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INTERNATIONAL6359(Online)Engineering and 2, March - April ENGINEERING International Journal of Mechanical 6340(Print), ISS...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) V...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) V...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) V...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) V...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) V...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) V...
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Effect of the welding process parameter in mmaw for joining of dissimilar metals

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Effect of the welding process parameter in mmaw for joining of dissimilar metals

  1. 1. INTERNATIONAL6359(Online)Engineering and 2, March - April ENGINEERING International Journal of Mechanical 6340(Print), ISSN 0976 – JOURNAL OF4,MECHANICAL (2013) ISSN 0976 – Volume Issue Technology (IJMET), © IAEME AND TECHNOLOGY (IJMET)ISSN 0976 – 6340 (Print)ISSN 0976 – 6359 (Online) IJMETVolume 4, Issue 2, March - April (2013), pp. 79-85© IAEME: www.iaeme.com/ijmet.aspJournal Impact Factor (2013): 5.7731 (Calculated by GISI) ©IAEMEwww.jifactor.com EFFECT OF THE WELDING PROCESS PARAMETER IN MMAW FOR JOINING OF DISSIMILAR METALS AND PARAMETER OPTIMIZATION USING ARTIFICIAL NEURAL FUZZY INTERFACE SYSTEM U.S.Patil1, M.S.Kadam2 1 (PG Student, Mechanical Engineering Department, Jawaharlal Nehru Engineering College, Aurangabad, India) 2 (Professor and Head of Mechanical Engineering Department, Jawaharlal Nehru Engineering College, Aurangabad, India) ABSTRACT In this research work, the optimization of welding input process parameters for obtaining greater weld strength with optimum metal deposition rate welding of dissimilar metals like stainless steel and Mild steel is done. The process used for welding is Manual Metal Arc welding and dissimilar metal used are low carbon steel and Stainless steel. Welding speed, voltage, current, electrode angle are taken as controlling variables. The weld strength (N/mm2) and Metal deposition rate (gms) are obtained through series of experiments according to Central Composite Design to develop the equation. Experimental results are analyzed through the Artificial Neural Fuzzy Interface System and the method is adopted to analyze the effect of each welding process parameter on the weld strength and Metal Deposition Rate, and the optimal process parameters are obtained to achieve greater weld strength. Validation of results obtained by Artificial Neural Fuzzy Interface System is done by using Experimental method. Keywords: Artificial Neural Fuzzy Interface System, Metal Deposition rate, Manual Metal Arc Welding, Response Surface Methodology, Weld strength. 79
  2. 2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEMEI INTRODUCTION In high pressure boilers, alloy materials are used for making the super heaterand economizer. The cost of alloy steel is very high and hence, in order to reduce thecost, the alloy steels may be combined with carbon steel. Hence, cost reduction is themain objective together with a better quality weld, so we use dissimilar metalswelding. A better quality weld in dissimilar metal welding is obtained by optimizing theprocess parameters because they play a vital role in deciding the weld strength. Someimportant parameters are welding current, welding voltage, welding speed, arc length, typeof electrode etc. These parameters can be selected based on screening experiments.Sivakumar et al [1] [8] .-proposed the optimization of the process parameters for MMAwelding of stainless steel and low carbon steel with greater weld strength has been reported.The higher-the-better quality characteristic is considered in the weld strength prediction. TheTaguchi method is adopted to solve this problem. The experimental result shows that theweld strength is greatly improved by using input parameters welding speed (353 mm /min),current (100 amps), voltage (30 volts). Mukhtar et al [2] – developed experimental work anddeveloped ANN model for prediction of weld bead geometry in gas tungsten arc (GTA)welding confirm that ANN tool can be fruitfully