Applications of Taguchi technique with fuzzy logic to optimise an electrochemical micromachining...

Int. J. Experimental Design and Process Optimisation, Vol. 2, No. 4, 2011 283

Copyright © 2011 Inderscience Enterprises Ltd.

Applications of Taguchi technique with fuzzy logic to optimise an electrochemical micromachining process

R. Thanigaivelan* Department of Mechanical Engineering, Muthayammal Engineering College, Rasipuram-637408, India E-mail: [email protected] *Corresponding author

R.M. Arunachalam Department of Mechanical Engineering, Sona College of Technology, Salem-636005, India E-mail: [email protected]

J. Jerald and T. Niranjan Department of Production Engineering, National Institute of Technology, Tiruchirappalli-620 015, India E-mail: [email protected] E-mail: [email protected]

Abstract: This paper deals with the use of Taguchi technique with fuzzy logic to optimise electrochemical micromachining (EMM) process with multiple quality characteristics. The truncated cone tip electrode produced the highest machining rate and the conical with rounded tip electrode produced holes with lesser overcut. Hence, achieving higher machining rate with lesser overcut can be considered as a multi-objective optimisation problem. The machining parameters (the tool electrode tip shape, machining voltage, pulse on-time, electrolyte concentration) are optimised with considerations of the multiple performance characteristics (machining rate and overcut). The experiments were conducted in the developed EMM set-up. The experimental results reveal that the conical with rounded electrode, machining voltage of 9 V, pulse on-time of 10 ms and electrolyte concentration of 0.35 mole/l is the optimum combination for higher machining rate and lesser overcut. In addition in-depth studies have also been made to examine the influence of tool electrode tip on the overcut through SEM micrographs of machined micro-hole.

Keywords: fuzzy logics; Taguchi techniques; machining rate; overcut and stainless steel.

Reference to this paper should be made as follows: Thanigaivelan, R., Arunachalam, R.M., Jerald, J. and Niranjan, T. (2011) ‘Applications of Taguchi technique with fuzzy logic to optimise an electrochemical micromachining process’, Int. J. Experimental Design and Process Optimisation, Vol. 2, No. 4, pp.283–298.

284 R. Thanigaivelan et al.

Biographical notes: R. Thanigaivelan is an Assistant Professor of Mechanical Engineering at Muthayammal Engineering College. He holds an MTech in Mechatronics from Vellore Institute of Technology and currently pursuing his PhD in the area of micromachining from Anna University of Technology, Coimbatore. His research interests include micromachining, mechatronics and design of experiments.

R.M. Arunachalam is working as a Professor in the Department of Mechanical Engineering, Sona College of Technology, Salem, India. He completed his BE in Mechanical Engineering with distinction from Annamalai University and ME (by research) in Manufacturing Engineering from the National University of Singapore. He received his PhD in Manufacturing Engineering also from the National University of Singapore. He has contributed about 40 research papers in journals and conferences at international level. His areas of research include traditional and non-traditional machining, high speed machining, surface integrity of machined components, pulse plating and synthesis of nanostructured materials.

J. Jerald is an Assistant Professor in the Department of Production Engineering, National Institute of Technology, Trichirapalli, India. He has contributed about 50 research papers in journals and conferences at international level. His areas of research include micro/nano machining and optimisation.

T. Niranjan is a postgraduate student in the Department of Production Engineering, National Institute of Technology, Trichirapalli, India.

1 Introduction

The demand for micro components and products has witnessed a quantum jump in fields such as bio-technology, electronics, optics, bio-medicine and mechanical engineering (Jo et al., 2008). To meet this requirement, various micro-manufacturing methods have been developed such as lithography-based technique, non-traditional machining process, mechanical machining, and other micro fabrication techniques (Ryu, 2009). Electrochemical micromachining (EMM) appears to be a promising micromachining technology, due to its several advantages such as no tool wear, good surface finish, burr free, no mechanical force, and no heat affected layer on the machined surface.

