RSM AND ANOVA: AN APPROACH SELECTION OF PROCESS PARAMETERS OF EDM OF ALUMINIUM TITANIUM DIBORIDE (AL-TIB2)

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1 International Journal of Civil Engineering and Technology (IJCIET) Volume 8, Issue 6, June 2017, pp , Article ID: IJCIET_08_06_028 Available online at aeme.com/ijciet/issues.asp?jtype=ijciet&vtyp pe=8&itype=6 ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed RSM AND ANOVA: AN APPROACH FOR SELECTION OF PROCESS PARAMETERS OF EDM OF ALUMINIUM TITANIUM DIBORIDE (AL-TIB2) Kim J Seelan Assistant Professor, Department of Mechanical Engineering, John Cox Memorial CSI Institute of Technology, Trivandrum, Kerala, India R. Rajesh Professor & Head, Department of Mechanical Engineering, Noorul Islam University, Kumaracoil, Tamilnadu, India S. Pugazhendhi Professor & Head, Department of Manufacturing Engineering, Annamalai University, Annamalainagar, Tamilnadu, India Liji R. F Assistant Professor, Department of Electronics and Communication Engineering, John Cox Memorial CSI Institute of Technology, Trivandrum, Kerala, India ABSTRACT The objective of the percent study is to evaluate which input parameters contribute more for Electric Discharge Machining (EDM) of Al-TiB2 using ANOVA technique. In manufacturing industry Metal Metrics Composites (MMCs) of aluminium has its own significance in percent era. In which LM25 has its role as it have high corrosion resistance properties incorporate with greater mechanical properties and has its own significant application in modern engineering areas. LM25 alloy is stir cast with halides (salts of titanium and boride) to get Al-TiB2 MMC is used for the current study. Electric Discharge Machining is one of the modern machining techniques (non- temperature conventional machining technique) used to machine high strength, resistant materials by means of spark erosion is used for machining Al-TiB2 with copper (commercial grade copper) as electrode. The output parameters used for this study are Metal Removal Rate (MRR), Tool Removal Rate (TWR) and Surface Roughness (Ra). The process parameters used in this study includes Voltage, Discharge Current, Spark-on-Time and Spark-off-Time. RSM modelling method is used for Design of experiment. ANOVA technique was implemented to find the priority of process parameters used during machining. It was found that sound castings (MMCs) can be made by using stir casting route. The Input Current is the editor@iaeme.com

2 Kim J Seelan, R. Rajesh, S. Pugazhendhi and Liji R. F most important parameter which affects machinability of EDM. The least contributed parameter is Spark OFF time. 3D surface plots are plotted in order to evaluate the potential relations between them. Key words: Metal Metrics Composites (MMCs), Metal Removal Rate (MRR), Tool Removal Rate (TWR) and Surface Roughness (Ra), RSM, ANOVA. Cite this Article: Kim J Seelan, R. Rajesh, S. Pugazhendhi and Liji R. F. RSM and Anova: An Approach for Selection of Process Parameters of EDM of Aluminium Titanium Diboride (AL-TIB2). International Journal of Civil Engineering and Technology, 8(6), 2017, pp INTRODUCTION In various grade of aluminium alloy LM25 aluminium alloy (Al Si7Mg) can play a greater role in achieving higher corrosion resistive metal with combined greater mechanical properties Jayakumae et. al. 2014, Suresh et.al This alloy is commonly used in almost all industries where complex shape with greater casting soundness is required. Its major application includes marine, electrical, food, chemical and various other industries. Its main significant is that it has excellent resistance to seawater attack. It is most preferable for building automobile parts like cylinder blocks and head, engine body, chassis, wheels rim, etc. It also poses excellent weldability. In order to strength Aluminium alloy (LM25) MMC of Al-TiB2 can made by adding halides and fabricateusing stir casting routelawrance et.al. 2015, Ramanujan et. al Halides of Titanium and Boride salt (K 2 TiF 6 and KBF 4 )has good metallurgical bound between aluminium alloys using stir casting. From this 6% MMC were made for the study. Electric Discharge Machining (EDM) is the unconventional machining technique to machine materials with greater mechanical properties with complicated shape is difficult to machine Srikanth et al. 2015, Shaaz et. al. 2014, Sushil et al In this process higher electric spark is used for eroding the work piece which is dipped inside the electrolyte.an electrode with a specific shape defines the area in which spark erosion has to occur, thus determining the shape of the resulting cavity or hole in the work piece. It is applied in machining high strength, temperature resistant alloys specifically in aeronautical, automobile and other manufacturing industries. In electrical discharge machining there are many process variables which influence the Material Removal Rate (MRR)of the work piece. This output parameter is governed by various input parameters like input current, gap voltage, spark gap, dielectric fluid, sparking time and frequency of sparks. For this experiment copper rod of 10 mm diameter is selected as electrode. Process parameters like Voltage V (Volt), Impulse Current - I (Amp), Sparkon-Time T ON (ϻm) and Spark-off-Time T OFF (ϻm)and Commercial grade EDM oil (specific gravity= 0.763, freezing point= 94 C) as Electrolyte. In-order to reduce the number of experiment various prediction methods should be applied to explain the desired output variables through developing mathematical models to postulate the relationship between the input parameters and output variables. The Response Surface Methodology (RSM) is very helpful in developing such a suitable approximation for generating functional relationship between the variables Singaram et.al Baraskar et.al. 2011, Sameh et.al For the present study mathematical models by RSM has been generated to predict the effect of the process parameters of Electrical Discharge Machining (EDM) on MRR. Minitab 14 was used for the modelling editor@iaeme.com

