International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
INTERNATIONAL JOURNAL OF MECHANICAL EN...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) ...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) ...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) ...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) ...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) ...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) ...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) ...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) ...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) ...
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  1. 1. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 6, November - December (2013) © IAEME AND TECHNOLOGY (IJMET) ISSN 0976 – 6340 (Print) ISSN 0976 – 6359 (Online) Volume 4, Issue 6, November - December (2013), pp. 275-284 © IAEME: www.iaeme.com/ijmet.asp Journal Impact Factor (2013): 5.7731 (Calculated by GISI) www.jifactor.com IJMET ©IAEME PREDICTION AND OPTIMIZATION OF PROCESS PARAMETERS FOR COPPER ALLOY TO CONTROL THE FLASH USING NON-TRADITIONAL ALGORITHM P. SHIVA SHANKAR Department of Mechanical Engineering, University College of Engineering and Technology, Mahatma Gandhi University, Nalgonda, Andhra Pradesh – 508001 ABSTRACT Friction welding is a solid state bonding technique which utilizes the heat generated by the faying surfaces. Friction welding is widely used for joining of similar and non- similar materials. Friction welding process is generally a multi input and multi output process, so we require the parameters to optimized for the sound weld. In present study the input variables are Friction pressure(FT), Friction time (FT), Upset time (UT) and Upset pressure (UP) similarly the output variables are considered to be Flash width (FW), Flash height (FH) and Flash thickness (FT). Empirical relation is formulated using by design Expert. Integration of these techniques in order to minimize the metal losses without sacrificing the Tensile Strength of the joints. INTRODUCTION Friction welding is a solid state welding process where the frictional heat is generated between the two faying surfaces. The temperature is raised up to higher interface enough to cause the two surfaces to be forged together under high forge pressure. In this friction welding we have two types of principles i.e, continuous drive friction welding for the circular geometry and linear friction welding for the other geometry like square, rectangular and etc. in continuous drive friction welding one of the work piece is fixed in rotating spindle which is driven by the motor, while the other is restricted form the rotation. the work piece in the spindle is rotated in a predetermined constant speed of 1500 rpm and the other is brought together with in 3rd stage for the predetermined time or until a preset amount of axial shortening takes place and then the Forge pressure is applied. Lastely the rotation is stopped abruptly by the help of the braking force. 275
  2. 2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 6, November - December (2013) © IAEME Fig 1: Steps involved in different phases Fig 2: Different parameters variying with repect to the TIME The input parameter that control the joints are Friction pressure (FP), Friction time (FT), Upset time (UT) and Upset pressure (UP). The output variables are Flash width (FW), Flash height (FH) and Flash thickness (FT). To get good quality of weld these parameters to be optimized. Optimization of friction welding parameters will be time consuming if conventional technique is employed, by concentrating on the single parameter whereas the other is kept constant. So the optimization of the parameter in this work by Design of Experiment technique are used especially TAGUCHI METHOD Orthogonal Array (L9). The main concentration of this study is to minimize the loss of material due to flash by controlling the attributes like Flash width (FW), Flash height (FH) and Flash thickness (FT) without compromising the Tensile Strength of the joint. MIMUM [1] investigated the hardness