EXPERIMENTAL STUDY AND MULTI- OBJECTIVE OPTIMIZATION OF INDEPENDENT VARIABLES FOR AISI 304 STAINLESS STEEL

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1 International Journal of Mechanical Engineering and Technology (IJMET) Volume 9, Issue 9, September 2018, pp , Article ID: IJMET_09_09_025 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed EXPERIMENTAL STUDY AND MULTI- OBJECTIVE OPTIMIZATION OF INDEPENDENT VARIABLES FOR AISI 304 STAINLESS STEEL Arunbharathi Ramaswamy* *Corresponding author, Assistant Professor, Department of Mechanical Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, Tamilnadu , India Ashoka Varthanan P Professor, Department of Mechanical Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, Tamilnadu , India Abinash Raju R, Abdul Haathi M and Aravind Kumar G.B UG Student, Department of Mechanical Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, Tamilnadu , India ABSTRACT In this work, an attempt has been made to optimize the process variables such as current, gap distance, pulse on time, pulse off time and voltage to obtain the desired performance characteristics like minimum tool wear rate (TWR), over cut (OC) and maximum material removal rate (MRR) in hole drilling EDM process. In order to achieve this, experimental work has been carried out on AISI 304 stainless steel using copper as tool electrode by implementing full factorial central composite design (CCD) based Response Surface Methodology (RSM). The measured response values viz., TWR, MRR and OC were also analyzed using MINITAB software to determine the significant parameters and adequacy of the proposed model with the aid of analysis of variance method. Mathematical model has been developed for the prediction of MRR, TWR and OC as a function of interaction and higher order terms of current, gap distance, pulse on time/off time and voltage. The optimal combination of independent parameters was obtained using desirability function approach through Response optimizer plot. Keywords: Hole Drilling EDM, RSM, AISI 304 SS editor@iaeme.com

2 Experimental Study and Multi-Objective Optimization of Independent Variables for Aisi 304 Stainless Steel Cite this Article: Arunbharathi Ramaswamy, Ashoka Varthanan P, Abinash Raju R, Abdul Haathi M and Aravind Kumar G.B, Experimental Study and Multi-Objective Optimization of Independent Variables for Aisi 304 Stainless Steel, International Journal of Mechanical Engineering and Technology, 9(9), 2018, pp INTRODUCTION Nowadays small hole drilling Electrical Discharge Machining (EDM) has developed as important manufacturing techniques for drilling fine holes precisely at a faster rate. In the thermal erosion process, the interaction between tool and work material has been eliminated. Generally, EDM drilling has been used for huge production work. In any electrical conductive materials, holes are drilled irrespective of its hardness. The process of EDM drilling has recognized in the manufacturing of metal pieces with complicated geometries [1-4]. It has some definite applications like fuel injectors, hardened punch for inserting ejectors, drilling holes in turbine blades, drilling starter holes in wire EDM, cutting a hole for circulating coolant etc. Among different EDM parameters particularly voltage (V), peak current (IP), pulse on time (T ON ), gap distance (GAP) and pulse off time (T OFF ) have important influences on the holes produced. Most of the researchers have studied that the effect of these parameters on TWR, MRR, surface finish, and OC and also have assumed the voltage, peak current and the gap distance between work piece as static and tool, i.e. deterministic [5-7]. Simon et al. [8] studied the effects of electrode revolution speed on MRR, current, voltage, duty factor. These researches were deliberated about performing rapid hole drilling EDM and the optimum parameter combinations. The electrodes were made of copper rod and holes were drilled on the work piece. Kiyak et al. [9] examined the stimulation of parameters in Electrical Discharge Machine on the surface roughness for the process of machining tool steel. The taken EDM parameters were pulse time, pulse pause time and pulse current. The authors reported that surface roughness of work piece and electrodes were directly proportional to pulse time and pulsed current. Higher estimated values of these parameters increases surface roughness of the work piece. Lower pulse time, relatively higher pulse pause time and lower current that produces a good surface finish on the work piece. Pradhan et al. [10] evaluated the surface roughness for steel work piece with AISI D2 tool and he has taken electrode made of copper. The required input parameters are pulse duration, pulse off time, applied voltage and discharge current. From the process parameters which have significant effects on the surface roughness are found to be pulse duration, discharge current and pulse off time. Wang et al. [11] inspected about micro-hole machining on the Polycrystalline diamond by means of the micro-edm process. A sequence of experiments was conducted to study the polarities for the influences of micro-edm parameters and effective machining on the machining performance. The outcomes of Experiments found that negative polarity machining is meant for micro-edm process on PCDs due to the adhesion of eroded materials brought over the guarded electrode. The tool electrode has a suitable volume of adhesion on which increases MRR and reduces the relative TWR. To study the EDM process parameters such as tool-wear rate, over cut material removal rate calculated by Taguchi methodology. Azad [12] had preferred design factors as current, frequency, width and voltage. The experimental results concluded that the optimization of most important parameters such as current and voltage for single quality characteristic are not suitable for the multiple quality responses. Experimentally investigated the performance obtained from the micro EDM deep editor@iaeme.com