3 RSM and Anova: An Approach for Selection of Process Parameters of EDM of Aluminium Titanium Diboride (AL-TIB2) Analysis of Variance (ANOVA) Vishnu et.al 2013, Bala et.al table was made to find the significant of the parameters which affects the machining characteristics. Analysis of variance (ANOVA) tests the hypothesis that the means of two or more populations are equal. ANOVAs assess the importance of one or more factors by comparing the response variable means at the different factor levels. The null hypothesis states that all population means (factor level means) are equal while the alternative hypothesis states that at least one is different. 2. EXPERIMENTAL PROCEDURE 2.1. Material Selection Aluminium LM25 alloy is selected for the study, its chemical composition is as in Table 1, paved the path for selection of LM25. It has the capability to slow destruction (non corrosive) property.it is commonly used in marine, electrical, automobile and various other noncorrosive manufacturing applications. Aluminium alloy LM25 was heated to a temperature of780 o C in a furnace after chipping the bar to fine chips using a shaper machine. When it reaches to the required temperature 6% salt (both Ti and B salt) (K 2 TiF 6 and KBF 4 ) has been added uniformly and stir casted by maintaining the temperature at C. Argon gas is also supplied for shielding purpose. After proper mixing and escape of fumes it was poured into the die and thus ingot was made. It was then sliced and milled and prepared for machining. Table 1 Composition of LM25 Aluminium alloy Alloy Cu Mg Si Fe Mn Ni Zn Pb Sn Ti LM Electric Discharge Machining For experimentation was conducted on Sparkonix SN35 EDM machine at CSI Industrial Training Centre Kodiyannoorkonam, Thiruvananthapuram. A figure of Sparkoniz SN35 is in Figure 1.After various trials made on Al-TiB2 alloy the range for the input parameters were fixed, machining was done by varying4 parameters for 3 levels as seen in Table 2. Machined for 5 min. Weight of the work piece was measured before (Wi) and after (Wf) each machine run with the help of a electronic weight balance in order to find the Metal Removal Rate (MRR) MRR= Where, Dw is density of work piece and t time taken for machining. Wi is the weight of work piece before machining. Wf is the weight of work piece after machining editor@iaeme.com

4 Kim J Seelan, R. Rajesh, S. Pugazhendhi and Liji R. F Table 2 Process Parameter Sl. No. Machining Parameter 1 Voltage, V (Volt) 2 Current, I (Amp) 3 Spark ON Time, TON (ϻ Levels ϻs) Spark OFF Time, TOFFF (ϻs) Design of Experiment using Response Surface Methodology (RSM) The combined use of these Design of Experiment and regression analysis techniques were made to create both first and second-order models, by which it can explain the variability associated with all the technological considered variables for this study. The method called Response Surface Methodology (RSM) was used to acquire proper parameter settings for the wanted responses. Minitab 14 was used for developing the orthogonal array. For the present study the experiment were designed based on Central Composite Design (CCD). The Face centred CCD were implemented with 16 cube points, 4 centre points in cube, 8 axial points and 2 centre points in axial. Full factorial design with all combination of factors at 4 factors, 3 levels and 30 experiments were implemented. 3. RESULTS AND DISCUSSIONS Figure 1 Sparkonix SN35 The Modelling array formulated for EDM machining using MINITAB 14 and the result after the machining are shown in Table3. The main effect plot (dataa means) for MRR from Minitab software is shown in Figure 2.3D surface plots plotted to explore the potential relationship between three variables. The figure shows Mean of MRR Vs Voltage, Mean of MRR Vs Current, Mean of MRR Vs Spark ON time, Mean of MRR Vs Spark OFF time. Figure 3 gives the Surface plots for MRR. Figure 3 (a) (f) shows various surface plots of MRR editor@iaeme.com