variations and the microstructure at the interfaces of steel welded joints. PAVENTHAN [2] investigated on the optimization of friction welding parameters to get good tensile strength of dissimilar metals. ANANTHAPADMANABAN [3] reported the experimental studies on the effect of friction welding parameters on properties of steel. DOBROVIDOV [4] investigated the selection of optimum conditions for the friction welding of high speed steel to carbon steel. SARALA UPADHYA [5] studied the mechanical behavior and microstructure of the rotary friction welding of titanium alloy. An extensive literature survey revealed that very few investigations was conducted on the friction welding of copper alloys (Catridge Brass) and their weldability aspects using Design of Experiments techniques. The aim of the present study is to minimize the loss of material due to flash 276
  3. 3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 6, November - December (2013) © IAEME by controlling the attributes like Flash width (FW), Flash height (FH) and Flash thickness (FT) without compromising the Tensile Strength of the joint. All the above investigations were carried out on trial and other basis to attain optimum welding strength. Hence in this investigation an attempt was made on similar non- ferrous metal which has low co-efficient of friction (0.15) to optimize the friction welding parameters for minimizing the loss of material using Taguchi Method, Simulated Annealing and Artificial Neural Networks are involved in this study. In order to determine the welding process parameter that produce the optimized Flash width (FW), Flash height (FH) and Flash thickness (FT) in friction welding had applied the non-traditional optimization technique called simulated annealing(SA). Artificial neural network (ANN) technique is suitably integrated with simulated annealing (SA). Simulation results confirm the feasibility of this approach and show a good agreement with experimental results. EXPERIMENTAL PROCEDURE MACHINE SPECIFICATIONS: The present investigation was performed on continuous drive friction welding machine type FWT - 12 with a maximum load of 120 KN with cylinder area of 73 cm2 and 80cm2 by which the friction and forge pressure is applied with the help of support hydraulic arrangement, speed ranging 1000- 3000 rpm, pressure 10 – 50 bar. The hydraulic system is maintained by powerful servomotors driven by the hydraulic power pack. The speed of the friction welding machine is controlled by the magnetic brakes which more effective of all braking systems. MATERIAL: The material used in the present investigation was Copper Alloy: Cu Zn30. The major alloying elements are copper (CU) – 70 % and Zinc (ZN) – 30%. CuZn30 has good mechanical and physical properties according to ASTM material specifications. The physical properties like melting range of solidus and liquidus are 915and 9550C respectively, Density 8.53 gm/cm @ 200C, Specific Gravity 8.53, Co-efficient of Thermal Expansion – 0.0000199 /0C (20 – 2000C), Annealing Temperature 425- 7500 C, Hot – Working temperature 725- 8500C, Hardness Rockwell F – 78, Hardness HR30T – 43, Ultimate Tensile Strength 330 – 372 Mpa, Yield Strength – 150Mpa, Modulus of Elasticity – 110 Gpa, Poisson’s ratio 0.375, Machinability – 30 %, Co- Efficient of Friction - 0.15. The material holds fair to excellent corrosion resistance, excellent cold workability, good hot formability, were used as the parent material in the study. From the literature survey the predominant factor which has great influence the friction weld (FW) joints were identified. Trial experiments were conducted to determine the working range of the parameters. The feasible limits of the parameters were chosen in such a way that it is not effecting external defects. The important parameters influencing are heating time 4 – 5 sec, heating pressure 10- 20 bar, upset time 3- 5 sec and upset pressure 20-30 bar and were used to produce the welded joint of the given material. The speed of spindle (RPM) is kept constant by 1500rpm which obtained in the previous study. Theoretical Optimization was carried out