The Scientific World Journal
Volume 2015 (2015), Article ID 895696, 6 pages
http://dx.doi.org/10.1155/2015/895696

Research Article

Optimization of Processing Parameters in ECM of Die Tool Steel Using Nanofluid by Multiobjective Genetic Algorithm

V. Sathiyamoorthy,¹ T. Sekar,² and N. Elango³

¹Department of Mechanical Engineering, Dr. Navalar Nedunchezhiyan College of Engineering, Tholudur 606 303, India
²Department of Mechanical Engineering, Thanthai Periyar Government Institute of Technology, Vellore 2, India
³Department of Mechanical Engineering, UCSI University, North Wing, 56000 Kuala Lumpur, Malaysia

Received 29 November 2014; Accepted 5 January 2015

Academic Editor: Venkatesh Jaganathan

Copyright © 2015 V. Sathiyamoorthy et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Formation of spikes prevents achievement of the better material removal rate (MRR) and surface finish while using plain NaNO₃ aqueous electrolyte in electrochemical machining (ECM) of die tool steel. Hence this research work attempts to minimize the formation of spikes in the selected workpiece of high carbon high chromium die tool steel using copper nanoparticles suspended in NaNO₃ aqueous electrolyte, that is, nanofluid. The selected influencing parameters are applied voltage and electrolyte discharge rate with three levels and tool feed rate with four levels. Thirty-six experiments were designed using Design Expert 7.0 software and optimization was done using multiobjective genetic algorithm (MOGA). This tool identified the best possible combination for achieving the better MRR and surface roughness. The results reveal that voltage of 18 V, tool feed rate of 0.54 mm/min, and nanofluid discharge rate of 12 lit/min would be the optimum values in ECM of HCHCr die tool steel. For checking the optimality obtained from the MOGA in MATLAB software, the maximum MRR of 375.78277 mm³/min and respective surface roughness Ra of 2.339779 μm were predicted at applied voltage of 17.688986 V, tool feed rate of 0.5399705 mm/min, and nanofluid discharge rate of 11.998816 lit/min. Confirmatory tests showed that the actual performance at the optimum conditions was 361.214 mm³/min and 2.41 μm; the deviation from the predicted performance is less than 4% which proves the composite desirability of the developed models.

1. Introduction

Advanced high hardness materials have a high importance especially for the applications such as automotive, metal forming, die making, and aerospace industries. ECM is more suitable process to have excellent and precise machining of these hard materials. It is a technical alternate in the field of manufacturing process to machine steels and superalloys due to avoidance of thermal stresses on their microstructures and absence of tool wear during the machining process [1, 2]. It is more appropriate one to machine a nonmachinable hard materials such as HCHCr die tool steel, AISI 202 Austenitic stainless steel, and superalloys [3, 4]. The parameters of the ECM influencing the objectives of MRR and surface roughness are applied voltage, tool feed rate, and electrolyte discharge rate [5–7]. The electrolyte flows through the interelectrode gap (IEG) and the machining reaction is very appreciable when the value of IEG is small [8, 9]. During the electrochemical machining, the formation of spikes, due to presence of passive layer formation, inconsistency of current density, and formation of gas at the IEG, prevent achievement of the better MRR and surface roughness. Hence this attempts to minimize the formation of spikes in the machined component by using copper nanoparticles suspended in NaNO₃ aqueous electrolyte solution. In order to find out an optimal condition, multiobjective genetic algorithm (MOGA) has been applied in this research work.

2. Experimental Setup

The experiments were conducted using ECM setup as shown in Figure 1. The selected workpiece material HCHCr die steel with hardness of 67 in HRc scale was one of the poor machinability materials [10]. The complete chemical composition of HCHCr die steel is presented in Table 1. The plain aqueous solution of 15% NaNO₃ and 40 g of Cu nanoparticles suspended in plain aqueous solution of 15% NaNO₃ were selected as electrolytes in these experiments [11]. The electrolyte solution was completely analyzed using deluxe water and soil analysis kit, Model-191E. A digital flow meter with two-digit accuracy was employed to adjust the flow rate of electrolyte to the IEG. Copper was chosen for fabrication of tool due to high electrical conductivity. In the present work, the IEG is set to be 0.5 mm initially throughout the experimentation [12]. Material removal (MR) is the difference in the weight of the workpiece before and after machining. The accuracy of measurement is ensured using Sartorius electronic weighing machine with three-digit accuracy. Mitutoyo surface tester with a range of 0–150 µm is used to measure surface roughness (Ra) and the average of values observed in three different surfaces on the workpiece is computed in each experiment. The process parameters used in the complete experiment are presented in Table 2.

