Prediction of tensile strength and microstructure characterization of immersed friction stir welding of aluminium alloy AA2014-T4

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1 Indian Journal of Engineering & Materials Sciences Vol. 22, April 2015, pp Prediction of tensile strength and microstructure characterization of immersed friction stir welding of aluminium alloy AA2014-T4 N D Ghetiya* & K M Patel Department of Mechanical Engineering, Institute of Technology, Nirma University, Ahmedabad , India Received 21 May 2014; accepted 18 September 2014 Immersed friction stir welding is one of the variants of friction stir welding (FSW) in which work piece is completely submerged in the cooling medium. Immersed FSW of aluminium alloy AA 2014-T4 is carried out using tool shoulder diameter, rotational speed and welding speed as typical process parameters. The immersed condition of heat treatable alloy while FSW avoids the dissolution of strengthening precipitates normally observed in air FSW. The response surface methodology was used to develop the second order mathematical model for the tensile strength. Box-Behnken experiment design is selected for finding out the relationship between a tensile strength and welding parameters. The analysis of variance (ANOVA) is used to test the significance of fit of the equation. Confirmation experiments are carried out to validate the study and it is found that predicted results are in good agreement with experimental findings. Microstructure analysis of weld joint is also carried out to know the intrinsic reasons for the strength improvement in immersed FSW. The result indicates that tensile strength of weld joint obtained using immersed FSW is 307 MPa which is 75% of the base metal strength. Keywords: Immersed FSW, Tensile strength, Strengthening precipitates, Response surface methodology, Genetic algorithm Friction stir welding (FSW) is a solid state joining process in which the material is forged instead of melting and recasting. The process was initially invented at The Welding Institute (TWI, UK) in Materials which previously thought to be non weldable like aluminium alloys 2-4, because of melting related defect can be weld very easily. FSW process has several advantages over fusion welding process. The heat treatable aluminium alloy of 2xxx series find wide spread use in aircraft and aerospace engineering due to high strength to weight ratio 5-7. In FSW of heat treatable aluminium alloys, although material does not melt, its mechanical characteristics deteriorates through coarsening or dissolution of strengthening precipitates 8,9. Krasnowski et al. 10,11 studied the influence of shape and weld configuration on the microstructure and mechanical properties of Al6082. It was observed that best tensile strength was obtained in the one sided joint produced using FSW by conventional and triflute tool. Two sided welded joint produced poor mechanical properties due to high heat transfer to the material during second pass. Higher fatigue strength was achieved in two sided butt welded joint. The recent developments in FSW *Corresponding author ( nghetiya@gmail.com) focused on immersed FSW to improve the tensile strength and mechanical properties by controlling temperature during welding and by obtaining fine grain microstructure. Several researchers reported that using cooling medium during friction stir welding can generate fine grain structure with good mechanical properties. Fratini et al. 12,13 performed FSW of AA7075-T6 aluminum alloy with a flow of water on the workpiece while welding. The tensile strength of the joint was found to be higher than that obtained in normal air condition. Liu et al. 14 used immersed FSW for AA2219-T6 aluminum alloy for strength improvement purpose. Reduction in coarsening and dissolution of strengthening precipitates were found responsible for the tensile strength improvement of the joint. Hofmann and Vecchio 15 demonstrated the use of submerged friction stir processing as an alternative and improved method for creating ultrafine-grained bulk materials through severe plastic deformation. Zhang et al. 16 studied joint performance of AA2219 alloys welded in immersed condition by varying the welding speed and rotational speed. They concluded that tensile strength of the joint increase but plasticity is deteriorated. The joint fracture pattern changed compared to the normal air FSW.