in order to minimize the Flash width (FW), Flash height (FH) and Flash thickness (FT) of the joint by Simulated Annealing. The process was considered to be multi input and multi output variable process. Flash parameters play an important role in determining properties of the weld. Theoretical and Experimental variations in the Flash width (FW), Flash height (FH) and Flash thickness (FT) of the joint were also predicted. Mathematical equation was formulated to represent the objective function and back propagation neural network was designed and trained to have the relationship between the input parameters and output parameters. The relationship obtained between input parameter and output parameters by artificial neural networks (ANN) was optimized by using Simulated Annealing 277
  4. 4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 6, November - December (2013) © IAEME TABLE 1: Input Parameters Range Sl.no Parameter Range 1 Heating Pressure 10-20 bar 2 Heating Time 4 - 6 sec 3 Upsetting Pressure 4 Upsetting Time 20- 30 bar 3- 5 sec General Model Process modeling and optimization are very important issues in welding. The ANN is used to map the input/output relationships of the process. An objective function variable f can then be defined as F = W1(FW) + W2( FH) + W3(FT) Where W1, W2, W3 are the weights for the normalized Flash width (FW), normalized Flash height (FH) and normalized Flash thickness (FT) of the weld respectively. Experiments are conducted according to the taguchi Orthogonal Array matrix (L9) were measured form the each set of data. From the experimental data, the ANN is trained. Parameters bounds of heating pressure, heating time, upsetting pressure and upsetting time Heating Pressure HPL ≤ HP ≤ HPU Heating Time HTL ≤ HT ≤ HTU Upsetting Pressure UPL ≤ UP ≤ UPU Upsetting Time UTL ≤ UT ≤ UTU Where the subscript ‘L’ and ‘U’ indicates the lower and upper boundaries respectively. HP FW HT FH UP FT UT Input layer Hidden layer Output layer Fig 3: Configuration of the Back Propagation Network for Friction Welding 278
  5. 5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 6, November - December (2013) © IAEME Simulated Annealing Algorithm Traditionally the annealing process used in metal working involves heating the metal to a high temperature and then letting it gradually cools down to reach a minimum stable energy state. Based on the metropolis criterion, a search algorithm called “Simulated Annealing” was developed. Simulated Annealing is a Monte Carlo approach used ffor minimizing multivariate functions. The term Simulated Annealing derives from the analogous physical process of heating and cooling a substance to obtain a strong crystalline structure. The Simulated Annealing process lowers the temperature by slow stage until the system “freezes” and no further changes occur. At each temperature the simulation must process long enough for the system to reach steady state or thermal equilibrium. It has been shown that the Simulated Annealing Algorithm processes several advantages in comparisons with a traditional search algorithm. First the Simulated Annealing algorithm does not required for most traditional search algorithm. This means that the Simulated Annealing algorithm can be applied to all kinds of objective and constraint functions. Next the Simulated Annealing algorithm with probabilistic hill- climbing characteristics can find the global minimum more efficiently instead of becoming trapped in a local minimum, where the objective function has surrounding barriers. Further Simulated Annealing algorithm search in independent of initial conditions. As the results, the Simulated Annealing algorithm has emerged as a general optimization tool and has been successfully applied in many manufacturing tasks. This Simulated Annealing algorithm simulates function value in a minimization problem. The algorithm begins with an initial point X1( HP1, HT1, UP1 & UT1) and a high temperature T, a second point X2 (HP2, HT2, UP2 & UT2) is created using Gaussian – Distribution and the difference in the function values ( E) at these points is calculated. The point