Table 1: Chemical composition of HCHCr die tool steel.

Element
C
Cr
Mn
P
S
Fe
Si

Wt%
1.936
11.84
0.27
0.044
0.089
85.34
0.48

Table 2: Process parameters.

Applied voltage (V)
12, 15, and 18

Interelectrode gap (mm)
0.1

Tool feed rate (mm/min)
0.1, 0.21, 0.32, and 0.54

Electrolyte discharge rate (lit/min)
8, 10, and 12

Selected electrolyte
15% NaNO₃ aqueous solution
40 g copper nanoparticles suspended in 15% NaNO₃ aqueous solution

Tool-electrode condition
Stationary

Electrolyte temperature range (°C)
30°–40°

Workpiece material with its hardness
HCHCr die steel—67 HRc

Machining time (min)
3

Figure 1: ECM setup.

3. Mathematical Modeling of Machining Parameters

Design Expert 7.0 software is used to determine the relationship among the selected influencing parameters. Three levels have been selected for influencing parameters of the applied voltage, electrolyte discharge rate, and four levels selected for tool feed rate. It is possible to assess the main and interaction effects of different machining parameters in L₃₆ array with most reasonable accuracy. A first-order experiment was performed to determine the magnitudes of the relative changes to the process parameters that would result in optimum MRR and surface roughness. It is obtained from the first-order experiments; copper nanoparticles suspended in aqueous NaNO₃ electrolyte significantly improve the MRR and surface roughness compared to plain aqueous NaNO₃ electrolyte. Subsequently, a second-order central composite design was selected to identify the optimum conditions which turn into the higher MRR and finest surface roughness. The general form of second-order polynomial mathematical model applied to investigate the parametric effects of ECM is

where is the response and terms , , and so forth are the second-order regression coefficients. Various sets of parametric combinations results are obtained by conducting a series of experiments. The respective mathematical models representing MRR in view of plain aqueous NaNO₃ and Cu nanoparticles suspended in plain aqueous NaNO₃ electrolyte are computed as

where , , , , and represent MRR of plain aqueous NaNO₃, Cu nanoparticles suspended in plain aqueous NaNO₃, applied voltage, tool feed rate, and electrolyte discharge rate, respectively. The developed mathematical model will enable improvement of the performance of ECM while machining HCHCr die steel. The degree of fitness of the developed mathematical model is confirmed through ANOVA test. The coefficient of determination for MRR in terms of aqueous NaNO₃ and Cu nanoparticles suspended in aqueous NaNO₃ solutions were 90.97% and 93.45%, respectively, which confirms the accuracy of fitness of the mathematical model.

The respective mathematical models representing surface roughness in view of plain aqueous NaNO₃ and Cu nanoparticles suspended in plain aqueous NaNO₃ electrolytes are computed as

where , , , , and represent surface roughness of aqueous NaNO₃, Cu nanoparticles suspended in aqueous NaNO₃ electrolyte, applied voltage, tool feed rate, and electrolyte discharge rate, respectively. The coefficient of determination obtained from ANNOVA for surface roughness in terms of aqueous NaNO₃ and Cu nanoparticles suspended in aqueous NaNO₃ electrolytes were 92.55% and 96.98%, respectively, which confirms the fitness of the mathematical model.

4. Optimization Using Multiobjective Genetic Algorithm in MATLAB

Evolutionary algorithms seem mainly suitable to solve multiobjective optimization problems, because they deal simultaneously with a set of possible solutions (population). The traditional mathematical programming techniques need a series of separate runs to find the optimum solution for multiobjective problems. Contrarily, this method allows finding several members of the optimal set in a single run of the algorithm. In this research work multiobjective genetic algorithm toolbox from the MATLAB software is chosen for optimizing the selected objectives, maximizing the MRR and minimizing the surface roughness. The ability of GA to simultaneously search different regions of a solution space makes it possible to find a diverse set of solutions for difficult problems with nonconvex, discontinuous, and multimodal solutions spaces [13–15].