2 134 INDIAN J. ENG. MATER. SCI., APRIL 2015 Rui et al. 17 studied the temperature distribution and mechanical properties of submerged friction stir welded AA7050 alloy joint. The welding was carried out in air, cold water (8 C) and hot water (90 C) condition. These researchers concluded that controlling weld temperature distribution improves the ultimate tensile strength of the weld joint. Zhang et al. 18 conducted thermal modelling of immersed FSW of high strength aluminium alloy using three dimensional heat transfer model. The boundary conditions of immersed FSW were fixed considering the vaporising characteristics of water at FSW tool. Zhang and Liu 19 developed mathematical model for tensile strength in immersed FSW using AA 2219-T6 and optimization of process parameters was carried out. Previous investigators highlighted the advantages of various external cooling medium for strength improvements in FSW. No attempt was made for development of mathematical model of heat treatable alloy AA2014-T4 in immersed condition. Looking to the wide spread applications of heat treatable AA2014-T4 alloy in industry, it would be beneficial to develop mathematical model for immersed FSW of AA2014-T4. This paper presents performance evaluation of the FSW of AA2014-T4 in immersed condition by performing experiments with important process parameters namely shoulder diameter, rotational speed and welding speed. Microstructural investigation was carried out to know the effect of cooling medium on microstructure changes which leads to improvement in the tensile strength. were fabricated using high carbon high chromium H-13 steel with various shoulder diameters. Conical threaded pin tool having mean diameter 5 mm, 0.8 mm pitch and 2.8 mm pin length was used throughout the experimentation. The aluminium alloy AA2014-T4 sheet having thickness 3 mm was cut into required size work piece by abrasive water jet cutting and square butt joint configuration (300 mm 300 mm) was prepared to fabricate FSW joints. Joints were fabricated by single pass welding and welding direction was normal to the rolling direction. While experimentation, a zero tilt angle and plunge depth of 0.3 mm was applied to welding tool. The temperature data for immersed FSW welding was collected using K-type thermocouples and Agilent make data logger. The thermocouples were placed at the bottom of plate and on advancing side with respect to weld direction. The tip of the thermocouples were in immersed water FSW located at 5 mm, 8 mm, 11 mm and 14 mm away from the weld seam line in width direction. The thermocouples could not be placed at the stirring zone because of the rotating pin and metal. Therefore, thermocouples were located in thermo mechanical affected zone and the heat affected zone of the welded workpiece as shown in Fig. 1. The temperature data thus obtained was used to understand the microstructural changes in immersed FSW. Trial experiments were conducted to determine the working range of the process parameters. Feasible limits of the parameters were chosen in such a way that the friction stir welded joints should be free from any visible Experimental Procedure Friction stir welding experiments were conducted using conventional milling machine. An indigenously designed fixture fabricated from mild steel and acrylic was used to hold the workpiece and water during welding. The chemical composition and mechanical properties of workpiece are presented in Tables 1 and 2, respectively. The chemical composition data is based on spectrograph results. Non-consumable tools Table 1 Chemical composition wt% of AA2014-T4 aluminium alloy Cu Si Mg Mn Fe Zn Ti Cr Al Fig. 1 Thermocouple placement at bottom of welded plate Tensile strengh MPa Density kg/m 3 Table 2 Properties of AA2014-T4 aluminium alloy Thermal conductivity W/mK Melting point ºC Hardness Vickers VHN Specific heat J/kg ºC