applied in modeling and predicting complexand nonlinear manufacturing processes with fair deal of accuracy. The effects of weld currentand weld speed are highly significant on bead geometry parameters. The effect of weldingvoltage is moderately significant, while that of gas flow rate in insignificant. Mustafa et al [3]-describes prediction of weld penetration as influenced .by FCAW process parameters ofwelding current, arc voltage, nozzle-to-plate distance, electrode-to - work angle and weldingspeed. Optimization of these parameters to maximize weld penetration is also investigated.The optimization result also shows that weld penetration attains its maximum value whenwelding current, arc voltage, nozzle-to-plate distance and electrode-to-work angle aremaximum and welding speed is minimum Srinivasa Rao et al [4] -focuses on studying theinfluence of various Micro Plasma Arc Welding process parameters like peak current, backcurrent, pulse and pulse width on the weld quality characteristics like weld pool geometry,microstructure, grain size, hardness and tensile properties. The results reveals that the usageof pulsing current, grain refinement has taken place in weld fusion zone, because of whichimprovement in weld quality characteristics have been observed. Rati Saluja et al [5] - dealswith the application of Factorial design approach for optimizing four submerged arc weldingparameters viz. welding current, arc voltage, welding speed and electrode stick out bydeveloping a mathematical model for sound quality bead width, bead penetration and weldreinforcement on butt joint. Kumanan et al [6] - details the application of Taguchi Techniqueand regression analysis to determine the optimal process parameters for submerged Arcwelding (SAW). Multiple regression analysis is conducted by using statistical packagesoftware and mathematical model is build to predict the bead geometry for any given weldingconditions. Result shows welding current and arc voltage are significant welding processparameters that affect the bead width. Saurav Datta et al [7] - considers four process controlparameters viz. voltage (OCV), wire feed rate, and traverse speed and electrode stick-out.The selected weld quality characteristics related to features of bead geometry are depth ofpenetration, reinforcement and bead width. This model was optimized finally within theexperimental domain using PSO (Particle Swarm Optimization) algorithm. The weld qualityimprovement is treated as a multi-factor, multi-objective optimization problem. 80
  3. 3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEMEII ARTIFICIAL NEURAL FUZZY INTERFACE SYSTEM METHOD OFOPTIMIZATION Artificial Neural Fuzzy Interface system is integration both neural networks and fuzzylogic principles, it has potential to capture the benefits of both in a single framework Neural framework.network in general is a highly interconnected network of a large number of processingelements called neurons in an architecture inspired by the brain”, as shown in figure 1. Neural architecturenetworks exhibits characteristics such as mapping capabilities or pattern association,generalization robustness, fault tolerance and parallel and high speed information processing.Neural networks learn by examples, they can, therefore be trained with known examples of a knownproblem to acquire knowledge about it, once appropriately trained, the network can be put toeffective use in solving unknown and or untrained instances of the problem. Neural networksadopt various learning techniques of which supervised learning and unsupervised learning Figure -1 Neuron and Artificial Neuron Neural network is composed of a large number of highly interconnected processingelements (neurons) working in unison to solve specific problems, information sharing takesplace across the synapses. Neural networks process information in a similar way the humanbrain does. The disadvantage is that because the network finds out how to solve the problemby itself, its operation can be unpredictable. On basis of this neural network, concept of twork,artificial neural network is introduced which mainly consist of inputs, weights, threshold orsummation and output neurons, model is introduced by scientist McCullough-Pitts