5. Analysis of the Influence of Parametric on the MRR and Surface Roughness for Aqueous NaNO₃ Electrolyte

The mathematical models developed using RSM and presented in (2) and (4) were used in GA toolbox as fitness functions. The limitation for the optimization is given as follows:

The GA generally includes three fundamental genetic operations of selection, namely, population, crossover, and mutation. These operations are used to modify the chosen solutions and select the most appropriate offspring to pass on to succeeding generations. The following parameters were considered during optimization using GA multiobjective tool. Population size = 225, crossover fraction = 0.8, mutation function = constraint dependent, crossover function = scattered, and number of iterations = 188.

Upon applying objective functions in GA tool, the results were obtained as tabulated in Table 3 and Figure 2.

Table 3: Process decision variables along with optimized response from GA for aqueous NaNO₃.

Sl. number
Voltage (V)
Feed rate (mm/min)
Discharge rate (lit/min)
MRR (mm³/min)
Surface roughness (micron)

1
12
0.1100281
8.134412
132.46309
1.513575

2
12.030211
0.1104329
9.087866
152.49089
1.777581

3
12.014045
0.1039534
10.04123
169.34532
2.152654

4
17.995146
0.5265484
11.98002
302.51291
2.656720

5
12.000245
0.1005786
9.147932
153.98288
1.791250

6
17.991590
0.3675932
11.97936
260.68191
2.254694

7
12.006746
0.2003393
8.234414
137.00311
1.560310

8
12.008757
0.1019808
10.06793
169.87964
2.159093

9
17.989152
0.3175749
11.99351
250.47074
2.189059

10
12.000737
0.1004096
9.719882
164.05383
2.038806

11
12.009474
0.2017167
8.488291
141.77756
1.587574

12
17.994398
0.5169848
11.97913
299.58104
2.623729

13
17.985153
0.2530979
11.99503
239.31519
2.164326

14
17.988464
0.3482414
11.99409
256.55458
2.219778

15
12.012065
0.1043002
8.710513
145.82497
1.564311

16
17.990082
0.4115904
11.98257
270.80421
2.330752

17
17.986068
0.3813624
11.99307
263.68107
2.268322

18
12.004311
0.1041213
8.848672
148.38304
1.639634

19
12.000057
0.2000266
8.109496
134.59464
1.771653

20
12.015943
0.1038107
8.938570
150.04776
1.691262

21
17.988003
0.4437773
11.99324
278.90557
2.397192

22
12.001201
0.1004778
8.995683
151.23304
1.714381

23
12
0.2
8.874121
132.46309
1.693575

24
17.995820
0.5399502
11.97976
306.69449
2.706127

25
12.007139
0.1076954
8.422892
140.26985
1.592297

26
17.985182
0.2532877
11.99497
239.34474
2.164350

27
12.013497
0.1039507
9.351800
157.47043
1.892639

Figure 2: Optimal parameters of aqueous NaNO₃ solution from GA.

The response plot shows the effects of applied voltage, tool feed rate, and electrolyte discharge rate on MRR and surface roughness of HCHCr die tool steel. MRR increases at higher voltage with the increase of tool feed rate and higher flow of electrolyte discharge rate at a mean time surface roughness slightly increased. A maximum MRR 306.69449 mm³/min was achieved under tool feed rate of 0.5399502 mm/min, 11.97976 lit/min of electrolyte discharge rate, and applied voltage of 17.995820 V. A minimum SR value of 1.513575 µm was observed at 12 V, 0.1100281 mm/min of tool feed rate, and 8.134412 lit/min of electrolyte discharge rate.

6. Analysis of the Influence of Parametric on the MRR and Surface Roughness for Cu Nanoparticles Suspended in Aqueous NaNO₃ Electrolyte

Table 4 and Figure 3 present the results from GA for Cu nanoparticles suspended in aqueous NaNO₃ electrolyte. MRR increases at higher values of electrolyte discharge rate and tool feed rate. The surface roughness decreases when the electrolyte discharge rate and tool feed rate are decreased. A maximum value of MRR 375.78277 mm³/min was obtained under 17.688986 V, 0.5399705 mm/min tool feed rate, and 11.998816 lit/min electrolyte discharge rate conditions. The minimum surface finish of 1.4973965 µm was observed at 17.999473 V, 0.2344207 mm/min tool feed rate, and 11.997052 lit/min electrolyte discharge rate condition.