is accepted with a probability exp ( E/T). This completes one iteration of the simulated annealing procedure. The algorithm is terminated when a sufficient small temperature is obtained or a small enough change in a function value is obtained. Simulated annealing steps Step 1 : choose an initial point X1, set Ts a sufficiently high value. Cooling rate Cr, set Te = 0 Step 2 : calculate a neighboring point using Gaussian Distribution ௡ X2 = ‫ݔ‬ଵ ൅ ߪ ቂ∑௡ ‫ݎ‬௜ െ ଶ ቃ ௜ୀଵ Variance = σ = (maximum value of parameter – minimum value of the parameter) / 6 Where ‘n’ is number of random numbers, r1 is the random numbers. Step 3: if E = E (X (t+1) – E(X (t)) > 0 i.e. if the difference in the fitness value is possible then, set Ts = Ts * Cr, else create one random number ® in the range (0, 1). If the r ≤ exp (( E/Ts) set Ts = Ts * Cr else go to step 2. Where E – difference between two consecutive fitness values. Step 4: if the temperature in small, terminate, else go to step 2. 279
  6. 6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 6, November - December (2013) © IAEME CHOOSE AND INITIAL POINT ( FW1, FH1 &FT1) SET INITIAL TEMPERATURE = T COOLING RATE = Cr SET FINAL TEMPERATURE = t CALCULATE FITNESS f1 = f(FW1, FH1 &FT1) CALCULATE NEIGHBOUR HOOD FW2 = FW1 ൅ ߪ ቂ∑௡ ‫ݎ‬௜ ௜ୀଵ FW2 = FW1 ൅ ߪ ቂ∑௡ ‫ݎ‬௜ ௜ୀଵ FW2 = FW1 ൅ ߪ ቂ∑௡ ‫ݎ‬௜ ௜ୀଵ ௡ െ ଶቃ ௡ െ ଶቃ (FW , FH NEIGHBOUR FITNESS f1 = f ௡ െ ቃ ଶ &FT ) f2 > f1 N ACCEPT f1 = f2 T = T x Cr Y r<e-(∆/T) Y T> t PRINT RESULTS Fig 4: Flow chart for the Simulated Annealing Algorithm Table 3: Parameter level and their ranges PARAMETER LOW MEDIUM HIGH 10 Heating pressure 15 20 Heating time Upsetting pressure 4 20 5 25 6 30 Upsetting time 3 4 5 The experiments were conducted by the design of experiment, Taguchi orthogonal Array (L9), as this study is for four factors with three levels so L9 Orthogonal Array is suitable for the experiment, which can complete the experimentation within 9 runs out of conducting 81 runs as for full factorial method. So by this material is saved at the time of experimentation. 280
  7. 7. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 6, November - December (2013) © IAEME Table 4: The experimental results for the given input parameters and the output variables is furnished as below Factors Heating pressure Heating time Upsetting pressure Upsetting time Flash width Flash height Flash thickness (bar) (sec) (bar) (sec) (mm) (mm) (mm) 1 10 4 20 3 9.5 5.2 4.2 2 10 5 25 4 10.5 5.5 4.9 3 10 6 30 5 16.4 5.7 8.2 4 15 4 25 5 9.8 5.3 6.0 5 15 5 30 3 11.4 5.3 6.2 6 15 6 20 4 12.8 5.0 6.6 7 20 4 30 4 11.5 4.2 6.0 8 20 5 20 5 11.1 4.2 6.5 9 20 6 20 3 6.8 4 4.1 RUNS RESULTS AND DISCUSSION Typical macrograph of the friction welded sample is shown in the figure 5. The metal loss in friction welding joint in the form of flash was observed. Minimization of the loss of metal without compromising the Tensile Strength of the welded joint. It is possible to validate the joints by assessing the flash parameters such as Flash width (FW), Flash height (FH) and Flash thickness (FT). The flash features are shown in the figure 6 below. The friction welding is carried out by using Orthogonal Array L9 (3x4) for 4 parameters with 3 levels. The process parameters and the experimental results are shown in the tables mentioned below: 281
  8. 8. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 6, November - December (2013) © IAEME TABLE 6: Experimental, predicted and percentage of errors for flash width, flash height and flash thickness with Heating Pressure HEATING PRESSURE V/S FLASH FEATURES EXPERIMENTAL VALUE(mm) S.NO 1 10 12.13 2 15 3 20 PERCENTAGE OF ERRORS (%) PREDICTED VALUE(mm) 5.46 5.76 12.2897 5.4878 5.8405 11.33 5.2 6.26 11.3691 5.2320 6.2277 9.8 4.13 5.53 9.7902 4.1797 -1.317 5.5147 -0.51 -1.399 -0.345 -0.6164 -0.5147 0.0991 -1.2041 0.2754 TABLE 7: Experimental, predicted and percentage of errors for flash width, flash height and flash thickness with Heating Time HEATING TIME V/S FLASH FEATURES 1 HEATING TIME (SEC) 4 2 3 S.NO EXPERIMENTAL VALUE (mm) PREDICTED VALUE (mm) PERCENTAGE OF ERRORS (%) 10.26 4.9 5.4 10.3951 4.9249 5.4755 -1.317 