In EMM the workpiece material is removed by anodic dissolution process in which the workpiece and the tool are the anode and the cathode, respectively, and are separated by an electrolyte under an electric field. According to faradays law, the anode workpiece dissolves locally and shape of the generated workpiece is approximately a negative mirror image of the tool. When EMM is applied for micro-hole drilling the size of the tool electrode used for machining is very small. Hence the flow channel cannot be provided for electrolyte in tool electrode and results in improper renewing of electrolyte. Thus, the electrolyte cannot dispel the reaction products from the machining area and resulting in short circuits. Some researchers have found several ways of improving the electrolyte flow in the inter electrode gap (IEG). Fan and Hourng (2010) have experimentally investigated the effect of rotational cathode tools on EMM. The rotational tool electrode produces micro-hole with high aspect ratio and lesser overcut. Wang et al. (2010) have used wedge-shaped electrodes to improve the machining accuracy of

Applications of Taguchi technique with fuzzy logic 285

inclined holes. Thanigaivelan and Arunachalam (2010) have experimentally studied the influence of different tool electrode tip shape on machining rate and overcut. It has been found that tool tip shape influences the circulation of the electrolyte in the IEG. Liu et al. (2010) used micro-electrode with various end shapes to improve the micro-hole accuracy and taper generation in EMM. Yang et al. (2009) have utilised a semi-cylindrical tool electrode with ultrasonic vibrations to produce micro-hole with higher accuracy and machining rate. Tsui et al. (2008) have studied the effect of micro-helical tool in electrochemical micro drilling to improve the machining accuracy and ability. The rotation of the helical tool creates a centrifugal action resulting in flushing out the reaction products and recharging with fresh electrolyte. The results showed that the accuracy of the micro-hole is improved using the micro-helical tool. Bhattacharyya et al. (2007) have studied the influence of tool vibration on machining performance and concluded that the micro-tool vibration improves the circulation of the electrolyte resulting in better material removal rate (MRR) and accuracy in EMM. Kurita et al. (2006) have implemented piezo-transducer to facilitate high speed movement of the electrode in the Z- axis direction. This high speed electrode moves up and down to flush out the contamination during the EMM process. Ebeid et al. (2004) have adopted a reciprocal motion between the tool and the workpiece to pump and increase the circulation of the electrolyte to improve ECM machining accuracy. They found that the amplitude of the tool vibration is the most effective parameter affecting ECM accuracy.

Based on these literatures, it is quite evident that although proper flushing of the IEG is essential for increasing machining rate and minimising overcut, not much research has been carried out to achieve proper flushing. Taking this issue into consideration, this research focuses on studying the effect of two electrode tip shapes and other parameters on the machining rate and overcut.

The optimal use of the EMM process for higher production rate, with better accuracy demands proper control of machining parameters through a suitable EMM set-up. Therefore, by controlling the various process parameters, optimal quality of the workpiece can be obtained. A fuzzy logic controller is the successful application of fuzzy set theory and has proven to be useful for dealing with uncertain and vague information (Zadeh, 1965). Tarng et al. (2000) used fuzzy-based Taguchi method to optimise the submerged arc welding process with multiple performance characteristics. Lin et al. (2000) have optimised the electric discharge machining (EDM) process based on the Taguchi method with fuzzy logics. Tzeng and Chen (2007) used fuzzy-based Taguchi method to optimise the high-speed electrical discharge machining process with multiple performance characteristics. Although, Taguchi-based fuzzy logic technique has been applied for process parameter optimisation. There is no systematic study reported to analyse the effects of tool tip shapes in EMM process using Taguchi method and fuzzy logic. Hence, analysis based on Taguchi method with fuzzy logic has been carried out to obtain significant process parameters and their optimal combination level for effective micro-hole generation in 304 stainless steel. The different process parameters levels have been identified and an L18 mixed orthogonal array (OA) is used in determining the optimal combinations of process parameters to maximise the machining rate and minimise overcut. Finally, in-depth studies have also been made to examine the effect of tool electrode tip on the overcut through SEM micrographs of machined micro-hole.


