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Comparative study on different pin geometries of tool profile in friction stir welding
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Comparative study on different pin geometries of tool profile in friction stir welding

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  • 1. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME245COMPARATIVE STUDY ON DIFFERENT PIN GEOMETRIES OFTOOL PROFILE IN FRICTION STIR WELDING USING ARTIFICIALNEURAL NETWORKSD. Kanakaraja 1, P. Hema 2, K. Ravindranath 31PG Student 2Assistant professor 3Professor Mechanical Engineering Department,Sri Venkateswara College of Engineering, S.V.University, Tirupati, Andhra pradesh- 517502,IndiaABSTRACTFriction stir welding (FSW) is an innovative solid state joining technique and hasbeen employed in aerospace, rail, automotive and marine industries for joining aluminium,magnesium, zinc and copper alloys. The FSW process parameters such as tool rotationalspeed, welding speed, axial force etc., play a major role in deciding the weld quality. Thepresent work is a comparitive study on different pin geometries of FSW tool using ANN inMATLAB. This work focuses on two methods such as Artificial neural networks andRegression analysis to predict the tensile strength of friction stir welded 6061 aluminiumalloy. An artificial neural network (ANN) model was developed for the analysis of thefriction stir welding parameters of AA6061 plates. The Tensile strength of weld joints werepredicted by taking the parameters Tool rotation speed, Weld speed and Axial force as afunction. A comparison was made between measured and predicted data. A regression modelis also developed and the values obtained for the response Tensile strengths are comparedwith measured values. The graphs were plotted between Regression predicted values andExperimental data to show the accuracy of experimental results. It was found that amongthese methods ANN model is easier and effective methodology in order to find out theperformance output and welding conditions.Key words: Friction stir welding, Aluminium alloy, Tensile Strength, Artificial neuralnetworks, Regression analysisINTERNATIONAL JOURNAL OF MECHANICAL ENGINEERINGAND TECHNOLOGY (IJMET)ISSN 0976 – 6340 (Print)ISSN 0976 – 6359 (Online)Volume 4, Issue 2, March - April (2013), pp. 245-253© IAEME: www.iaeme.com/ijmet.aspJournal Impact Factor (2013): 5.7731 (Calculated by GISI)www.jifactor.comIJMET© I A E M E
  • 2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME246I. INTRODUCTIONFriction stir welding (FSW) is a solid state method developed by the welding institute(TWI) (Thomas, 1991) and now being increasingly used in the welding of aluminum.Aluminum alloys find wide applications in aerospace, automobile industries, in ship building,in train wagons and trams, in offshore structures and in bridge constructions due to its lightweight and higher strength to weight ratio (Dawes, 1995). FSW is a innovative solid phasewelding process in which there is no melting of the material [1]. Hence, FSW is preferredover the commonly used fusion welding techniques for the advantages such as: there are novoids and cracking in the weld, there is no distortion of the work piece, no need of fillermaterials, no costly weld preparation required, no shielding gas is required during FSWprocess (Thomas et al., 1997). It is a clean and environment friendly process because thereare no harmful effects like arc formation, radiation, release of toxic gas etc. FSW is perhapsthe most remarkable and potentially useful welding technique. However, during FSW processusing inappropriate welding parameters can cause defects in the joint and deteriorate themechanical properties of the FSW joints (Cavaliere et al., 2008). Although FSW consistentlygives high quality welds, proper use of the process and control of number of parameters isneeded to achieve this. To produce the best weld quality, theses parameters have to bedetermined individually for each new component and alloy (Wei et al., 2007). The quality offriction stir welded joint is controlled by three welding parameters, these are Tool’s RotationSpeed, Welding Speed and Axial Force.In FSW a rotating tool moves along the joint interface, generating heat and resultingin a re-circulating flow of plasticized material near the tool surface [2-5]. This plasticizedmaterial is subjected to extrusion by the tool pin rotational and traverse movements leading tothe formation of the so called stir