3 GHETIYA & PATEL: IMMERSED FRICTION STIR WELDING 135 Process parameters external defects. Process parameters and their working ranges are presented in Table 3. Three tensile specimens were prepared for each experiment as per the ASTM E8M-04. Tensile strength of the FSW joints were measured by conducting test on universal testing machine and the average of 3 results is presented in Table 4. Optical and SEM images were used for the detail microstructure study. Chemical etching was performed using Keller s reagent made up of a mixture of 2.5 ml nitric acid, 1.5 ml hydrochloric acid, 1 ml hydrofluoric acid and 95 ml water. Vickers microhardness measurements were carried out across the weldment by applying a load of 9.81 N for 10 s along the straight line. The fracture feature of the sample was analysed by scanning electron microscopy to study fracture behaviour after tensile strength test. In this study, the three levels and three factorial Box-Behnken experiment design, was chosen for finding out the relationship between response (tensile strength) and variables (welding parameters). The process parameters with levels are summarized in Table 3. Total 15 experiments were conducted with 3 levels to study the influence of the parameters on tensile strength as shown in Table 4. Expression for the quadratic response Y is described in the form of following equation 22,23 0 k k 2 i= 1 i i i= 1 ii i i< j ij i j Y = β + Σ β X + Σ β X + ΣΣ β X X + ε (1) Where Y is process yield, while X i are the factors or variables. The expression contains linear terms in X i, quadratic terms in X i 2 and product terms in X i X j. The terms β i, β ii and β ij are constant coefficients and ε is the random error. Method of least squares was used to determine the constant coefficients. Equation (1) represents the response surface therefore these designs are also called as response surface designs. In this technique, main objective is to optimize the response surface that is influenced by various process parameters. Response surface methodology, also quantifies the relationship between controllable input Table 3 Welding parameters and their levels Levels Welding parameter Symbols Low(-1) Middle(0) High(+1) Tool shoulder diameter (mm) D Rotational speed (rpm) N Welding speed (mm/min) V Exp. No. Table 4 Design matrix and experiment result Welding parameters N (rpm) V(mm/min) D (mm) Tensile strength TS (MPa) parameters and the obtained response surface. In the present study, the response, i.e., tensile strength (TS), is a function of rotation speed (N), welding speed (V) and shoulder diameter (D), and therefore the equation can be expressed as: TS = b 0 +b 1 N +b 2 V +b 3 D +b 12 NV +b 13 ND+b 23 VD +b 11 N 2 +B 22 V 2 +B 33 D 2 (2) By applying multiple regression analysis on the design matrix and the response values, the following second-order polynomial equation is established: Tensile strength(ts)= (N) (V) (D) (N) (V) (N)(D) (V) (D) (N) (V) (D) (3) The analysis of variance was used to test the significance of fit of the equation and same is presented in Table 5. The model gives the F value of which implies that the model adequately represents the relationship between the response and the variables. Model terms D, NV, ND, N 2, V 2 are significant as their p values are less than It is

4 136 INDIAN J. ENG. MATER. SCI., APRIL 2015 Table 5 ANOVA for second order polynomial equation Source Sum of square df Mean square F value P-value prob>f Remark Model significant A-N B-V C-D AB AC BC A B C Residual Lack of fit not significant Pure error Cor. total R-squared Adj R-squared also evident from the table that Lack of Fit is not significant. Goodness of the fit for the model is represented by determination coefficient R 2. The model presents a determination coefficient (R 2 ) of 0.97 and an adjusted determination coefficient (adjusted R 2 ) of 0.93 which indicates a high correlation between experimental and predicted results. Developed mathematical model can be used to predict the tensile strength of the joint at any combination of parameters in the present working ranges. Response surface equation was derived from quadratic regression fit; conformation tests must be performed to verify their validity. For checking the adequacy of the model developed, four confirmation experiments were performed as shown in Table 6. The test conditions for first two confirmation experiments were among the welding conditions those have been part of design of experiment planned earlier using design matrix. The remaining two confirmation experiments were within the range of the levels defined for the various parameters. The predicted values and the actual experimental values are presented and compared in Table 6. From the analysis of Table 6, it can be observed that the calculated errors are small for various sets of experiment. The errors between experimental and predicted values for tensile strength lie within -1.83% to 3.23% which indicates high correlation between experimental values and predicted values and also