.

Mean for MRR.

(0.07MB).

Table 10 shows the regression analysis to estimate difference between the level 1, 2 and 3 effects on MRR, overcut and conicity.

Table 10.

Statistical regression analysis.

Predictor  Coefficient  SE Coefficient  T  P 
Constant  −98.9632  2.470  −40.063  0.001 
Voltage 16  2.9617  3.493  0.848  0.486 
Voltage 17  2.9617  3.493  0.693  0.560 
Feed ratio 0.5  2.2256  3.493  0.637  0.589 
Feed ratio 0.6  −4.8340  3.493  −1.384  0.301 
Duty ratio 0.6  −3.1815  3.493  −0.911  0.459 
Duty ratio 0.7  0.1246  3.493  0.036  0.975 

S=7.411; R2=72.8%; R2 (adj)=0.0%.

4.2Effect of input parameters on overcut

The effect of input parameters over overcut is shown in Table 12. It is seen that the duty ratio obtains the first rank and has maximum effect on duty ratio, little effect on voltage and least effect on feed rate. The results are plotted in Fig. 10.

Fig. 10.

S/N ratio for overcut.

(0.06MB).

The response table of overcut is shown in Table 11. The mean ratio estimation depends on smaller value for better quality characteristic (Fig. 11).

Table 11a.

Analysis of variance for SN ratios.

Source  DF  Seq SS  Adj SS  Adj MS  F  P 
Voltage  130.88  130.88  65.44  1.19  0.456 
Feed rate  105.38  105.38  52.69  0.96  0.510 
Duty ratio  58.45  58.45  29.22  0.53  0.259 
Residual error  109.84  109.84  54.92     
Total  404.54         
Table 11b.

Response table for mean for overcut.

Levels  Voltage (V)  Feed rate (mm/min)  Duty ratio 
0.4793  0.4462  0.5022 
0.4033  0.4485  0.4220 
0.4273  0.4153  0.3858 
Delta  0.0760  0.0332  0.1163 
Rank 
Fig. 11.

Mean ratio of overcut.

(0.06MB).

Table 10 shows the regression analysis to estimate difference between the level 1, 2 and 3 effects on MRR, overcut and conicity (Table 13).

Table 12a.

Statistical regression analysis for overcut vs voltage, feed rate, duty ratio.

Predictor  Coefficient  SE coefficient  T  P 
Constant  7.2750  0.07235  100.549  0.000 
Voltage 16  −0.8047  0.10232  −7.864  0.016 
Voltage 17  0.6319  0.10232  6.176  0.025 
Feed ratio 0.5  −0.1200  0.10232  −1.173  0.362 
Feed ratio 0.6  −0.2452  0.10232  −2.396  0.139 
Duty ratio 0.6  −1.2476  0.10232  −12.193  0.007 
Duty ratio 0.7  0.2462  0.10232  2.406  0.138 

S=0.2171; R2=99.2%; R2 (adj)=96.8%.

Table 12b.

Response table of S/N ratio for overcut.

Levels  Voltage (V)  Feed rate (mm/min)  Duty ratio 
6.470  7.155  6.027 
7.907  7.030  7.521 
7.448  7.640  8.276 
Delta  1.437  0.610  2.249 
Rank 
Table 13.

Analysis of variance for SN ratios.

Source  DF  Seq SS  Adj SS  Adj MS  F  P 
Voltage  3.2299  3.22987  1.61494  34.28  0.028 
Feed rate  0.6237  0.62375  0.31187  6.62  0.131 
Duty ratio  7.8595  7.85950  3.92975  83.41  0.012 
Residual error  0.0942  0.09423  0.04711     
Total  11.8073         
4.3Effect of input parameters on Conicity

The effect of input parameters over conicity is shown in Table 14. It is seen that the duty ratio obtains the first rank and has maximum effect on conicity, little effect on voltage and least effect on feed rate. The results are plotted in Fig. 12.

Table 14.

Response table for S/N ratio for conicity.

Levels  Voltage (V)  Feed rate (mm/min)  Duty ratio 
6.470  7.155  6.027 
7.907  7.030  7.521 
7.448  7.640  8.276 
Delta  1.437  0.610  2.249 
Rank 
Fig. 12.

S/N ratio for conicity.

(0.06MB).

The response table of conicity is shown in Table 15. The mean ratio estimation depends on smaller value of better quality characteristic (Fig. 13).

Table 15.

Response table for mean for conicity.

Levels  Voltage (V)  Feed rate (mm/min)  Duty ratio 
15.74  18.16  13.24 
19.20  19.42  18.92 
13.16  10.52  15.94 
Delta  6.05  8.90  5.68 
Rank 
Fig. 13.

Mean for conicity.

(0.06MB).

Table 16 shows the regression analysis to estimate difference between the level 1, 2 and 3 effects on MRR, overcut and conicity (Table 17).

Table 16.

Statistical regression analysis for Conicity Vs Voltage, Feed Rate, Duty Ratio.

Predictor  Coefficient  SE coefficient  T  P 
Constant  −22.9719  2.291  −10.026  0.010 
Voltage 16  −0.6829  3.240  −0.211  0.853 
Voltage 17  −1.1359  3.240  −0.351  0.759 
Feed ratio 0.5  −0.9540  3.240  −0.294  0.796 
Feed ratio 0.6  −2.7517  3.240  −0.849  0.485 
Duty ratio 0.6  1.5353  3.240  0.474  0.682 

S=6.873; R2=49.5%; R2 (adj)=0.0%.

Table 17.

Analysis of Variance for SN ratios.

Source  DF  Seq SS  Adj SS  Adj MS  F  P 
Voltage  15.19  15.19  7.597  0.16  0.861 
Feed rate  66.64  66.64  33.322  0.71  0.586 
Duty ratio  10.67  10.67  5.337  0.11  0.898 
Residual error  94.49  94.49  47.245     
Total  187.00         

The testing material attains maximum MRR, minimum over rate and reduced conicity, shown in Table 18.

Table 18.

Optimum parameters for SS304.

Physical requirements  Voltage (V)  Feed rate (mm/min)  Duty ratio 
MRR  16  0.7  0.8 
Overcut  16  0.6  0.6 
Conicity  17  0.6  0.8 
5Conclusion

In this paper, the micro holes are drilled in pure stainless steel using fuel injector nozzle, which attains maximum accuracy. Further, optimization of certain electrical parameters is agreed to attain optimization of micro-hole drilling. The stainless steel work piece attains best MRR and overcut values based on central feed rate value, duty ratio and high voltage level. The current, duty ratio and voltage response parameters are influenced by order of independent variables influencing the response parameters is (a) current, (b) voltage and (c) duty ratio. Further, it is observed that conicity of the micro hole is reduced considerably from trails 1–9. Increased stability has further leads to fall in conicity and side erosion are prevented using product accumulation. Further, the impact of duty cycle is evaluated based on response parameters like MRR, machining time and overcut. It is seen that as the value of duty cycle increases, the machining time has reduced and MRR remarkably increases with dissolution rate rise. The erosion rate of material determines the convergent behaviour of tool diameter. In this study, the non-convergence exists as the erosion increases and overcut roughness of the surface increases further, which is influenced by duty cycle variation. With increasing erosion, the generation of extreme hydrogen bubbles increases the Ra value. The lowered duty cycle is found, which provides improved results based on response parameters. Finally, the pulsed electrochemical micromachining with TOFF component flushes the by-products with better surface quality and anodic shape control.

Conflicts of interest

The authors declare no conflicts of interest.

.

[18–26].

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Journal of Materials Research and Technology

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