Table 4: Process decision variables along with optimized response from GA for Cu nanoparticles suspended in aqueous NaNO₃ electrolyte.

Sl. number
Voltage (V)
Feed rate (mm/min)
Discharge rate (lit/min)
MRR (mm³/min)
Surface roughness (micron)

1
17.688986
0.5399705
11.998816
375.78277
2.339779

2
17.999473
0.2344207
11.997052
291.21779
1.4973965

3
17.982536
0.3619794
11.990806
324.01735
1.6773238

4
17.812326
0.5399910
11.998295
375.72198
2.3501116

5
17.974140
0.4719105
11.991869
354.97140
2.0744706

6
17.986820
0.3385383
11.997917
317.93316
1.6169858

7
17.995289
0.2727332
11.99707
300.7935
1.5171501

8
17.981729
0.4545162
11.997314
350.07699
1.9885303

9
17.970973
0.5100869
11.997924
366.45561
2.2630915

10
17.991024
0.3212817
11.997223
313.33371
1.5817197

11
17.957900
0.5030484
11.997998
364.38746
2.2357070

12
17.955889
0.4263774
11.997138
342.11877
1.8942504

13
17.896183
0.5389892
11.998794
375.34015
2.3713213

14
17.917751
0.5150918
11.998277
368.07683
2.3296118

15
17.960839
0.4768214
11.996795
356.60081
2.1042235

16
17.913224
0.5219700
11.998223
370.14799
2.3709887

17
17.963386
0.4965703
11.997834
362.44458
2.1984027

18
17.997099
0.2431022
11.997066
293.37153
1.5003086

19
17.991022
0.3212817
11.997223
313.33371
1.5817197

Figure 3: Optimal parameters of Cu nanoparticles suspended in aqueous NaNO₃ solution from multiobjective GA.

It is obvious that the optimum search can be obtained based on the developed second-order response, surface equations for correlating the various process variable effects with the MRR and surface roughness. The optimal combination of various process variables thus obtained for achieving controlled electrochemical machining of the workpieces is found to be within the bounds of the mathematical model.

7. Confirmation Test

The confirmatory experiments were further conducted for the optimal parameters obtained from the MATLAB multiobjective GA tool. The error between optimum values from GA and the confirmation test was derived by considering the serial number 24 and serial number 1 from Tables 3 and 4, respectively, at the condition of maximum MRR and is shown in Table 5.

Table 5: Error between optimum values from GA and confirmation test value for maximum MRR.

Sl. number
        Electrolyte
Obtained from GA
Confirmation test
Error

MRR
mm³/min
SR
μm
MRR
mm³/min
SR
μm
MRR
%
SR
%

1
Aqueous NaNO₃
306.69449
2.706127
294.012
2.82
4.13
4.25

2
Cu nanoparticles suspended in aqueous NaNO₃
375.78277
2.339779
361.214
2.41
3.87
3.31

8. Conclusions

This work employs a multiobjective genetic algorithm (GA) tool to optimize influencing parameters of ECM to maximize the MRR and minimize surface roughness of HCHCr die steel. Based on the experimental results, the following conclusions are drawn.

(1)Material removal rate increases linearly with applied voltage and nonlinearly increases with tool feed rate. Surface roughness decreases with increase in the applied voltage and all tool feed rates. Mathematical models for MRR and surface roughness have been developed by Design Expert 7.0 software. It is useful for analyzing the influence of the various process parameters for achieving better MRR and surface roughness of HCHCr die tool steel.(2)Genetic algorithm (GA) tool optimizes the range of influencing parameters in order to obtain a maximum MRR and minimum surface roughness. The experimental results reveal that applied voltage of 18 V, tool feed rate of 0.54 mm/min, and electrolyte discharge rate of 12 lit/min would be the optimum values in ECM of HCHCr die tool steel under copper nanoparticles suspended in aqueous NaNO₃ electrolyte solution machining condition.(3)For checking the optimality obtained from the multiobjective GA in MATLAB, MRR of 375.78277 mm³/min and surface roughness Ra of 2.339779 μm were predicted at applied voltage of 18 V, tool feed rate of 0.54 mm/min, and electrolyte discharge rate of 11.99 lit/min.(4)Confirmatory tests showed that the actual performance at the optimum conditions was 361.214 mm³/min and 2.41 μm; a deviation from the predicted performance is less than 4% at maximum material removal rate condition which has proven the composite desirability of the developed models for MRR and surface roughness under copper nanoparticles suspended in aqueous NaNO₃ electrolyte solution machining condition. Aqueous NaNO₃ electrolyte solutions performance is poor comparing to copper nanoparticles suspended in aqueous NaNO₃ electrolyte solution.(5)Comparing the predicted performance of aqueous NaNO₃ and copper nanoparticles suspended in aqueous NaNO₃ electrolyte solutions on experimentally and mathematically, copper nanoparticles suspended in aqueous NaNO₃ electrolyte solution performs better for MRR and surface roughness on HCHCr die tool steel.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