zone. The formation of the stir zone is affected by thematerial flow behavior under the action of the rotating tool. The FSW process is appliedpresently for welding aluminum and magnesium alloys as well as copper, steel, compositesand dissimilar materials [6-10]. Welding of aluminum alloy especially heat treatable wroughtaluminum alloy of AA6XXX aluminum by FSW produces better quality [11].II. PLAN OF INVESTIGATIONThis investigation was planned to be carried out in following steps (i) Identifying theimportant process parameter and finding the range of process parameter such as toolrotational speed, welding speed and Axial force. (ii) Collection of experimental data. (iii)Developing of Regression model (Developing mathematical model and checking theadequacy) and predict the Tensile strength as the function of input parameters. (iv)Developing ANN model, Training and Testing of Neural Network to predict the TensileStrength values. (v) Comparing and concluding about different pin profiles.The important processes parameters (Tool rotational speed, welding speed, Axialforce) and tool probe (pin) geometry were identified based on series of trials and author’searlier study. Parameters such a way that the friction stirred welded joint should be free fromany visible external defect. The selected process parameters with their levels are given inTable 1. The experiment was based on three factors with three levels of full factorialexperimental design. As prescribed in the Experimental design matrix twenty seven jointswere carried out using Two different probe geometry by considering three levels of processparameter, namely tool rotational speed and welding speed as given in the Table 2. It is to be
  • 3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME247further noted that the experiments were conducted with a constant tool rotational speed,welding speed and axial load for two pin profiles.Table 1: Process parameters and their levels taken for AA6061 materialLevel Tool speed(rpm)Welding speed(mm/min)Axial force(K.N)1 1200 48 1.52 1600 60 2.03 2000 72 2.5Table 2 Experimental design matrix and resultsTrialno.Coded value Real valueTensile strength(N/mm2)N S FToolspeed(rpm)Weldingspeed(mm/min)Axialforce(K.N)ForConical pinForTriangularpin1 1 1 1 1200 48 1.5 61.4 113.392 1 1 1 1200 48 2 66.7 172.473 1 1 1 1200 48 2.5 40.5 171.114 1 2 2 1200 60 1.5 58.3 187.365 1 2 2 1200 60 2 45.3 101.286 1 2 2 1200 60 2.5 38.8 168.087 1 3 3 1200 72 1.5 64.6 129.778 1 3 3 1200 72 2 65.3 44.619 1 3 3 1200 72 2.5 40.8 153.1910 2 1 2 1600 48 1.5 59.6 142.2511 2 1 2 1600 48 2 75 184.1412 2 1 2 1600 48 2.5 79.5 156.4613 2 2 3 1600 60 1.5 60.7 181.9214 2 2 3 1600 60 2 65.3 156.7715 2 2 3 1600 60 2.5 62.9 120.4316 2 3 1 1600 72 1.5 65.6 175.4917 2 3 1 1600 72 2 62.7 135.8218 2 3 1 1600 72 2.5 75.6 88.9219 3 1 3 2000 48 1.5 97.8 127.1120 3 1 3 2000 48 2 86.2 91.8221 3 1 3 2000 48 2.5 75.57 129.2722 3 2 1 2000 60 1.5 86.08 137.8623 3 2 1 2000 60 2 73.8 119.4524 3 2 1 2000 60 2.5 85.71 108.8825 3 3 2 2000 72 1.5 90.22 109.2526 3 3 2 2000 72 2 89.5 124.7627 3 3 2 2000 72 2.5 88.3 89.72
  • 4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME248III. PREDICTION OF TENSILE STRENGTHA. Development of mathematical model by Regression analysisTensile strength of the joints is the function of rotational speed, welding speed, and axialforce and it can be expressed asY = f (N, S, F) ---------(1)WhereY-The response, N- Rotational speed (rpm), S- Welding Speed (mm/s) , F - Axial Force(tones).For the three factors, the selected polynomial (regression) could be expressed asY = k+ aN + bS + cF ----------(2)Where k is the free term of the regression equation, the coefficients a, b, and c are lineartermsTable 3: Estimated regression coefficients of mathematical modelsRegressioncoefficientsTensile Strength N/mm2For Conical Pin For Triangular PinK -2.65 8.83A 0.961 -0.197B -0.014 -0.581C -0.232 -0.228MINITAB 15 Software Packages is used to calculate the values of those coefficients fordifferent responses and is presented in Table 3. After determining the coefficients, themathematical models are developed. The developed final mathematical model equations inthe coded form are given below:For Conical pin profileTensile strength = - 2.65 + 0.961 (N) - 0.014 (S) - 0.232(F) --------- (3)For Triangular pin profileTensile strength = 8.83 - 0.197 (N) - 0.581 (S) - 0.228 (F) ----------(4)The validity of regression models developed is tested by drawing scatter diagrams. Typicalscatter diagrams for all the models are presented in Figures 6 and 7. The observed values andpredicted values of the responses are scattered close to the 45° line, indicating an almostperfect fit of the developed empirical models [12]
