EXPERIMENTAL STUDY ON MICRO ELECTROCHEMICAL MACHINING OF SS 316L USING TEACHING LEARNING BASED OPTIM

International Journal of Mechanical and Production Engineering Research and Development (IJMPERD) ISSN (P): 2249–6890; ISSN (E): 2249–8001 Vol. 10, Issue 3, Jun 2020, 1051-1060 © TJPRC Pvt. Ltd.

EXPERIMENTAL STUDY ON MICRO ELECTROCHEMICAL MACHINING OF SS 316L USING TEACHING LEARNING BASED OPTIMIZATION SUDHARSAN GUNASEKARAN1*, SURESH PERIYAGOUNDER2, SIVALINGAM. A3 & SARAVANAN ANNAMALAI4 1,3

Assistant Professor, Department of Mechatronics Engineering, Sona College of Technology, Salem, Tamil Nadu, India 2

4

Professor, Sona College of Technology, Department of Mechanical Engineering, Salem, Tamil Nadu, India

Research scholar, Sona College of Technology, Department of Mechatronics Engineering, Salem, Tamil Nadu, India

ABSTRACT Electrochemical machining is a radical machine, which is widely used for machining various intrinsic shapes in alloys; optimal usage of ECM process parameters can lessen machine operation such as tooling, and maintenance cost.

Attempts are made to investigate the optimum values of Metal Removal Rate in micro-Electro chemical machining (micro-ECM) on Stainless Steel alloy voltage, Electrolyte concentration, Duty cycle and Temperature that are the input parameters for the experiment, while output responses are metal removal rate. Taguchi approach is used to evaluate the best possible criteria of the method and the number of experiments necessary to design the answers. The approach of the mathematical model from RSM is used to exercise function for single-objective optimization [7], using teachinglearning-based optimization. Results demonstrate that the best developed mathematical model varies less than 5% of the experiment.

KEYWORDS: Metal Removal Rate (MMR), Analysis of Variance (ANOVA), Response Surface Methodology

Original Article

surface response (RSM) is related to maximize the material removal rate (MRR) in the machining of SS316L. The

(RSM) & Teaching Learning-Based Optimization (TLBO)

Received: Apr 22, 2020; Accepted: May 12, 2020; Published: Jun 10, 2020; Paper Id.: IJMPERDJUN202091

1. INTRODUCTION Innovations and discoveries in metalworking have been vital; metalworking is the oldest method of shaping metal into various parts. The demand for products and production processes has been the driving factor behind developments in today’s cutting technologies. The improved properties for the steel as greater corrosion resistance, high-temperature strength and increased stiffness to serve specific purposes, have called for high importance on a suitable, effective, and efficient machining process. The miniaturization of products and product components in the submicron and nanometer level becoming a trend, while demands for micro and convoluted parts are increasing, micromachining is becoming an important precision engineering technique.

In the non-traditional machining process, Electrochemical machining (ECM) has gained tremendous versatility on its application. It is one of the promising, successful, and commercially utilizing machining operation in the recent industrial sectors. A different process based on the philosophical principle of electrochemical machining, which drives vital role in the manufacturing of various parts, complex and large metallic pieces. It has a major advantage over other machining operations such as zero tool wear, stress-free machining, bright surface finish, higher MRR in hard material [8]. During machining, the ion displacement takes places from anode

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(workpiece) on the application of cathode (Tool feed) by the application of electric charge. In which the hydroxide ion OH− are formed, tool forms the desired shape on the workpiece. The forced electrolyte circulation removes carries away the metal hydroxide formed during the machining, which is governed by faraday’s law of electrolysis which states that, The amount of chemical changes created by an electric current or the value of substance deposited or dissolved is proportional to the quantity of electricity transferred.

The quantity of various particles deposited or dissolved by an equivalent quantity of electricity are proportional to the chemical equivalent weights.

2. METHODOLOGY Changing parameters concerning other functions result in a variety of choices for a given situation. This function allows you to evaluate various choices to determine a better result. The main of applications is a minimal cost, optimal design, optimal management, increased profit and reduced error, variational principles for all kind of algorithms, especially evolutionary and particle swarm intelligence-based algorithms needs controlling parameters like population size and several generations. In addition to common control parameters, completely varying algorithms need their algorithm and specific control parameters. For illustration, Genetic algorithm practices the mutation probability, the crossover probability, the selection operator; PSO depression uses weight, social and psychological factors; Artificial Bee Colony algorithms use a wide variety of honeybees, used bees, scout bees and so on. Similarly, the other algorithms like ACO, BBO, FPA, ALO, etc. need the turning of respective algorithm-specific parameters. The exact setting of parameters is important which influences the performance of the algorithm.