Pitts.III DISSIMILAR METALS JOINING BY MMA WELDING PROCESS In the manual metal arc (MMA) welding process, a 3.15 mm diameter consumable hestainless steel 309 L Grade electrode is used to strike an electric arc with the base metal. Theheat generated by the electric arc is used to melt and join the base metal. In this st study anMMA welding machine is used to weld the base plates of 304 Stainless Steel and Mild Steel.The chemical composition of Mild steel is given in Table 1 and for Stainless steels given inTable 2. Two plates of size 150mm x 63 mm x 5 mm are tacked together to form a weld pad mmof 300 mm x 63 mm x 5mm .Welding is carried out in the down hand position and beads arelaid along the weld pad centerline to form a butt joint. The plates are allowed to cool to roomtemperature, after the completion of welding. As shown in figure 2 tion 81
  4. 4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME Table 1 Chemical Composition of Mild SteelComposition Carbon Manganese Silicon Sulphur Phosphorous Aluminium% 0.16 0.30 0.25 0.030 0.030 0.02 Table 2 Chemical Composition of Stainless Steel 304Composition Carbon Manganese Silicon Sulphur Phosphorous Aluminium% 0.0195 1.7153 0.2884 0.00086 0.0282 0.006A measurement of the tensile strength is performed by using an ultimate t str tensile testing(UTM) machine. Metal deposition rate is measured by measuring weight of work piece ne.before welding and after welding. SS 304 ELECTRODE 309 L PLATE 150X63X5 DOUBLE BUTT WELDED JOINT MS 150X 63X5 mm Figure 2 Manual metal arc welding set up The independently controllable process parameters affecting the weld strength and Metal deposition rate were identified to enable the carrying out of experimental work and developing the mathematical model. These are welding current (I), weldin speed (S), welding welding voltage (V), electrode angle (A). The Design of experiment is done by using Response surface Method. Experimental results are analyzed through the Artificial Neural Fuzzy Interface system. Factor and their operating level are shown in Table 3 Table 3 Factor And Operating Level S. Level Factor Unit no. Low High 1 Welding Current Amp 80 120 2 Welding Voltage Volt 360 420 3 Welding Speed mm/min 120 240 4 Electrode Angle Degree 30 150 Experimental runs are planned by using DOE table of RSM using central composite method. Total numbers runs are 31 for 4 factors with 5 operating levels. 82
  5. 5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME IV MATHEMATICAL MODELING Mathematical modeling is done by using Regression for each response (Metal deposition rate and welding strength) The regression equation is Metal Deposition (Gms) = 12.0 + 0.175 Welding Current (Amp)- 0.0167 Welding Voltage (volts) - 0.0562 Welding Speed (mm/min) + 0.0167 Electrode Angle Predictor Coef SE Coef T P Constant 11.978 5.546 2.16 0.040 Current 0.17500 0.02514 6.96 0.000 Voltage -0.01667 0.01257 -1.33 0.196 Speed -0.056250 0.006284 -8.95 0.000 Angle 0.016667 0.008379 1.99 0.057 S = 1.231 R-Sq = 83.8% R-Sq(adj) = 81.3% Welding Strength (N/mm2) = - 76.6 + 4.71 Welding Current (Amp) + 0.103 Welding Voltage (volts) - 0.316 Welding Speed (mm/min) - 0.102 Electrode Angle Predictor Coef SE Coef T P Constant -76.60 47.40 -1.62 0.118 Current 4.7067 0.2148 21.91 0.000 Voltage 0.1033 0.1074 0.96 0.345 Speed -0.31583 0.05371 -5.88 0.000 Angle -0.10222 0.07161 -1.43 0.165 S = 10.52 R-Sq = 95.2% R-Sq (adj) = 94.5% V OPTIMIZATION Optimization of process parameter is done by using Artificial Neural Fuzzy InterfaceSystem tool box in Matlab. Network in trained by using 31 data set obtained from experimentalwork and testing is done by using 15 data sets. Figure 3 Training and Testing of network Figure 4 – Network for Metal Deposition Rate 83