  • 5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME249Figure 2: Scatter diagram for conical pin Figure 3: Scatter digram for Triangular pinB. Artificial Neural Network (ANN)ANNs are computational models, which replicate the function of a biologicalnetwork, composed of neurons and are used to solve complex functions in variousapplications. Neural networks consist of simple synchronous processing elements that areinspired by the biological nerve systems. The basic unit in the ANN is the neuron. Neuronsare connected to each other by links known as synapses; associated with each synapse there isa weight factor. Details on the neural network modeling approach are given in elsewhere[13].One of the most popular learning algorithms is the back-propagation (BP) algorithm. In thispresent study, BP algorithm is used with a single hidden layer improved with numericaloptimization techniques called Levenberg Marquardt (LM) [14]. The architecture of ANNused in this study is 3-20 -1, 3 corresponding to the input values, 20 to the number of hiddenlayer neurons and 1 to the output. The topology architecture of feed-forward three-layeredback propagation neural network is illustrated in Figure 4 below.Figure 4: Architecture of feed forward three layered back propagation neural network3050709011030 50 70 90RegressionpredictedT.SvaluesExperimental T.S values40608010012014016018020040 60 80 100 120 140 160 180 200RegressionpredictedT.SvaluesExperimental T.S values
  • 6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME250MATLAB 7.1 has been used for training the network model for tensile strength prediction.The training parameters used in this investigation are shown in Table.5 The neural networkdescribed in this paper, after successful training, will be used to predict the tensile strength offriction stir welded joints of 6061 aluminium alloy within the trained range.Table 4: Training parameters used in ANNNumber of input nodes 3Number of hidden nodes 20Number of output nodes 1Learning rule Levenburg marquattNumber of epochs 5000Mu 0.01IV. ANN RESULTSThe results obtained after training and testing of Artificial Neural Networks are shown in thetables belowTable 5: Test conditions at different hidden neurons (TS) for Conical pin profileToolspeed(rpm)Weldingspeed(mm/min)Axialforce(KN)Exp.ValueN/mm2Predicted Tensile Strength N/mm2@ 20N DEV @25N DEV @30N DEV @35N DEV1200 48 2.5 40.5 64.19 -0.585 65.01 -0.605 60.62 -0.497 48.08 -0.1871200 60 2.5 38.8 71.46 -0.842 42.62 -0.098 59.85 -0.543 58.08 -0.4971200 72 2.5 40.8 78.16 -0.916 55.63 -0.363 66.82 -0.638 64.95 -0.5921600 48 1.5 59.6 73.38 -0.231 85.22 -0.430 72.85 -0.222 64.38 -0.0801600 60 1.5 60.7 72.84 -0.200 69.09 -0.138 65.25 -0.075 64.94 -0.0702000 48 2.5 75.57 86.58 -0.146 89.72 -0.187 87.91 -0.163 78.15 -0.0342000 60 2 73.8 89.02 -0.206 89.09 -0.207 87.66 -0.188 86.15 -0.167AVERAGES 55.68 76.51 -0.447 70.91 -0.290 71.56 -0.332 66.39
  • 7. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME251Table 6: Test conditions at different hidden neurons (TS) for Triangular pin profileComparison of Conical pin and Triangular pin profile toolsFigure 5: Conical pin profile vs Triangular pin profile experimental T.S values(N/mm2)From the above plot it is observed that Triangular pin profile tool is better than conical pinprofile tool which yields more (2 times) Tensile strength.Toolspeed(rpm)Weldingspeed(mm/min)Axialforce(KN)Exp.ValueN/mm2Predicted Tensile Strength N/mm2@ 20N DEV @25N DEV @30N DEV @35N DEV1200 60 2 101.28 174.76 -0.726 170.96 -0.688 152.03 -0.501 140.22 -0.3841200 72 2 44.61 141.48 -2.171 143.12 -2.208 143.03 -2.206 125.42 -1.8111600 60 2.5 120.43 155.83 -0.294 119.1 0.011 138.34 -0.149 131.54 -0.0921600 72 2.5 88.92 134.76 -0.516 173.64 -0.953 134.74 -0.515 90.83 -0.0212000 48 2 91.82 125.23 -0.364 137.34 -0.496 124.31 -0.354 119.23 -0.2992000 60 2.5 108.88 124.91 -0.147 119.6 -0.098 126.25 -0.160 115.57 -0.0612000 72 2.5 89.72 126.26 -0.407 118.49 -0.321 131.78 -0.469 118.68 -0.323AVERAGES 92.23 140.46 -0.661 140.32 -0.679 135.78 -0.622 120.21