2 Taguchi method with fuzzy logic

The Taguchi method is a robust design method technique (Ross, 2005) which presents a simple way to design an efficient and cost effective experiment. In order to efficiently reduce the numbers of conventional experimental tasks, the OA by using design parameters (control factors) in column and standard quantities (levels) in row were proposed and further implemented. The experimental results are then transformed into a signal-to-noise (S/N) ratio. The S/N ratio can be used to measure the deviation of the performance characteristics from the desired values. Usually, there are three categories of performance characteristics in the analysis of the S/N ratio: the lower-the-better, the higher-the-better, and the nominal-the-better. Despite of the category of the performance characteristic, a larger S/N ratio corresponds to better performance characteristic. Hence, the optimal level of the process parameters is the level with the highest S/N ratio. Additionally, the analysis of variance (ANOVA) is performed to find out the factors which have a significant effect on the sensitivity of the process. Lastly, a confirmation experiment is conducted to verify the optimal process parameters obtained from the parameter design.

In manufacturing processes, Taguchi technique plays a major role in optimising performance characteristics. Taguchi technique is mostly designed to handle the optimisation of single performance characteristics but use of fuzzy logic helps to optimise a process with multiple performance characteristics (Elsayed and Chen, 1993). A fuzzy logic unit first fuzzifies the S/N ratio, then based on the fuzzy rule it generates the fuzzy value. The generated fuzzy value is converted into a Multi-Response Performance Index (MRPI) value. This MRPI value is used to find the process parameters that are statistically significant. The MATLAB R2008 software is used in the research.

3 Selection of control factors

The experimental factors were selected based on the above literature review includes factors possibly affecting the machining rate and overcut in general. Like in the reviewed literature, this study included pulse on-time, machining voltage and electrolyte concentration as a control factors. Tooling information such as tool tip shape and tool electrode diameter is also important, therefore, two tip shapes (truncated cone and conical with rounded tip shape) with same geometric specification were employed. In this way, the tool tip shape was treated as a control factor to study its impact on the machining rate and overcut.

4 Experimental set-up

The EMM set-up used for conducting the studies is shown in Figure 1. The set-up basically consists of various elements, such as mechanical machining unit, tool electrode feeding system, inter-electrode gap control system, electrolyte supply system and pulsed power supply system. The tool electrode feed mechanism, with resolution of 2 µm along Z-axis is designed with stepper motor and 8051 microcontroller. The set-up maintains the initial set gap (40 µm) constantly throughout the machining by providing appropriate control signals to the stepper motor with the help of microcontroller unit. Electrically


non-conductive perspex material is used for fabricating the work holding fixture and mounted in the machining chamber. The machining chamber is also made up of perspex because of its corrosion resistance, electrically non-conductive and transparent nature. The electrolyte supply and cleaning system consist of a pump and filter. A pulsed power supply with the capability for varying voltage, current and pulse width to the required values within the specified range is used.

Figure 1 EMM set-up

The 304 stainless steel of 200 µm thickness is used as a workpiece. The basic tool electrode is made up of stainless steel of Φ 452 µm and the tip shapes were shown in Figure 2. The side walls of the tool electrode are coated with a bonding liquid. The tool electrode is made as a cathode and workpiece is made as anode. The workpiece is placed in sodium nitrate (NaNo3) electrolyte. In the EMM process the preparation of brine solution plays an important role in MRR and accuracy. Brine solution is prepared by adding sodium nitrate with distilled water in a beaker. Then it is mixed properly with the help of magnetic stirrer.

Table 1 shows the machining parameters and their levels used for the experiments. The experiments were conducted at 50 Hz frequency and a machining current of 0.8 A.

The machining performance is evaluated in terms of machining rate and overcut. Machining rate is defined as amount of material removed (in thickness direction) per unit machining time. Overcut (radial overcut) of the machined micro-hole has been considered as machining accuracy criteria. It is the difference between the radius of the machined hole and the radius of the tool electrode. The micro-hole diameter is measured


with the help of an optical microscope. Machining time is noted for each experiment. In EMM, higher machining rate and lower overcut are desired.