5 11 5 5.387 11.0379 5.0308 5.8397 -0.345 6 12 4.9 6.3 11.9882 4.9594 6.2826 0.0981 -0.51 0.6166 1.2131 -1.399 0.5146 0.2760 TABLE 8: Experimental, predicted and percentage of errors for flash width, flash height and flash thickness with Heating Pressure UPSETTING PRESSURE V/S FLASH FEATURES 1 UPSETTING PRESSURE (MPA) 20 11.13 4.80 5.77 11.2764 4.8249 5.8501 -1.316 -0.52 -1.389 2 25 9.03 4.93 5 9.0617 4.9729 4.9742 -0.351 -0.6166 -0.5152 3 30 13.1 5.07 6.8 13.0870 5.1311 6.7812 0.0990 -1.2048 0.2752 S.NO EXPERIMENTAL VALUE(mm) PREDICTED VALUE(mm) PERCENTAGE OF ERRORS (%) TABLE 9: Experimental, predicted and percentage of errors for flash width, flash height and flash thickness with Heating Pressure UPSETTING TIME V/S FLASH FEATURES S.NO UPSETTING TIME (SEC) EXPERIMENTAL VALUE(mm) 1 3 9.23 4.83 4.83 9.3515 4.8546 4.8975 -1.317 -0.51 -1.399 2 4 11.6 4.9 5.83 11.6401 4.9301 5.7999 -0.345 -0.616 -0.5146 3 5 12.43 5.07 6.9 12.4423 5.1315 6.8810 0.0991 -1.2131 0.2753 PREDICTED VALUE(mm) PERCENTAGE OF ERRORS (%) Artificial neural network is trained using the trained ANN the theoretical prediction of the flash was carried. The variations of the flash features with the process parameters like heating pressure, heating time, upsetting pressure and upsetting time are inscribes in the tables below respectively. The experimental and predicted results with percentage of errors between them are also included in tables. The variation of flash features can be understood, from the above tables, it is 282
  9. 9. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 6, November - December (2013) © IAEME observed that the increase in the heating pressure has an effect in the flash features. Heating pressure for the given heating time conditions the interface. If the heating pressure is increased the material expelled. The variation of flash features and the heating time is, with increase in the heating time increases the metal loss by way of increasing the dimensions of the formed flash. The duration of heating is selected so as to ensure the cleaned contact of faying surfaces by friction. The temperature at interface is increased to achieve the required softening for the joints. When heating time is too short, the heating effect become irregular, and unbounded region increases. Increase3d heating time results in increased metal loss. So an optimum level of heating time should be taken to minimize the metal loss. Upsetting pressure in conjunction with surface speed, determine the thermal conditions established in the weld region. Increase in the upsetting pressure increases the metal loss as the amount of flash is more. It can be understood that theoretically predicted flash features by ANN are almost closer to the experimental results. Good agreement between the predicted and the measured values of the flash width, flash height and flash thickness of the weld is observed from the presented tables. Hence the formulated ANN is reassembling the real process. By carrying out the experimental trials on this input parameter, minimized metal loss can be obtained. CONCLUSION Friction welding of similar material CuZn30 copper alloy is successfully performed. By way of conducting experiments on selecting the parameters by Taguchi design of Experiment and the flash features are measured. From the experimental data, ANN is trained. Trained network predicts the flash width, flash height and flash thickness are observed very closely. The percentage of variation between the actual and predicted is around 1.52 %. The global optimization technique called Simulated Annealing is applied to the network model to achieve optimized parameters though the optimized input. Parameters minimize the flash features such as Flash Width – 9.03mm, Flash Height – 4.13 mm and Flash Thickness – 4.83 mm. Table 10: optimized parameters and the flash Features Heating pressure Heating time Upsetting pressure Upsetting time (bar) (sec) (bar) (sec) 20 5 25 3 Flash width (mm) 9.03 Flash height (mm) Flash thickness (mm) 4.13 4.83 ACKNOWLEDGEMENTS The authors wish to acknowledge the support from the Mr. Yathin Thambe,Director- Friction Welding Pvt. Ltd, Pune and sincere thanks to the Ramanandathirta Engineering College, Nalgonda who permitted me to conduct the tests in their laboratories. Constructive comments and suggestions from the referee are also acknowledge. REFERENCES 1. 2. 3. Ross P J (1996) Taguchi Techniques for Quality Engineering (McGraw Hill, New York). Phadke M S (1989), Quality engineering Using Robust Design(PHI, NJ, USA). R. Fisher (1935)., The design of experiments, Oliver-Boyd, Edinburgh. 283