  6. 6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME Figure 5 Rules for prediction of metal deposition rate Figure 6 Rules for prediction of weld strengthVI RESULTS AND DISCUSSION Mathematical modeling for Metal deposition rate (Gms) is done by using Regressionwith Minitab 14 software and result gives R2 value as 84% indicating significance of model.For determining Metal deposition rate, welding current, welding speed and electrode angle aremost significant (as p < 0.05) while welding voltage is less significant. Similarlymathematical modeling of welding strength gives R2 value as 95% indicating significance ofmodel. Welding strength is significantly affected by welding current and welding speedWhile doing optimization of process parameter by using ANFIS method, once the network istrained by using training data, network is tested by using testing data set. Figure 3 show thetraining and testing of network. Artificial neural network is architected by using Matlab 7,shown in figure 4. On building network, rules for predicting output is developed by system,figure 5 and 6 respectively shows the rules for predicting the metal deposition rate and weldstrength. Results obtained by using ANFIS are validated by doing the experimental runs. 84
  7. 7. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEMECONCLUSION In this paper, the optimization of the process parameters for MMA welding of stainlesssteel and mild steel with greater weld strength and optimum metal deposition has beenreported. The higher-the-better quality characteristic is considered in the weld strengthprediction. The Artificial Neural Fuzzy Interface system is used to solve this problem. Theexperimental result shows that the weld strength can be controlled, according to demand bysetting the input value predicted by ANFIS system.REFERENCES[1] Sivakumar M. “ Process Parameter Optimization in ARC Welding of Dissimilar Metals”Thammasat Int. J. Sc. Tech., Vol. 15, No. 3, July-September 2010[2] Mukhtar, H. Sahir “ANN Assisted Prediction of Weld Bead Geometry in Gas Tungsten ArcWelding of HSLA Steels” Proceedings of the World Congress on Engineering 2011 Vol I, WCE2011, July 6 - 8, 2011, London, U.K[3] N.B.Mostafa et al “Optimization of welding parameters for weld penetration in FCAW”journal of Achievements in Materials and Manufacturing Engineering, volume 16 issue 1-2,May-June 2006[4] Srinivasa Rao et al “A Study on Weld Quality Characteristics of Pulsed current Micro PlasmaArc Welding of SS304L Sheets” 2011 International Transaction Journal of Engineering, Management,& Applied Sciences & Technologies.[5] Rati Saluja et al“Modeling and Parametric Optimization using Factorial Design Approach ofSubmerged Arc Bead Geometry for Butt Joint” International Journal of Engineering Research andApplications (IJERA) ISSN: 2248-9622 ,Vol. 2, Issue 3, May-Jun 2012, pp. 505-508 505[6] S.Kumanan et al “Determination of submerged arc welding process parameter using Taguchimethod and regression analysis” Indian Journal of engineering and material sciences, volume 14,June 2007, pp. 177-183[7] Saurav Datta et al “Multi-Objective Optimization of Submerged Arc Welding Process” TheJournal of Engineering Research Vol. 7, No. 1, (2010) 42-52[8] Ajay Bangar et al “Optimization of Welding Parameters by Regression Modeling andTaguchi Parametric Optimization Technique” International Journal of Mechanical and IndustrialEngineering (IJMIE), ISSN No.[9] Satish et al “ Weldability and Process Parameter Optimization of Dissimilar Pipe Joints UsingGTAW” International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622Vol. 2, Issue 3, May-Jun 2012, pp.2525-2530[10] Sudhakaran et al “optimization of process parameters to minimize angular distortion ingas tungsten arc welded stainless steel 202 grade plates using particle swarm optimization”Journal of Engineering Science and Technology Vol. 7, No. 2 (2012) 195 - 208[11] Parth D Patel et al “Prediction of weld strength of metal active gas (MAG) welding usingartificial neural network” International Journal of Engineering Research and Applications (IJERA)ISSN: 2248-9622 Vol. 1, Issue 1, pp.036-044[12] Aniruddha Ghosh and Somnath Chattopadhyaya, “Submerged Arc Welding ParametersEstimation Through Graphical Technique” International Journal of Mechanical Engineering &Technology (IJMET), Volume 1, Issue 1, 2010, pp. 95 - 108, ISSN Print: 0976 – 6340, ISSN Online:0976 – 6359.[13] D.Muruganandam, “Friction Stir Welding Process Parameters for Joining DissimilarAluminum Alloys” International Journal of Mechanical Engineering & Technology (IJMET),Volume 2, Issue 2, 2011, pp. 25 - 38, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. 85

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