Figure 2 Tool electrode tip shape, (a) truncated cone tip (b) conical with rounded tip

Table 1 Machining parameters and their levels

Symbol Control factors Level 1 Level 2 Level 3

A Tool electrode tip shape Conical with rounded tip Truncated cone tip B Machining voltage (volts) 8 9 10 C Pulse on-time (millisecond) 7.5 10 15 D Electrolyte concentration (mole/l) 0.23 0.29 0.35


4.1 OA selection

The OA is selected based on the degrees of freedom (DOF) (Ross, 2005). In this study, an L18 mixed OA is used. This array has 4 columns and 18 rows and it can handle one, two-level process parameter and three, three-level process parameters. The experimental combinations of the machining parameters using the L18 OA are presented in Table 2. Each of the experiments has been conducted twice. Machining rate and overcut are the two responses considered in this study. In EMM, higher machining rate and lower overcut are desired. Therefore, the machining rate is the higher-the-better performance characteristic and the overcut is the lower-the-better performance characteristics. Table 2 L18 OA

Machining rate (µm/sec) Overcut (µm) Exp.

no. A B C D Expt. 1 Expt. 2

S/N ratio in dB

Expt. 1 Expt. 2

S/N ratio in dB MRPI

1 1 1 1 1 0.083 0.09 –21.281 103 94 –39.877 0.167 2 1 1 2 2 0.11 0.138 –18.298 54 62 –35.289 0.293 3 1 1 3 3 0.166 0.158 –15.817 24 35 –29.544 0.484 4 1 2 1 1 0.111 0.104 –19.385 17 12 –23.354 0.5 5 1 2 2 2 0.133 0.158 –16.839 77 88 –38.348 0.659 6 1 2 3 3 0.151 0.138 –16.829 22 28 –28.02 0.505 7 1 3 1 2 0.133 0.123 –17.875 84 88 –38.692 0.663 8 1 3 2 3 0.151 0.166 –16.028 14 24 –25.865 0.685 9 1 3 3 1 0.158 0.175 –15.605 18 14 –24.149 0.5 10 2 1 1 3 0.222 0.238 –12.781 52 44 –33.654 0.278 11 2 1 2 1 0.166 0.185 –15.152 97 114 –40.493 0.1 12 2 1 3 2 0.555 0.476 –5.831 130 126 –42.145 0.456 13 2 2 1 2 0.133 0.144 –17.191 16 34 –28.488 0.473 14 2 2 2 3 0.185 0.151 –15.627 15 12 –22.659 0.684 15 2 2 3 1 0.151 0.133 –17.006 149 140 –43.201 0.12 16 2 3 1 3 0.158 0.138 –16.654 36 34 –30.884 0.38 17 2 3 2 1 0.133 0.123 –17.875 139 129 –42.548 0.167 18 2 3 3 2 0.37 0.416 –8.156 142 148 –43.229 0.274

5 Results and discussion

5.1 Analysis of s/n ratio

In the Taguchi technique, the S/N ratio is used to determine the deviation of the performance characteristic from the desired value. To obtain optimal machining performance, the maximum machining rate and minimum overcut are desired. Therefore, the higher-the-better machining rate and lower-the-better overcut should be selected. The loss function is further transformed into an S/N ratio.

S/N ratio for larger-the-better performance characteristic can be expressed as:


210log 1/

n

ii

n Yη−⎡ ⎤

= − ⎢ ⎥⎢ ⎥⎣ ⎦∑ (1)

S/N ratio for smaller-the-better performance characteristic can be expressed as:

210log 1/

n

ii

n Yη⎡ ⎤

= − ⎢ ⎥⎢ ⎥⎣ ⎦∑ (2)

where η: S/N ratio, Yi: the ith result of the experiment; n: the repeated number of the ith experiment. The experimental results of the machining rate, overcut and S/N ratio are presented in Table 2. The S/N ratios are estimated based on equation (1) and equation (2) (Sundaram, 2008).