3 Arunbharathi Ramaswamy, Ashoka Varthanan P, Abinash Raju R, Abdul Haathi M and Aravind Kumar G.B drilling with electrically and standard insulated tools. Brass electrodes are used for EDM drilling of 2mm diameter on Inconel 718.The response surface methodology is used to develop mathematical model which gives reliable forecasted experimental results within acceptable range. Prasad and Krishna [13] made on attempt to study the machining of complex profiles. Choosing the right combination of input parameters will decide the performance of any machining process. The most significant output parameters are SR and MRR which influences the performances of machining process. The response surface methodology is used for modeling SR and MRR and optimization developed mathematical models are used. Jahan et al. [14] examined the major parameters that influence the performances of Electrical Discharge Response. It is very difficult to achieve the optimal performance as there are many process parameters that determined the output characteristics. The work piece used is AISI 304 and rotating copper electrode used to drill a hole on it. The optimal combination of process parameters T ON, T OFF, IP, voltage and gap distance are found for better MRR, OC and TWR in this paper. In the majority of the past examinations, just single objective has been investigated. In this work an endeavor has been made to consider and enhance multiple quality characteristics. 2. MATERIALS AND METHODS The EDM DRILLING machine, SD 350 ZNC fabricated by Oscar EDM Ltd has been utilized to do the trials. The target of this exploration is to get minimum value of OC, TWR and maximum value of MRR. the machining zone has shown in the figure 1. The extent of the work piece considered as 250mm length, 10.5mm thickness and 100mm width. In this investigation, ø of 3mm hole has been drilled in all the experiments utilizing copper cathode. Distilled water was utilized as dielectric liquid. The EDM experiment parameters and conditions are described in the table 1 Figure 1 Photograph of Machining The elements present in AISI 304 Stainless Steel work piece material is as per the following: Fe: %, Ni: 8 to 10%, Cr: 17 to 19%, Mn: Max 2%, Si: Max 1%. Table 1 Experimental Conditions and Parameters Parameters Designation Description Work piece material - AISI 304 stainless steel Electrode material - Copper Electrode diameter - 3 mm Pulse current A 3-7 A Pulse on time B μs Pulse off time C 9-12 μs Voltage D 2-4 volt Gap Distance (E) E 2-4 mm editor@iaeme.com