confirms excellent reproducibility of the experimental conclusions. Sr. No. Table 6 Results of confirmation experiments FSW Parameters Tensile Strength (MPa) N (rpm) V(mm/min) D (mm) Exp. Pre. % Error Optimization of Tensile Strength using Genetic Algorithm Optimization of the FSW process increases the utility of the welding economics and product quality. Genetic algorithm was used to get the best possible tensile strength within the constraints. MATLAB R2012b was used for the GA development. This approach makes a binary coding system to represent the variables tool shoulder diameter (D), rotational speed (N) and welding speed (V) that is each of these variables is represented by a ten bit binary equivalent, limiting the total string length to forty. It is known as chromosome. The variables are represented as genes (substring) in the chromosome. The randomly generated 100 such chromosome (population size is 100), fulfilling the constraints on the variables, are taken in each generation. The first generation is called initial population. Once the coding of the variables has been done, then the actual decoded values for the variables are estimated. The GA program uses different type of crossover and mutation operators to predict maximum values of tensile strength. Here, the

5 GHETIYA & PATEL: IMMERSED FRICTION STIR WELDING 137 second-order mathematical model acts as objective function in order to maximize the output tensile strength factor. The same coding was followed for input parameters tool shoulder diameter (D), rotational speed (N), welding speed (V) and tensile strength (TS) as in the RSM formulation. For optimization of present problem using GA, the following parameters were specified. Population size = 100 Maximum number of generation = 500 Total string length = 40 Crossover probability = 0.8, Mutation probability = 0.01 Elite count = 10 Maximize MRR using the model Tensile strength (TS) = (N) (V) (D) x10-3 (N) (V) (N)(D) (V) (D) x 10-4 (N) (V) (D) 2 (4) Subject to 14 mm D 18 mm (D = Tool shoulder diameter) 710 rpm N 1400 rpm (N = Rotational speed) 80 mm/min S 125 mm/min (V= Welding speed) The GA gives the maximum tensile strength of MPa which was obtained at welding parameters, rotation speed of rpm, welding speed of mm/min and shoulder diameter of 18 mm. model. Figure 2(a) shows the relationship between welding speed and tensile strength. It is evident from the graph that with an increase in welding speed at given rotational speed, initially increases the tensile strength of the welded joint to a maximum value and tensile strength decreases afterward. Poor tensile strength is obtained at lower welding speed because of higher heat input, which leads to more flash formation and improper metal consolidation at the advancing side, same is visible in Fig. 3. Due to higher heat input, precipitates may dissolve at higher temperature and reduces the strength of the joint 16. An increase in the welding speed up to 100 mm/min reduces the heat input and flash which results into proper consolidation of the stirred materials at trailing zone. At high welding speed, leads to less heat input at the joint and reduce the material movement which produces the internal defects same is visible in Fig. 4. Results and Discussion FSW process is widely used for the joining of different aluminium alloys since its invention. Many studies are carried out to understand the effect of various welding parameters on the tensile strength in air FSW, but limited wok has been reported in the area of FSW of aluminium alloys in immersed condition. In this work, attempt has been made to understand the effect of various weld parameters on tensile strength by elaborating microstructure changes occurred and same is presented in the following paragraphs. Effects of welding parameters on tensile strength Influence of the welding parameters on tensile strength in immersed FSW is shown in Fig. 2 using 3D response graphs formed based on developed Fig. 2 Effect of immersed FSW parameters on tensile strength