The engineering units for relating the machining rate and the overcut are different. These units are transformed into S/N ratios corresponding to the machining rate and overcut is processed by the fuzzy logic unit, which converts the two performance characteristics (two responses) into MRPI (single response).

5.2 Fuzzy logics unit

A fuzzy logic unit consists of a fuzzifier, membership functions (MFs), a fuzzy rule base, an inference engine, and a defuzzfier. In the system of fuzzy reasoning, the fuzzifier primarily uses MFs to fuzzify the S/N ratios. A MF is a curve to determine how each input value is mapped to a membership value (or degree of membership) between 0 and 1. Next, the inference engine performs a fuzzy interface so as to generate a fuzzy value according to the MF and fuzzy rules. Finally, the defuzzifier converts the fuzzy value into a non-fuzzy value called MRPI. In this study, the MF adopts the trapezoidal MF that has a flat top and a truncated triangle curve. The class of MF is restricted to trapezoidal, as it is general enough and widely used. In fuzzy logic, if–then rule statements are used to formulate the conditional statements. In the following, the concept of fuzzy reasoning is described briefly based on the two-input-one-output fuzzy logic unit, where x1 and x2 are the two inputs and y is the output.

Figure 3 MFs for machining rate


Figure 4 MFs for overcut

Figure 5 MFs for MRPI

Table 3 Fuzzy rule table

Machining rate MRPI

Small Middle Large Small Very small Small Middle

Middle Small Middle Large Overcut

Large Middle Large Very large

Under Rule i where i = 1, 2, …, n), y is Ci if x1 is Ai and x2 is Bi. Ai, Bi, and Ci are fuzzy subsets defined by the corresponding LFs, i.e., μAi, μBi and μCi.

In this work, three fuzzy subsets (small, middle, large) are assigned in the two inputs (Figure 3 and Figure 4). Five fuzzy subsets (very small, small, middle, large, and very large) are assigned in the output as shown in Figure 5.

Nine fuzzy rules (Table 3) are derived directly based on the information that larger is the S/N ratio, the better is the performance characteristic. By taking the max – min compositional operation, the fuzzy reasoning of these rules fetches a fuzzy output. Supposing that x1 and x2 are the two input values of the fuzzy logic unit, the MF of the output of fuzzy reasoning can be expressed as


( ) ( )( ) ( ) ( )( )0 1 1 1 2 1 1 2( ) ( ) V.. ( )A B C An Bn CnC y x x y x x yμ μ μ μ μ μ μ= Λ Λ Λ Λ (3)

where Λ is the minimum operation and, V is the maximum operation.

( )( )

0

0

0C

C

y yy

y

μ

μ= ∑∑

(4)

A defuzzification method, called the centroid method, is taken on here to transform the fuzzy inference output into a non-fuzzy value, called MRPI using equation (4). Based on the above discussion, the larger is the MRPI, the better is the performance characteristic. The MRPI value of the corresponding machining parameters is shown in Table 2. Table 4 MRPI table

MRPI Symbol FACTORS

Level 1 Level 2 Level 3 Maximum A Tool electrode tip shape 0.49511 0.32578 0.49511 B Machining voltage 0.29633 0.49017 0.44567 0.49017 C Pulse-on-time 0.41017 0.43133 0.38938 0.43133 D Electrolyte concentration 0.259 0.46967 0.50267 0.50267

Total mean of MRPI = 0.41048 dB

Figure 6 MRPI graph

The effect of each machining parameters at different levels is calculated and summarised in the MRPI table (Table 4). Based on Table 4, an optimal combination of process parameter for higher machining rate and lesser overcut is conical with rounded tip shape (level 1), machining voltage of 9V (level 2), pulse on-time of 10 ms (level 2) and electrolyte concentration of 0.35 mole/l (level 3). The total mean of all 11 values are


calculated and presented in Table 4. Figure 6 shows the MRPI graph and the total mean of the MRPI are indicated in dashed line.