4 Experimental Study and Multi-Objective Optimization of Independent Variables for Aisi 304 Stainless Steel Prior to the experimentation, top surface of the work piece was leveled utilizing surface granulating machine. During initial and last phase of the machining procedure, weight of the work piece has been estimated using electronic weight adjust. Refer to (1-3) to compute the tool wear rate, material removal rate and over cut for the hole with the assistance of tool maker microscope and it is showed up in figure 2. Figure 2 Tool Makers Microscope ( (1) (2) The combination of parameters with constrained numbers can be framed by utilizing Design of Experiment. In this examination, full factorial central composite design based RSM has been utilized to design the experiments. Table 2 exhibits the process parameters considered for designing the experiments. Five levels were taken for pulse current (factor A), pulse on time (factor B), pulse off time (factor C), voltage (factor D) and gap distance (E). The factors were chosen by the guide of primer tests and furthermore the handbook prescribed by the machine producers. Table 2 Machining Parameters and Levels. Factors Symbol Unit Levels A IP A B T ON Μs C T OFF Μs D V Volt E GAP Mm runs/tests were recommended by CCD based RSM method and it is shown in table 3. Tests were conducted according to the design combination parameters and the responses tool wear rate, material removal rate and over cut were calculated. Final hole drilled components are shown in figure 3(a) & 3(b). (3) editor@iaeme.com

5 Arunbharathi Ramaswamy, Ashoka Varthanan P, Abinash Raju R, Abdul Haathi M and Aravind Kumar G.B Table 3 Experimental Results Run A B C D E MRR g/min TWR (%) OC mm editor@iaeme.com

6 Experimental Study and Multi-Objective Optimization of Independent Variables for Aisi 304 Stainless Steel Figure 3 a Final Hole Drilled Component (Run 1-27) Figure 3 b Final Hole Drilled Component (Run 28-54) 3. MATHEMATICAL MODELS Response surface methodology (RSM) model is utilized for collecting of mathematical and statistical techniques that are useful for improving, developing and optimizing the process. RSM additionally has basic applications in the development, design, and specifying of new items and further in the difference in existing item design. In this work, second order regression model is generated for forecasting TWR, OC and MRR as far as interactive and higher order machining parameters through RSM methodology using experimental data and it is appeared in (6-8). Generally, the surface response can be rewritten as 4 The term Y is the corresponding response e.g. TWR, MRR and OC are created by the various process parameters of hole drilling Electrical Discharge Machine; xi (1, 2,, n) speaks to the coded levels of n quantitative process parameters. Where the term β0, βi, βii, and βij are the second order regression co-efficient. Under this summation the second term represents that the condition is polynomial due to the linear effects, however the third term relates to the higher order effects, the fourth term corresponds to the interactive effects of process parameters. Applying the least square technique, using the observations collected (Y1, Y2,..Yn ) during design points (n), the values of these co-efficient can be determined. This equation can also be rewritten based on the five variables in the coded form (5). Y u = b o + b 1 X 1 + b 2 X 2 + b 3 X 3 + b 4 X 4 + b 5 X 5 + b 11 X 1 2 +b 22 X b 33 X b 44 X b 55 X b 12 X 1 X 2 + b 13 X 1 X 3 + b 14 X 1 X 4 + b 15 X 1 X 5 + b 23 X 2 X 3 + b 24 X 2 X 4 + b 34 X 3 X 4 + b 35 X 3 X 5 + b 45 X 4 X 5 (5) MRR = A B C D E A*A B*B C*C E-04 A*B A*C A*D A*E B*C B*D B*E C*D C*E D*E (6) TWR = A B C D E A*A B*B C*C A*B editor@iaeme.com

7 Arunbharathi Ramaswamy, Ashoka Varthanan P, Abinash Raju R, Abdul Haathi M and Aravind Kumar G.B A*C A*D A*E B*C B*D B*E C*D C*E D*E (7) OC = A B C D E A*A B*B C*C A*B A*C A*D A*E B*C B*D B*E C*D C*E D*E (8) 4. RESULTS AND DISCUSSIONS The Minitab Statistical package was utilized to examine the response parameters and experimental data. To check the capability of the recommended models and the significance of individual parameters at 95% assurance level that can be Analysis of variance has been performed. The regression coefficients for MRR, OC, TWR and the corresponding P values shown as it are in the table (4-6). When the value of P less than 0.005, then the factor is called as significant factor. When the P is 0, it is known as the most significant factor. Table 4 Estimated Regression Coefficients for MRR Term coef SE coef T P CONSTANT IP TON TOFF V GAP IP * IP TON*TON TOFF*TOFF IP *GAP TON*GAP V*GAP S = R Sq = R Sq(adj) = Table 5 Estimated Regression Coefficients for TWR Term coef SE coef T P CONSTANT IP TON TOFF V GAP IP * IP TON*TON TOFF*TOFF IP *GAP TON*GAP V*GAP S = R Sq = R Sq (adj) = editor@iaeme.com