6 138 INDIAN J. ENG. MATER. SCI., APRIL 2015 Fig. 3 Pictures of crown appearance of welded joint with different welding conditions in immersed FSW using 16 mm shoulder diameter and 80 mm/min welding speed (a) 710 rpm and (b) 1400 rpm Fig. 4 Macrograph of welded joint with different welding condition in immersed FSW using 16 mm shoulder diameter (a) 710 rpm, 125 mm/min welding speed and (b) 1400 rpm, 80 mm/min welding speed Less heat input may retains more precipitates in the nugget and TMAZ region but because of internal defect component fails in that region. This may be the reason that welding speed close to 100 mm/min gives higher tensile strength. Figure 2(a) and (b) shows the tensile strength of the joint as a function of rotational speed in immersed FSW. Tensile strength of joint increases with an increase in rotational speed which reaches its maximum value and then start decreasing. It is due to an increase in the heat input which leads to the good consolidation and material movement in the friction stir welded region. Lower tensile strength is observed at lower rotational speed, this may be due to the presence of pin holes and crack in weld regions as shown in Fig. 4(a). Similarly at higher rotational speed, tensile strength reduces again. This may be due to increased turbulence of semisolid metal which reduces the forging action and consolidation of the material at the trailing zone of the friction stir welded region. Turbulence flow causes microstructure discontinuities which finally cause the formation of the void defect. It is clear from the response surface graphs that tensile strength is less sensitive to rotation speed and shoulder diameter and more sensitive to welding speed. The change in the value of tensile strength with variation in diameter is insignificant and same can be seen from the Fig. 2 (b) and (c). Tensile strength improvement and microstructural changes in immersed FSW Immersed FSW gives rise to noticeable microstructural changes. Figures 5(a) and (b) show Fig. 5 Optical micrographs of joint welded at 1000 rpm and 100 mm/min using 16 mm shoulder diameter (a) BM, (b) NZ, (c) NZ and TMAZ, and (d) HAZ Fig. 6 SEM images of secondary precipitates found in joint welded at 1000 rpm and 100 mm/min using16 mm shoulder diameter (a) BM, (b) NZ, (c) TMAZ and (d) HAZ typical microstructure of base metal (BM) and nugget zone (NZ) of immersed FSW joint. The main difference between the microstructures of BM and NZ is grain size and precipitates. Large grains of nonuniform size are observed in the microstructure of BM, whereas grain size in the NZ is remarkably small. This can be attributed to higher plastic deformation in the rotating pin region and higher heat conduction by the water cooling in immersed FSW. Presence of precipitates in BM and NZ is clearly visible in SEM images as shown in Fig. 6(a) and (b). The density of the precipitates is less in the NZ in comparison to the BM. This may be because of the different peak temperatures experienced in BM and NZ during FSW in immersed condition as shown in

7 GHETIYA & PATEL: IMMERSED FRICTION STIR WELDING 139 Fig. 7. The second-phase particles get fragmented into smaller particles because of severe plastic deformation in the NZ. Figure 5(c) shows microstructure at the interface between NZ and thermomechanical affected zone (TMAZ). The fracture tests of welded joint under tensile loading were performed to find out the location of fracture. It should be noted that the joint was fractured on advancing side and at the interface between the NZ and TMAZ in immersed FSW. Interface between NZ and TMAZ is the weakest portion of the joint, this may be due to abrupt microstructural change occurs at the interface. Formation of softer region at the interface between NZ and TMAZ in the immersed condition may be due to effect of friction heat. Similar behaviour reported by Liu et al. 24 in their work while FSW of AA 2017-T35 in air. Figures 5(d) and 6(d) show the microstructures of HAZ of AA2014-T4 friction stir welded in immersed condition at rotational speed 100 rpm and at 100 mm/min welding speed using 16 mm shoulder diameter. It is evident from Fig. 5(a) and (d) that grain size and density difference are more or less same in BM as well as HAZ regions. This may be due to small temperature variation in these two regions in the presence of water. Many researchers observed positive effect of cooling medium during immersed FSW on tensile property. The tensile property of welded joint depends on microhardness when the joint is defect free. Microhardness measurement was carried out to check the strength improvements and same is shown in Fig. 8. It is observed that microhardness at the nugget zone is lower than base metal but higher than that of the lowest microhardness value of TMAZ. This may be the reason for strength improvement in the NZ of the immersed FSW. Peak temperature