Larger values of the MRPI in the graph (or in the MRPI table) indicate that the smaller is the variance of the performance characteristics around the desired value. Moreover, the relative importance between the machining parameters for the multiple performance characteristics still needs to be known so that the optimal combinations of the machining parameter levels can be determined more exactly.

5.3 Analysis of variance

The MRPI values, determined from the experimental values are statistically studied by ANOVA to investigate the effects of each machining parameter on the observed values and to clarify which machining parameters significantly affects the observed values. In addition, the F-test named after fisher can also be used to determine which process parameters have a significant effect on the performance characteristic (Montgomery, 1997). Usually, the change of the process parameter has a significant effect on the performance characteristic when the F-value is large. Table 5 shows the calculated F-values for machining rate and overcut respectively to determine the relative significances of different control factors. The results of the F-test clearly indicate that the tool electrode tip shape and the electrolyte concentration have statistically significant effect on machining rate and overcut at the 95% confidence level. This could be attributed to the fact that the shape of tool electrode provides proper supply of electrolyte solution to the workpiece surfaces during EMM. According to faradays law of electrolysis, the metal removal rate is depending on current density distributed on the workpiece surface during EMM. The current density distribution under the bottom and along the sidewall of the tool electrode is based on the tool electrode tip shape and electrolyte conductivity (Bhattacharyya et al. 2004). Therefore, the tool electrode tip shape and electrolyte concentration found to be the significant factor that influences the machining rate and overcut. Table 5 Results of ANOVA

Symbol Factor Sum of squares

Degrees of freedom

Mean square (variance)

F0 value F0.05

Contribution (%)

A Tool electrode tip shape

0.129032 1 0.129032 6.770 4.96 19.4824

B Machining voltage

0.127809 2 0.063904 3.353 4.10 19.2979

C Pulse on-time 0.005167 2 0.002583 0.136 4.10 0.78023 D Electrolyte

concentration 0.209685 2 0.104842 5.500 4.10 31.6602

E Error 0.190605 10 0.019060 28.7793 T Total 0.662300 17 100.00

Percentage contribution indicates the relative power of a factor to reduce variation. For a factor with a higher percent contribution, a small variation will have a great influence on the performance. The percent contribution of the machining parameters on the machining rate and overcut shown in Table 5, reveals that electrolyte concentration and pulse on-time have the maximum and minimum influence on machining rate and overcut.


5.4 Validation

After identifying the optimal level of the process parameters, the last step is to predict and verify the improvement of the performance characteristic using the optimal level of the process parameters. The purpose of the confirmation test is to validate the conclusions drawn during the analysis phase. The predicted or estimated MRPI value using the optimal level of the process parameters can be calculated as

( )1

q

m i mi

η η η η=

= + −∑ (5)

where ηm is the total mean of the MRPI, q is the number of significant parameters, ηi is the mean of the MRPI at the optimal level.

The MRPI was predicted based on the equation (5) and new experiment has been designed and conducted with the optimal levels of the machining parameters to verify the improvement of the multiple performance characteristics. Table 6 shows the confirmation test table using optimal machining parameters. This table evaluates the predicted and actual MRPI value. As shown in Table 6, MRPI is increased from 0.167 to 0.709. It is shown clearly that MRPI is greatly improved. So the parameter combination suggested for the EMM process is tool electrode tip shape at level 1, machining voltage at level 2, pulse on-time at level 2, and electrolyte concentration at level 3. The comparison of the S/N ratios between the initial machining parameters and the optimal machining parameters is presented in Table 7. It is shown clearly that the machining rate and the overcut are significantly improved through this study.