8 Experimental Study and Multi-Objective Optimization of Independent Variables for Aisi 304 Stainless Steel Table 6 Estimated Regression Coefficients for OC Term coef CONSTANT A TON TOFF V GAP A*A TON*TON TOFF*TOFF A*GAP TON*GAP V*GAP S = SE coef T R Sq =98.75 P R Sq (adj) = The above shown tables demonstrate that pulse on-time, peak current significantly effects on material removal rate and also have effects on the Tool wear rate and it likewise observed that second order peak current and pulse off time significantly has effects on Over Cut. The multiple regression coefficients were calculated to check whether the model has actually defined the experimental data or not. The percentages of multiple regression coefficients were observed to be 82.28%, % and 98.75% for TWR, MRR and OC respectively. When the regression coefficients have larger value, it tends to be said the second order model that are sufficient for the process. Surface plot for the responses (TWR, MRR and OC) plotted by using most significant parameters and significant parameters and therefore others parameters were kept constant due to their influence is not much on the responses and it has shown in the figure 4(a),4(b) and 4(c). MRR TWR Hold Values TOFF 12 V 2.5 GA P 2.5 Hold Values TOFF 9 V 2 GA P M RR 0.10 T WR A T ON A 6.0 T ON z Figure 4 a Surface Plot for MRR Figure 4 b Surface plot for TWR OC Hold Values TON 15 V 2 GA P OC A T O FF Figure 4 c Surface Plot for OC editor@iaeme.com

9 Arunbharathi Ramaswamy, Ashoka Varthanan P, Abinash Raju R, Abdul Haathi M and Aravind Kumar G.B From the surface plots we came to know that MRR increases while there is an increase in pulse current value. The reasons that the discharged energy has increased to facilitate the action of vaporization and melting and to early large impulsive force in the spark gap, therefore it increases the material removal rate(mrr). In case of high current, Material removal rate is high and more concentrated at the electrode gap as well as debris is larger. Bridging the gap between work piece and electrode because of the excessive concentration of larger size of debris and subsequently short circuits which reduces MRR. From the figure 4(b) shown that TWR increases while there is an increasing in pulse on time and pulse current. Therefore, the large pulse duration and large pulse current will produced larges discharge energy and it causes a Large Tool Wear Rate in the work piece. Due to the low level of pulse on time and high level of pulse current, reduces the TWR value. Many designed investigations which comprises determining optimal conditions that can be produces the best resultant value for these responses. The operating conditions can be controlled depends upon the design type (responses surface, factorial or mixtures). It may include one or more following design parameters are components, factors, amount of variables or process variables. Response optimizer has been utilized for providing optimal solutions of optimization plot and the input variable combinations. The optimization plot is more interactive and by adjusting input parameters settings on the plot to obtain more desirable solutions. Using Response optimizer plot to identify the optimal combination of parameters are shown in the figure 5. Figure 5 Response Optimizer The figure 5 shows that optimal solution for the EDM drilling process among various DOE combination. The optimized value for the process can be indicated through square bracket at the top of the figure 5. The optimized value is engaged between the high and low range of process parameters value. The best optimal parameter for output reaction on work piece AISI 304 stainless steel as follows, T off = 12 μs, T on = 20 μs, Ip = 5 amp, V = 3 volt and Gap distance = 2mm. 5. CONCLUSION Hole drilling EDM machine has been used to carry out the experiments and the process parameters were optimized to attain the desired performances. From the experimental results, it is inferred that current and sparking time has major effect on material removal rate and tool wear rate. Also it has been observed that pause time and higher order of current has major influence on over cut editor@iaeme.com

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