The inopportune setting of parameters either improves the computational effort or yields the local the optimum results. The teaching-learning-based optimization algorithm does not require any algorithm-specific parameters [1]. The teaching-learning based optimization algorithmic program requires controlling parameters. Teaching-Learning Based Optimization Algorithmic Program Optimization has gained widespread recognition among researchers. This optimization uses a population-based algorithm that replicates the teaching and learning process of a specific region. Subsequently, this algorithm requires parameter number of groups and does not necessitate any specific control parameters. The operating of Teaching Learning Based Optimization can be categorized as ‘Teacher phase’ and second ‘Learner phase’.

2.1 Experimental Details The stainless steel (316L) material is considered as the workpiece in this paper, and it is used in medical and aquatic applications [10]. The substantial role in medical implants includes pelvic screws, screws and orthopedic implants (hip and knee replacement) due to its properties i.e., high antioxidant, corrosion resistance, and hardness. Most process parameters impact micro-ECM's machinability criteria, and this study addresses four main process parameters duty cycle, voltage, electrolyte concentration, and temperature with output parameter material removal rate [3] experimental setup pictured in figure 1. Initially the Taguchi method was used to estimate the number of tests needed for optimum process variables and sample response functions. RSM was effectively used to correlate the selected object's input process variables and output answers [5]. The RSM-derived mathematical model was then used as an exercise function for TLBO optimization. A schematic of the micro ECM machine as shown were the experiment was performed with a copper electrode and form tool of L shape and sodium chloride (NaCl) with glycerol in distilled water as an electrolyte for the experiment [6] & [9], the temperature of the electrolyte is maintained at a constant temperature using temperature feedback system and machining is Impact Factor (JCC): 9.6246

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done on stainless steel SS316L.

Figure 1: Micro-ECM step-up.

2.2 Power Supply System Power supply requirement for machining is done by pulsed DC supply. Due to the specified reason, AC power supply is converted to low voltage pulsed DC power supply by using pulsed rectifier. The voltage and current can be set digitally according to the need for the machining to be carried out. The pulse rectifier is shown in figure 2. Surge protector (protective fuse) is used to prevent overloading condition and short-circuit phenomenon. Electrodes change their polarities quickly; henceforward the reaction occurs in the first half of the cycle is reversed in the cycle.

Figure 2: Pulse Rectifier.

3. DESIGN OF EXPERIMENT Taguchiis modest, well organized and structured approach to process optimal parameters, which considerably cuts the number of tests required for the model [7] and [8]. This method is based on orthogonal array testing, provides a condensed variation of test as a result of the optimal set of process control parameters. The influences and related levels are measured in the listed Table 1. The selection of the orthogonal array is built on the number of parameters and showed in figure 3.

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Figure 3: Steps of Taguchi process.

Table 1: Input parameters and their Corresponding Levels [2] Input Parameters Symbols Levels 1 Levels 2 Levels 3 Voltage V 18 19 20 Electrolytic concentration E 30 40 50 Temperature T 35 40 45 Duty cycle D 33 37 41 In this paper, L9 orthogonal array taken given in Table 2 and the material removal rate has been calculated for each experiment. The formula to be calculated for material removal rate is given below: MRR = (Weight before machining - Weight after machining)/ (Machining time)

(1)

Table 2: Experimental values obtained from the Design of Experiments (L9) Duration Weight before Weight after MRR Exp. No V E T D (min) machining(gm) machining(gm) (mg/min) 1 18 30 35 33 24 15.973 15.677 12.3333 2 18 40 40 37 19 15.050 14.764 15.0526 3 18 50 45 41 17 14.462 14.168 17.2941 4 19 30 40 41 18 13.605 13.309 16.4444 5 19 40 45 33 20 15.337 15.050 14.3500 6 19 50 35 37 17 14.168 13.882 16.8235 7 20 30 45 37 19 15.637 15.337 15.7894 8 20 40 35 41 16 14.744 14.462 17.6250 9 20 50 40 33 16 13.882 13.605 17.3125 3.1 Response-Surface Methodology (RSM) The connection between the reaction and the autonomous variables is undiscovered in most RSM problems. In RSM, therefore, the first step is to find an appropriate estimate for actual relationship between the reaction and the process parameters[4]. The quantitative way in which, the desired response is related to independent input variables can be

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represented as y =f ( ) x x 1 2,, x x 3,..., n + ε,