References

S. H. Ahn, S. H. Ryu, D. K. Choi, and C. N. Chu, “Electro-chemical micro drilling using ultra short pulses,” Precision Engineering, vol. 28, no. 2, pp. 129–134, 2004.

R. Goswami, V. Chaturvedi, and R. Chouhan, “Optimization of electrochemical machining process parameters using Taguchi approach,” International Journal of Engineering Science and Technology, vol. 5, no. 5, pp. 999–1006, 2013.

T. Sekar and R. Marappan, “Experimental investigations into the influencing parameters of electrochemical machining of AISI 202,” Journal of Advanced Manufacturing Systems, vol. 7, no. 2, pp. 337–343, 2008.

L. Tang and Y.-F. Guo, “Experimental study of special purpose stainless steel on electrochemical machining of electrolyte composition,” Materials and Manufacturing Processes, vol. 28, no. 4, pp. 457–462, 2013.

Z. Li and H. Ji, “Machining accuracy prediction of aero-engine blade in electrochemical machining based on BP neural network,” in Proceedings of the International Workshop on Information Security and Application, pp. 244–247, 2009.

M. Wang, W. Peng, C. Yao, and Q. Zhang, “Electrochemical machining of the spiral internal turbulator,” International Journal of Advanced Manufacturing Technology, vol. 49, no. 9–12, pp. 969–973, 2010.

A. Hasçalik and U. Çaydaş, “A comparative study of surface integrity of Ti-6Al-4V alloy machined by EDM and AECG,” Journal of Materials Processing Technology, vol. 190, no. 1–3, pp. 173–180, 2007.

Z. Brusilovski, “Adjustment and readjustment of electrochemical machines and control of the process parameters in machining shaped surfaces,” Journal of Materials Processing Technology, vol. 196, no. 1—3, pp. 311–320, 2008.

J. Kozak, M. Chuchro, A. Ruszaj, and K. Karbowski, “The Computer aided simulation of electrochemical process with universal spherical electrodes when machining sculptured surfaces,” Journal of Materials Processing Technology, vol. 107, no. 1–3, pp. 283–287, 2000.

K. G. Judal and V. Yadava, “Cylindrical electrochemical magnetic abrasive machining of AISI-304 stainless steel,” Materials and Manufacturing Processes, vol. 28, no. 4, pp. 449–456, 2013.

P. C. Tan and S. H. Yeo, “Investigation of recast layers generated by a powder-mixed dielectric micro electrical discharge machining processg,” Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, vol. 225, no. 7, pp. 1051–1062, 2011.

B. Bhattacharyya, M. Malapati, and J. Munda, “Experimental study on electrochemical micromachining,” Journal of Materials Processing Technology, vol. 169, no. 3, pp. 485–492, 2005.

D. F. Jones, S. K. Mirrazavi, and M. Tamiz, “Multi-objective meta-heuristics: an overview of the current state-of-the-art,” European Journal of Operational Research, vol. 137, no. 1, pp. 1–9, 2002.

K. Tang, J. Yang, H. Chen, and S. Gao, “Improved genetic algorithm for nonlinear programming problems,” Journal of Systems Engineering and Electronics, vol. 22, no. 3, pp. 540–546, 2011.

R. Mukherjee and S. Chakraborty, “Selection of the optimal electrochemical machining process parameters using biogeography-based optimization algorithm,” The International Journal of Advanced Manufacturing Technology, vol. 64, no. 5–8, pp. 781–791, 2013.