5 RSM and Anova: An Approach for Selection of Process Parameters of EDM of Aluminium Titanium Diboride (AL-TIB2) Expt. No Voltage, V (Volt) Current, I (Amp) Figure 2 Main effect plot for MRR Table 3 Experimental Result Spark ON Time (ϻm) Spark OFF Time (ϻm) MRR (mm3/min) TWR (mm3/min) SR (ϻm) editor@iaeme.com

6 Kim J Seelan, R. Rajesh, S. Pugazhendhi and Liji R. F (a) (b) (c ) (d) (e) (f) Figure 3 (a) (f). Surface Plots for MRR 3.1. ANOVA ANOVA is technique used to investigate the design parameters and to indicate which parameters are significantly affecting (contributing) the output parameters. In the analysis the sum of squares and variance are calculated. GLM is an ANOVA procedure editor@iaeme.com

7 RSM and Anova: An Approach for Selection of Process Parameters of EDM of Aluminium Titanium Diboride (AL-TIB2) 3.2. General Linear Model The general linear model is a generalization of multiple linear regression models to the case of more than one dependent variable. Least squares regression approach is used for the calculations. Here the statistical relationship between one or more predictors and a continuous response variable is described. The predictors used can be factors and co variants. Table 4 shows the levels and values of input factors. Table 4 Levels and Values of input factors. Factor Type Levels Values C1 fixed 3 40, 50, 60 C2 fixed 3 6, 10, 14 C3 fixed 3 4, 6, 8 C4 fixed 3 5, 7, General Linear Model: MRR Versus C1, C2, C3, C4 Analysis of Variance for MRR, using Adjusted SS for Tests. Table 5 shows the analysis parameters for MRR. The contribution of input parameters for MMR is shown in Table 6. A pie chart representation which shows the contribution of input parameters for MRR is shown in Figure 4 Table 5 Variance analysis for MRR Source DF Seq SS Adj SS Adj MS F P Voltage Current Spark ON Time Spark OFF Time Error Total S = R-Sq = 97.89% R-Sq(adj) = 97.09% Table 6 Percentage of contribution for MRR Source Sum of Square Contribution (%) Voltage, V Current, I Spark ON Time, TON (ϻs) Spark OFF Time, TOFF (ϻs) Error Total % Contribution of input Parameters for MRR 29% 12% 18% 39% Voltage, V Current, I Spark ON Time, TON (ϻs) Spark OFF Time, TOFF (ϻs) Error Figure 4 Contribution of input Parameters for MRR editor@iaeme.com

8 Kim J Seelan, R. Rajesh, S. Pugazhendhi and Liji R. F General Linear Model: TWR Versus Voltage, Current Analysis of Variance for TWR, using Adjusted SS for Tests Table 7 Percentage of contribution for TWR Source DF Seq SS Adj SS Adj MS F P Voltage Current Spark ON Time Spark OFF Time Error Total S = R-Sq = 99.32% R-Sq(adj) = 99.06% Source Sum of Square Contribution (%) Voltage, V Current, I Spark ON Time, TON (ϻs) Spark OFF Time, TOFF (ϻs) Error Total Contribution of input Parameters for TWR 3%1% 7% Voltage Current 37% Spark On 52% Spark Off Error Figure 5 Contribution of input parameters for TWR General Linear Model: RA Versus Voltage, Current Analysis of Variance for Ra, using Adjusted SS for Tests Table 8 Percentage of contribution for Ra Source DF Seq SS Adj SS Adj MS F P Voltage Current Spark ON Time Spark OFF Time Error Total S = R-Sq = 99.01% R-Sq(adj) = 98.64% editor@iaeme.com