(2)

Where y is response, f is response function x1, x2, x3, …, xn are the independent input variables, and ε is the fitting error. The response function's presence resembles a surface curve while scheming the obvious response. Identifying the appropriate estimate for f will decide if successful RSM application is achieved. Typically, the information necessary to build the response system is gathered from test design. The experimental data were fitted into two factors in the current research. y = β0 + β1x1 + β2x2 + β12x1x2 + β11x12 + β22x22 + ε

(3)

Where y is response, βi represents the linear effect of xi, βij, represents the quadratic effect of xi. They are crossproduct terms shows a linear by-linear interaction between xi and yi. ε is statistical error term. To obtain the regression models, Minitab software is used; MRR = -31.04-6.920 * v + 0.1364 * e + 2.959 * t + 1.907 *d + 0.2040 * v * v - 0.000275 * e *e - 0.03672 * t * t 0.02163 * d * d;

(4)

Where V = voltage, e = Electrolyte concentration, t = Temperature, d = Duty cycle (%)

4. OPTIMIZATION The working of the TLBO algorithm, get us metal removal rate. The objective function finds the value of a, b, c that increase the MRR function [1].

MRR = - 31.04 - 6.920 * v + 0.1364 * e + 2.959 * t + 1.907 * d + 0.2040 * v * v - 0.000275 * e * e - 0.03672 * t * t - 0.02163 * d * d

(5)

Subject to, 

18 < v < 20



30 < e < 50



35 < t < 45



33 < d < 41

It’s time to signify the TLBO algorithm, by assuming a population size of nine experiment values in table 2 (i.e., the total number of learners), four design variables v, e, t, d, and one iteration as termination criterion. The randomly generated initial population is within the ranges of variables and corresponding values of the objective function shown in Table 2.And mean values of v, e, t, and d are also displayed in Table 3. Due to this maximum function, maximum value of MRR is valued as best learner. So, the teacher is trying to improve the average end of class. Assuming random numbers are r1 = 0.3 for a and r2 = 0.45 for b, r3 = 0.4 for c and r4 = 0.5 for d and Tf = 1, difference mean values for a, b & c are determined

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Table 3: Initial Population E T 30 35 40 40 50 45 30 40 40 45 50 35 30 45

Exp. No 1 2 3 4 5 6 7

V 18 18 18 19 19 19 20

D 33 37 41 41 33 37 37

8

20

40

35

41

9 MEAN

20 19

50 40

40 40

33 37

MRR 12.3333 15.0526 17.2941 16.4444 14.3500 16.8235 15.7894 17.6250 (teacher) 17.3125

X''j,P,m = X'j,P,m + rm (X'j,P,m - X'j,Q,m), If X' total - P,m < X' total - Q,m

(6)

X''j,P,m = X'j,P,m + rm (X'j,Q,m - X'j,P,m), If X' total - Q,m < X' total - P,m

(7)

difference Mean (v) = 0.3* (20 - 19)

= 0.3

difference Mean (e) = 0.45 * (40 - 40)

=0

difference Mean (t) = 0.4 * (40 - 35)

=2

difference Mean (d) = 0.4 * (41 – 37)

=2

The value of the difference means (v) is added to the whole values under the column 'V'; and the value of the difference mean (e) is added to the whole values under the column 'E'; and the value of the difference mean (t) is added to the whole values under the column 'T'; and the difference mean value (d) is added to the whole values under the column 'D' of table 3; and table 4 displays the new voltage (v), electrolyte (e), temperature (t) and duty cycle (d) values; for the correct objective function values are shown. The initial population values of MRR are now shown in Table 3; and the extracted values are analyzed through the teachers 'process shown in Table 4, and the best MRR values ending the TLBO teachers' process.

Table 4: Fitness Comparison of Variables with Objective Function Exp. No V E T D MRR 1 18.3 30 37 35 13.9476 2 18.3 40 42 39 16.6352 3 18.3 50 45* 41 17.3980 4 19.3 30 42 41 16.5674 5 19.3 40 45* 35 15.4532 6 19.3 50 37 39 18.2115 7 20 30 45* 39 16.2753 8 20 40 37 41 18.5069 9 20 50 42 35 18.0447 The learner phase starts, and any one of the students can collaborate with any other student for transfer of their knowledge. The correlations are made randomly; the illustration and interactions between learners 1 and 6, 2 and 4, 3 and 7, 4 and 6, 5 and 7, 6 and 2, 7 and 6, 8 and 3, and 9 and 5 are measured. It should be noted that each learner must interact with any other learner. That is why nine relations were viewed in this illustration. Table 5 shows the new values of v, e, t and d for the learners after the interactions and considering the random numbers to be r1 = 0.2 for a, r2 = 0.45 for b, r3 =

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0.35 for c and r4 = 0.44 for d. In this case, the new values of v, e, t, d for learner 1 are calculated as explained below. As it is a maximization function, the value of MRR is better for learner 5 as compared to that of learner 1, and hence the knowledge transfer is from learner 5 to learner 1. Hence, the new values of v, e, t, d for learner 1 are calculated and MRR of Tables 5 and 6 are compared.