Table 6 Confirmation test table

Optimal combination levels of machining parameters

Initial levels of

machining parameters Prediction Experiment

Level A1B1C1D1 A1B2C2D3 A1B2C2D3

Observed machining rate value 0.0865 0.4435

Observed overcut value 98.5 17.5

MRPI 0.167 0.68784 0.709

Improved MRPI is 0.542

Table 7 S/N ratio for the initial machining parameters and the optimal machining parameters

Machining rate Overcut

Initial levels of machining parameters (A1B1C1D1)

–21.281 –39.877

Optimal combination levels of machining parameters (A1B2C2D3)

–10.42 –24.95

Improvement S/N ratio (dB) –10.861 –14.927


Figure 7 SEM of the machined hole at different machining conditions as (a) Truncated cone tip shape/10ms/9V/0.35 mole/l/50Hz/0.8 A (b) Conical with rounded tip shape /10ms/9V/0.35 mole/l/50Hz/0.8 A

6 Analysis based on SEM micrographs

The effect of EMM parameters on micro-hole accuracy and surface quality can be clearly observed from the SEM micrographs of micro-hole machined under different parametric combinations. The analysis of the micro holes, have been carried out at the optimal


parametric combination for the two tip shapes. (Figure 7) shows the micrographs of machined micro-holes under parameter combinations:

a truncated cone tip shape/10ms/9V/0.35 mole/l/50Hz/0.8 A

b conical with rounded tip shape cone tip shape/10ms/9V/0.35 mole/l/50Hz/0.8 A.

The SEM micrographs show that the conical with rounded electrode obtain the lesser overcut than the truncated cone tip shape. This is because the conical with rounded tip shape allows sufficient flow of electrolyte towards the machining area and prevents the accumulation of machined debris. The proper flushing of machined debris eliminates the undesirable micro sparks. This reduction of micro sparks reduces the more material removal from the larger area of workpiece and thus results in lesser overcut. In the case of truncated cone tip shape the bottom surface area of the tool tip is more when compared with the conical tip shape. This larger surface area increases the stray current in the machining zone and results in higher overcut. Moreover the occurrence of micro spark due to presences of sharp corner also attributes to higher overcut.

Figure 8 represents the micro-hole machined at parametric combination of conical with rounded tip shape/10ms/9V/0.41 mole/l /50Hz/0.8A. Figure 7(b) and Figure 8 is compared to analyse the effect of electrolyte concentration on overcut. The figures clearly show that the overcut found to increase with increase in electrolyte concentration. For higher electrolyte concentration, the ions associated with the machining process in the machining area increases. This higher concentration of ions results in higher machining rate and hence the overcut is higher.

Figure 8 SEM of the machined hole using conical with rounded tip shape/10ms/9V/0.41 mole/l/50Hz/0.8 A


7 Conclusions

Experiments were conducted for optimising the tool tip shape and process parameters in EMM of 304 Stainless Steel by using Taguchi technique and fuzzy logics. Based on the conducted experiments, the following conclusions are made:

1 The optimal tool tip shape and parametric combination for higher machining rate and lesser overcut were found to be conical with rounded tip, machining voltage at 9 V, pulse on-time at 10 ms and electrolyte concentration at 0.35 mole/l.

2 ANOVA and F-test experimental data related to EMM essential machining parameters revealed that tool electrode tip shape and electrolyte concentration significantly affects the machining rate and overcut.

3 A confirmation test result proves that there is a remarkable improvement in the MRPI value from 0.167 to 0.709, when machining is done with the optimal parametric combination.

4 Based on the SEM micrographs, it is seen that the conical with rounded tip shape is found to produce lesser overcut when compared with the truncated tip shape. Also, it is seen that at lower electrolyte concentration the overcut is less when compared with higher electrolyte concentration.

Acknowledgements

The 2nd author acknowledges the financial assistance provided by the All India Council for Technical Education (AICTE), New Delhi under the CAYT File No 1-51/FD/CA/18/2007-08 dated 31/03/2008. The authors also thank the management of Sona College of Technology, Salem and Muthayammal Engineering College, Rasipuram, Tamil Nadu, for the encouragement and support. The authors also thank HOD, Department of Metallurgy, National Institute of Technology, Tiruchirappalli for allowing to use their scanning electron microscope.

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Applications of Taguchi technique with fuzzy logic to optimise an electrochemical micromachining...

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