and cooling rate experienced during welding are important factors that determine the microstructure and mechanical properties of the welded joint. The peak temperature is below 250ºC which prevents the dissolution of the strengthening precipitates in heat treatable alloy AA2014. The dynamic recrystallization is taking place at the NZ because of the severe plastic deformation disperses strengthening precipitates CuMgAl 2 properly in the nugget zone improves the tensile strength and same can be seen in Fig. 6. The weld joint was prepared at optimal condition obtained earlier and same was used to study the fracture feature. The SEM image of fracture feature of optimal immersed FSW joint is shown in Fig. 9. It shows varying size and fine dimples with some void which indicates the brittle fracture at the interface between NZ and TMAZ. Fig. 8 Vickers microhardness profile of welded joint prepared using immersed FSW (joint welded at 1000 rpm and 100 mm/min using16 mm shoulder diameter) Fig. 7 Peak temperature data at bottom of plate in width direction at various locations (joint welded at 1000 rpm and 100 mm/min using 16 mm shoulder diameter) Fig. 9 Fracture feature obtained at optimal weld condition (joint welded at 1000 rpm and 100 mm/min using 16 mm shoulder diameter

8 140 INDIAN J. ENG. MATER. SCI., APRIL 2015 Conclusions The following conclusions can be drawn from this study: (i) A mathematical model was developed to predict the tensile strength of immersed friction stir welded AA2014-T4 aluminium alloy. Among the three investigated parameters, the welding speed has predominant effect on the tensile strength. Confirmation experiments were carried out and the errors between experimental and predicted values for tensile strength lie within -1.83% to 3.23%. The developed relationships can be used to predict the tensile strength in immersed FSW of AA2014-T4 aluminium alloy joints within the range of parameters. (ii) Detailed microstructural analysis of weld joint was carried out to know reason for strength improvement in immersed FSW. It may be due to formation of fine grains in NZ and microstructure improvement at TMAZ. Intense heat and severe plastic deformation followed by water cooling were responsible for fine grain formation. (iii) Fracture feature of weld joint obtained at optimal weld condition was studied. Joint was fractured at advancing side and at the interface between NZ and TMAZ. Fracture pattern shows varying size and fine dimples with some void which indicates the brittle fracture at the interface between NZ and TMAZ. Acknowledgement The authors would like to appreciate financial supports from Science and Engineering Research Board (SERB) of Department of Science and Technology (DST), New Delhi, India, SR/S3/MERC/0108/2012. References 1 Thomas W M, Nicholas E D, Needham J C, Murch M G, Templeshmith P & Dawes C J, G.B. Pat Appl No Kumar K, Kalyan C, Kailas S V & Srivatsan T S, Mater Manuf Process, 24 (2009) Sinclair P C, Longhurst W R, Cox C D, Lammlein D H, Strauss A M & Cook G E, Mater Manuf Process, 25 (2010) Suresha C N, Raaprakash B M & Upadhya S A, Mater Manuf Process, 26 (2011) Oosterkamp A, Djapic O & Nordeide A, Weld J, 255 (2004) Dawes, C J &Thomas W M, Weld J, 75 (1996) Threadgill, P, Leonard A J, Shercliff, H R & Withers P J, Int Mater Rev, 54 (2009) Ahmed M M Z, Wynne B P, Rainforth W M & Threadgill P L, Mater Charact, 64 (2012) Liang X P, Li H Z, Li Z, Hong T, Bing M & Liu S D, Mater Des, 35 (2012) Krasnowski K, Dymek S & Hamilton C, Arch Civil Mech Eng, DOI: /j.acme , Krasnowski K & Dymek S, J Mater Eng Perform, 22(12) (2013) Fratini L, Buffa G & Shivpuri, R, J Adv Manuf Technol, 43 (2009) Fratini L, Buffa G & Shivpuri R, Acta Mater, 58(2010) Liu H J, Zhang H J, Huang Y X &Yu L, Trans Nonferrous Met Soc Chin, 20(8) (2010) Hofmann, D C & Vecchio K S, Mater Sci Eng A,402(2005) Zhang H J, Liu H J &Yu L, Mater Des, 32(2011) Fu R D, Sun, Z Q, Sun R C, Li Y, Liu H J & Liu L, Mater Des, 32(2011) Zhang H J, Liu H J & Yu L,Trans Nonferrous Met Soc Chin, 23(2013) Zhang H J & Liu H J, Mater Des, 45(2013) Hosseini, M& Manesh H D, Mater Des, 31(2013) Liu H J,& Feng X L, Mater Des, 47(2013) Khuri A & Cornell J, Response surfaces, design and analysis, (Marcel Dekker Inc, New York, USA), Myres R & Montgomery D, Response surface methodology: process and product optimization using designed experiments, (Willey, India) Liu, H J, Fujii H, Maeda M & Nogi K, J Mater Process Technol, 142 (2003)