9 RSM and Anova: An Approach for Selection of Process Parameters of EDM of Aluminium Titanium Diboride (AL-TIB2) Source Sum of Square Contribution Voltage, V Current, I Spark ON Time, TON (ϻs) Spark OFF Time, TOFF (ϻs) Error Total Contribution of input Parameters for Ra 4% Voltage, V 50% 39% Current, I Spark ON Time, TON (ϻs) Spark OFF Time, TOFF (ϻs) Error Total 0% 3% 4% 4. CONCLUSIONS Figure 6 Contribution of input Parameters for Ra MMC of Al-TiB2 (LM25-TiB2) can be fabricated using Stir Casting Route with better mechanical and metallurgical property. Applied current contribute more for the machining of Al-TiB2 MMC using EDM. MRR and TWR increases by increasing the Current and Spark ON Time. MRR and TWR will reduce when we increase the Spark OFF Time. Ra purely depends on Applied current; it increases by increasing the applied current. REFERENCES [1] Jayakumar A, Rangaraj M., Property,Analysis of Aluminium (LM-25) Metal Matrix Composite, International Journal of Emerging Technology and Advanced Engineering., Volume 4, Issue 2, 2014 pp [2] Suresh V, Maguteeswaran R, Sivasubramaniam R, Vadivel DS, Micro Tensile Behaviour of LM25 Aluminium Alloys by Stir Cast Method Compared with Finite Element Method, International Journal of Research in Mechanical Engineering. 2013, Volume 1, Issue 1, pp [3] Lawrance CA, Prabhu PS, Al 6061-TiB2 Metal Matrix Composite Synthesized with Different Reaction Holding Times by In-Situ Method, International Journal of Composite Materials Sep, Volume 4, Issue 9, pp [4] Ramanujam R, Venkatesan K, Kothawade N, Shivangkumar J, Dusane H, Fabrication of Al-TiB2 Metal Matrix Composites for Evaluation of Surface Characterization and Machinability, Indian Journal of Science and Technology Jan, Volume 8 Issue S2, pp editor@iaeme.com

10 Kim J Seelan, R. Rajesh, S. Pugazhendhi and Liji R. F [5] Srikanth P., Ch. Pranay Kumar, Electrical Discharge Machining Characteristics of Aluminium Metal Matrix Composites - A Review, International Journal of Science and Research Jun, Volume 4, Issue 6, pp [6] Shaaz Abulais, Current Research trends in Electric Discharge Machining(EDM) : Review, International Journal of Scientific & Engineering Research, Volume 5, Issue 6, 2014, pp [7] Sushil Kumar Choudhary, Dr. R.S Jadoun R.S., Current Advanced Research Development of Electric Discharge Machining (EDM): A Review, International Journal of Research in Advent Technology, Volume 2, Issue 3, 2014, pp [8] Singaram Lakshmanan, Mahesh Kumar, Optimization of EDM parameters using Response Surface Methodology for EN31 Tool Steel Machining, International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 2, Issue 5, 2013, pp [9] Baraskar S.S., Banwait S.S., Laroiya S.C., Mathematical Modeling of Electrical Discharge Machining Process through Response Surface Methodology, International Journal of Scientific & Engineering Research, Volume 2, Issue 11, 2011, pp [10] Sachin Kulkarni, Nilesh Sharanappanavar, Sameer Raichur, Arjun Dhavaleshwar and Vinayak Kulkarni, Modelling and Analysis of AlSiC HMMC using WEDM Process. International Journal of Mechanical Engineering and Technology, 7(5), 2016, pp [11] Sameh S. Habib, Study of the parameters in electrical discharge machining through response surface methodology approach, Applied Mathematical Modelling, Volume , pp [12] Minitab14. Minitab User Manual Release 14. State College, PA, USA., 2003 [13] Vishnu D Asal, Prof. Patel R.I., Alok B Choudhary, Optimization Of Process Parameters Of EDM Using ANOVA Method, International Journal of Engineering Research and Applications, Volume 3, Issue 2, 2013, pp [14] Mukesh Regmi, Anil Pol, Sachin Kulkarni, Multi Response Optimisation of Die Sinker EDM for Alsic Composite. International Journal of Mechanical Engineering and Technology, 7(3), 2016, pp [15] BalaMurugan Gopalsamy, Biswanath Mondal and Sukamal Ghosh, Taguchi method and ANOVA : An approach for process parameters optimization of hard machining while machining hardened steel, Journal of Scientific & Industrial Research, Volume 68, 2009, pp [16] R. Rajesh, M. Dev Anand and K.N. Benny. Prediction of EDM Process Parameters for AISI 1020 Steel Using RSM, GRA and ANN. International Journal of Mechanical Engineering and Technology, 8(5), 2017, pp [17] Das M.N., Giri N.C., Design and Analysis of experiments, 2 nd edn., New Age International Ltd. Publisher, New Delhi, editor@iaeme.com