  • 8. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME252VI CONCLUSIONSThis paper has describes two models for Predicting the Tensile strength of friction stirwelded AA6061 aluminium alloy using Regression analysis and Artificial NeuralNetwork(ANN). From this investigation, the following important conclusions are derived.1) a regression model is developed and the values obtained for the response strengths arecompared with measured values. it shows that the models are adequate without anyviolation of independence or constant assumption.2) ANN model has been developed for prediction of strength as a function of weldingparameters. The model has been proved to be successful in terms of agreement withexperimental results. The proposed model can be used in optimization of welding processfor efficient and economic production by forecasting the strength in welding operations.3) The results that are obtained for the response Tensile strength in ANN model andRegression analysis are compared with experimental data. Among these methods ANNmodel is easier and effective methodology in order to find out the performance output andwelding conditions.4) Finally it is observed that Triangular pin profile is better than Conical pin profile becausethe obtained tensile strength values are higher for Friction stir welded joint.REFERENCES1. Ratnesh K. Shukla and Pravin K. Shah, “Comparative study of friction stir welding andtungsten inert gas welding process” Indian Journal of Science and Technology Vol. 3No. 6 (June 2010) ISSN: 0974- 68462. R. Nandan, T. DebRoy and H. K. D. H. Bhadeshia, “Recent advances in friction stirwelding–process, weldment structure and properties”, Progress in Materials Science,vol.53, 980-1023, (2008)3. W. M. Thomas, E. D. Nicholas, J. C. Needham, M. G. Murch, P. Temple- Smith and C.J. Dawes, “Friction stir butt welding”, International Patent Application No. PCT/GB92/02203, December 1991.4. C. J. Dawes and W. M. Thomas, “Friction stir process welds aluminum alloys”, WeldingJournal 75, 41-44, (1996).5. Jae-Hyung Cho, E. Donald, Boyce and Paul R. Dawson, “Modeling strain hardening andtexture evolution in friction stir welding of stainless steel”, Material Science EngineeringA, 398, 146-163. (2005).6. Kalemba, S. Dymek, C. Hamilton and M. Blicharski, “Microstructural investigation offriction stir welded 7136-T76511 aluminium”, Proceedings of the 13th InternationalConference on Electron Microscopy,’EM2008’, Zakopane, 79 , (2008).7. H. Uzun, C.D. Donne, A. Argagnotto, T. Ghidini and C. Gambaro, “Friction stir weldingof dissimilar Al 6013-T4 to X5CrNi18-10 stainless steel”, Materials and Design, 26, 41-46, (2005).8. P. Cavaliere, R. Nobile, F. W. Panella and A. Squillance, “Mechanical andmicrostructural behavior of 2024-7075 aluminium alloy sheets joined by friction stirwelding”, International Journal of Machine Tools and Manufacture, 46, 588-594, (2006).
  • 9. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME2539. L. Litynska, R. Braun, G. Staniek, C. Dalle Donne and J. Dutkiewicz, “TEM study of themicrostructure evolution in a friction stir-welded Al Cu Mg Ag alloy”, MaterialsChemistry and Physics, 81, 293-295, (2003).10. C. Yeni, S. Sayer, O. Ertugrul and M. Pakdil, “Effect of post-weld aging on themechanical stir and microstructural properties of friction welded aluminium alloy 7075”,Archives of Materials Science and Engineering, 34, 105-109, (2008).11. C. Hamilton, S. Dymek and M. Blicharski, “Mechanical properties of al 6101-T6 weldsby friction stir welding and metal inert gas welding”, Archives of Metallurgy andMaterials, 52, 67-72, (2007).12. R. Palanivel, P. Koshy Mathews and N. Murugan “Development of mathematical modelto predict the mechanical properties of friction stir welded AA6351 aluminum alloy”Journal of Engineering Science and Technology Review 4 (1) (2011) 25-31.13. ZHANG Z, FRIEDRICH K. Artificial neural networks applied to polymer composites:A review [J]. Composites Science and Technology, 2003, 63: 2029−2044.14. ARCAKHOGLU E, CAVUSOGLU A, ERISEN A. Thermodynamic analyses ofrefrigerant mixtures using artificial neural networks [J]. Applied Energy, 2004, 78:219−230.15. D.Muruganandam and Dr.Sushil lal Das, “Friction Stir Welding Process Parameters forJoining Dissimilar Aluminum Alloys”, International Journal of Mechanical Engineering& Technology (IJMET), Volume 2, Issue 2, 2011, pp. 25 - 38, ISSN Print: 0976 – 6340,ISSN Online: 0976 – 6359.16. U.S.Patil and M.S.Kadam, “Effect of the Welding Process Parameter in MMAW forJoining of Dissimilar Metals and Parameter Optimization using Artificial Neural FuzzyInterface System”, International Journal of Mechanical Engineering & Technology(IJMET), Volume 4, Issue 2, 2013, pp. 79 - 85, ISSN Print: 0976 – 6340, ISSN Online:0976 – 6359.