X''j,P,m = X'j,P,m + rm (X'j,P,m - X'j,Q,m), If X'total - Q,m < X'total - P,m

(7)

X''j,P,m = X'j,P,m + rm (X'j,Q,m - X'j,P,m), If X'total - P,m < X'total - Q,m

(8)

New for learner 1 = 18.3 + 0.2 (19.3-18.3)

= 18.5

New for learner 1 = 30 + 0.45 (50-30)

= 39

New for learner 1 = 37 + 0.35 (37-37)

= 37

New for learner 1 = 35 + 0.44 (39-35)

= 36.76

Table 5: Reframed values and their Objective Functions (learner phase) Exp. No V E T D MRR Interaction 1 18.5 39 37 36.76 15.7464 1&5 2 18.5 44.5 42 39.88 17.4386 2&4 3 18.64 50 45* 41* 17.6074 3&7 4 19.3 39 43.75 41* 17.2921 4&6 5 19.44 44.5 45* 36.76 16.7246 5&7 6 19.5 50 38.75 39 18.7211 6&2 7 20* 39 45* 39 17.3321 7&6 8 20* 44.5 39.8 41* 19.1145 8&3 9 20* 50 43.05 35 17.8724 9&5 For best values of f (x) are considered and positioned in Table 5. This ends the TLBO algorithm's learner phase iteration. It may be observed that, the maximum objective function value shown in the initial population obtained in table 1 is 17,625 and at the end of the first iteration, it was greatly increased to 19,1145. If increased the number of iterations, the known target function can be acquired in the next few. By observing maximized function value refers saturated value of the objective function.

5. RESULTS AND DISCUSSIONS The experiment is performed, and optimization is carried out of L9 orthogonal and the maximum MRR is achieved from TLBO is 19.1145 from table 5. The effects of voltage, electrolyte concentration, and duty cycle are plotted shows the significant increase in the MRR, as shown in figure 4 (a, b& d).

Interval Plot of mrr vs e

Interval Plot of mrr vs v

95% CI for the Mean

20

20

19

19

18

18

17

17

mrr

mrr

95% CI for the Mean

16

16

15

15

14

14

13

13 12

12 18

19

30

20

The pooled standard deviation is used to calculate the intervals.

(a) MRR vs. Voltage

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40

50

e

v

The pooled standard deviation is used to calculate the intervals.

(b) MRR vs. Electrolytic Concentration

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Sudharsan Gunasekaran, Suresh Periyagounder, Sivalingam. A, Saravanan Annamalai Interval Plot of mrr vs d

Interval Plot of mrr vs t

95% CI for the Mean

95% CI for the Mean

20

20

19

19

18

18 17

mrr

mrr

17 16

16 15

15

14 14

13

13

12 35

40

33

45

37

41

d

t

The pooled standard deviation is used to calculate the intervals.

The pooled standard deviation is used to calculate the intervals.

(c) MRR vs. Temperature (d) MRR vs. Duty Cycle Figure 4: Interval plot of MRR and various Parameters.

As the parameter temperature (t) increases, the MRR up to 40ᵒ C and then it decreases figure 2 (c), which supports the MRR is optimum at 19.1145.

6. CONCLUSIONS A new effort has been made to improve the interfering parameters in the micro ECM of stainless steel 316L, using 485 μm copper electrodes with L shape. It aims to get the best MRR possible. The voltage, electrolyte concentration, temperature, and duty cycle with three levels were the major processing parameters in micro-ECM on SS316L. The mathematical model was obtained from RSM, and the result of it was worn as a fitness function in TLBO. The results exposed the developed mathematical models which considerably improved the chosen objective (improved MRR). The use of temperature maintaining is also given significant results, and buffered electrolyte also gives a significant increase in the MRR. The use of the L-shaped tool provides lots of possibilities to machine the SS316L, and that can be used in many medical types of equipment and aeronautical types of equipment.

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