  10. 10. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 6, November - December (2013) © IAEME 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. H. Kreye And G. Reiner, (1986) The ASM Conference on Trends in Welding Research, (ASM International Metals Park,) PP. 728-731. M.Aritoshi, K. Okita, T. Endo, K. Ikeuchi and F. Matsuda, (1977) Trends in welding technology (Japan. Welding Society). 8 50. M. J. Cola(1992)., M.A.Sc thesis, Ohio State University, OH. M.J.Cola and W. A. Baeslack, in Proceedings of the 3rd International. SAMPE Conference, Toronto Oct., 1992, edited by D. H. Froes, W. Wallace, R. A. Cull, and E. Struckholt, Vol. 3, PP 424-438. Aeronautics for Europe Office for Official Publications of the European Communities, 2000. Esslinger, J. Proceedings of the 10th World conference of titanium (Ed. G. LUTJERING) Wiley-VCH, WEINHEIM, Germany, 2003. Roder O., Hem D., Lutjering G. Proceedings of the 10th World conference of titanium (Ed. G. LUTJERING) Wiley-VCH, WEINHEIM, Germany, 2003. Barreda J.L., Santamaría F., Azpiroz X., Irisarri A.M. Y Varona J.M. “Electron beam welded high thickness Ti6Al4V plates using filler metal of similar and different composition to the base plate”. Vacuum 62 (2-3), 2001.PP 143-150. Eizaguirre I., Barreda J.L., Azpiroz X., Santamaria F. Y Irisarri A.M. “Fracture toughness of the weldments of thick plates of two titanium alloys”. Titanium 99, Proceedings of the 9th World Conference on Titanium: Saint Petersburg, (1999), PP. 1734-1740. P T Houldcroft, (1977) “Welding Process technology”, Cambridge University Press, Cambridge 1977,p1. “Exploiting Friction Welding in Production”, (1997) Information Package Series, The Welding Institute, Cambridge,. p. satiya ( 2006)” optimization of friction welding parameters using simulated Annealing”, Indian . j. engg & mat sci, vol. 13 PP. 37-44. D. Kanakaraja, P. Hema and K. Ravindranath, “Comparative Study on Different Pin Geometries of Tool Profile in Friction Stir Welding using Artificial Neural Networks”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 4, Issue 2, 2013, pp. 245 - 253, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. B. D. Gurav and S.D. Ambekar, “Optimization of the Welding Parameters in Resistance Spot Welding”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 4, Issue 5, 2013, pp. 31 - 36, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. Kannan.P, K.Balamurugan and K. Thirunavukkarasu, “Experimental Investigation on the Influence of Silver Interlayer In Particle Fracture of Dissimilar Friction Welds”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 3, Issue 2, 2012, pp. 32 - 37, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. C.Devanathan, A.Murugan and A.Suresh Babu, “Optimization of Process Parameters in Friction Stir Welding of Al 6063”, International Journal of Design and Manufacturing Technology (IJDMT), Volume 4, Issue 2, 2013, pp. 42 - 48, ISSN Print: 0976 – 6995, ISSN Online: 0976 – 7002. U.S.Patil and M.S.Kadam, “Effect of the Welding Process Parameter in Mmaw for Joining of Dissimilar Metals and Parameter Optimization using Artificial Neural Fuzzy Interface System”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 4, Issue 2, 2013, pp. 79 - 85, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. P. Shiva Shankar, “Experimental Investigation and Stastical Analysis of the Friction Welding Parameters for the Copper Alloy – Cu Zn30 using Design of Experiment”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 4, Issue 5, 2013, pp. 235 - 243, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. 284

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