1

ISME Journal of Manufacturing Sciences

Vol. 09, No. 01, 2020 pp. 01-07

Spin-Arc GMA Welding of AA5083 Alloy

Poonguzhali V1, Deepan Bharathi Kannan T2, Umar M1 and Sathiya P1*

1Department of Production Engineering, National Institute of Technology, Trichy, Tamilnadu, India-620015.

2Department of Mechanical Engineering, SRM Institute of Science and Technology, Kattankulathur, Tamilnadu, India-603203.

*Corresponding Author: psathiya@nitt.edu; Tel.: +914312503510; Fax: +914312500133

Abstract Present work aims on obtaining optimized parameter combination in spin arc welding of Al5083. Welding trials were carried out based on taguchi L9 array with welding current, filler spinning speed, filler spin diameter as input variables. Weld quality was analyzed by measuring microhardness, corrosion resistance (width of passive region), and creep resistance (steady state creep rate). TOPSIS was used to arrive at optimized set of input variables. The most influencing welding parameters on the multiobjective function were identified through ANOVA. From TOPSIS analysis the optimized parameters are welding current: 120 A, filler spinning speed: 1250 rpm and filler spin diameter: 2 mm. Keywords: Al 5083; Spin arc Welding process; TOPSIS; ANOVA.

1. Introduction New materials are being continuously developed to meet out the demands of marine, automobile, aerospace industries. Aluminum alloys plays a crucial role in the above said industries for the fabrication of important components. Among the various aluminum alloys, Al 5083 is widely preferred in the marine and automobile industries because of its improved corrosion resistance, high specific strength and good formability [1,2]. Al 5083 is a non-heat treatable alloy and it is strengthened by work hardening process. Joining plays a crucial part in the fabrication of components involving Al 5083. Welding of Al 5083 is little tedious owing to the formation of hot cracks and porosities [3]. In addition to these, the work hardened effect get hugely affected in heat affected zone (HAZ) owing to the thermal cycle associated with the welding process [4]. Till date, joining of Al 5083 is mostly explored by conventional Tungsten inert gas (TIG), Metal inert gas (MIG), Cold metal transfer (CMT) arc welding and laser welding process. Kumar singh s et al. [5] investigated the TIG welding of Al 5083 and the authors understood that the weld’s tensile strength was maximum at welding current of 134 A. Weld strength was comparatively lesser than the base metal. Umar m et al. [6] studied the effect of peak current duration in TIG welding of Al 5083. The authors obtained very good corrosion resistance and creep resistance when the peak current duration increased from 40 % to 60 % owing to the formation of favorable Intermetallics and alloying elements. The formation of Intermetallics and dislocation plays a significant part in controlling the weld properties. Umar m el al. [7] Studied the influence of melting current duration on the Intermetallics in pulsed TIG welding of Al 5083. The authors concluded that the used of shorter melting current duration (40 %) resulted in the β phase precipitation at the grain boundary, whereas at higher melting current

2

duration (60 %), β phase precipitation was not seen in the weld. Solid state welding processes are also explored in joining Al 5083 alloys. The authors observed Corrosion properties of the weld was comparatively similar to that of base metal but observed significant reduction in the mechanical properties. Heirani F et al. [8] compared water cooled and air cooled environments in friction stir welding of Al 5083. The authors observed that the water cooled environment produced weld with better tensile properties owing to the presence of fine grains. Welding of thick plates in a single pass using conventional arc welding process is little difficult. In order to increase the production, other alternate options have to be explored. Bunaziv I et al. [9] explored the welding of 5 mm Al 5083 plates using laser- MIG hybrid welding process. The authors observed the formation of severe porosities in the weld, when the two power sources are separated by small distance. The authors also recommended to use trailing torch arrangement for minimizing the formation of porosities. Spin arc welding process is one of the recently developed process where the filler wire is made to rotate between the plates that are being joined. Spin arc welding results in the joining of thicker plates in a single pass and even with a less amount of heat input [10]. Spin arc welding is least explored in joining of Al 5083. Successful application of any new process on a specific material depends on the identification of correct combination of parameters. Identification of optimized parameters through trial error method is not recommended owing to higher cost and increased time consumption. One of the other ideal option is the application of multiobjective optimization techniques. GRA, TOPSIS, Genetic algorithm, Particle swarm optimization are some of the recently developed and widely used multiobjective optimization techniques in the manufacturing sectors. Among the various techniques, TOPSIS is widely preferred by the research community owing to its simple approach and better prediction accuracy. Mahidhar V et el. [11] studied the laser welding of Hastelloy C-276 and successfully identified the optimized parameter combination using TOPSIS multiobjective optimization technique. Similarly, Sampreet K R et al. [12] used TOPSIS for identification of optimized parameters in laser welding of Titanium alloys. Srinivasan L et al. [13] compared GRA and TOPSIS in predicting the optimized parameters in TIG welding of 15CDV6 aerospace material. The authors found that the results of both GRA and TOPSIS were different. From the above literatures, it is understood that welding of Al 5083 plays a key role in fabrication of components in marine and automobile industries. It is also understood that conventional arc welding is not suitable for welding thicker plates and hence advanced processes such as spin arc welding has to be explored. There are very few literatures related to spin arc welding of Al 5083. It is also understood that TOPSIS is suitable for identifying the optimized parameters in any manufacturing processes. Hence in this work, an attempt is made to use spin arc welding for joining Al 5083 and the optimized parameter combination is identified with the help of TOPSIS technique.

2. Experimental Details 3 mm thickness Al 5083 was welded in butt joint position using spin arc welding process. ER5356 grade filler wire having a diameter of 1.2 mm was used for joining the Al 5083 plates. The chemical composition of the base metal and filler is shown in Table 1.

Table 1.The Elemental composition of the base and filler metal (% wt.) Elements Cu Si Fe Mg Mn Cr Ni Ti Zn Al

AA5089-H111 0.02 0.12 0.4 4.57 0.94 0.06 0.01 0.027 0.02 Bal. ER5356 - - 5 0.12 0.12 - 0.12 - Bal. -

Experiments were carried out based on L9 Taguchi array. The input parameters along with their ranges are presented in Table 2

Table 2.Input Parameters and their levels Factors / Levels

Level I Level II Level III

Welding current (Amps) 120 130 140

Filler spinning speed (rpm) 1050 1250 1450

Filler spin diameter (mm) 1 2 3

3

Shielding gas flow rate, wire feed rate, travel speed, stick out distance, polarity, arc gap and filler diameter were maintained at a constant value and their values are 15 lpm, 8.3 mm/min, 10 mm/sec, 22 mm, electrode positive, 2 mm and 1.2 mm respectively. L9 Taguchi array along with the input and output parameters are shown in Table 3 Table 3. L9 Taguchi array with input and output parameters Experiment

No Welding current (Amps)

Filler spinning

speed (rpm)

Filler spin diameter

(mm)

Microhardness (Hv)

Width of passive region (mV)

Steady state creep

rate (S-1)

1 120 1050 1 74.8 361 3.81 x 10-7 2 120 1250 2 72.3 352 4.11 x 10-7

3 120 1450 3 70.33 304 4.15 x 10-7 4 130 1050 2 77.23 381 2.89 x 10-7

5 130 1250 3 72.13 321 4.24 x 10-7 6 130 1450 1 68.9 309 3.65 x 10-7 7 140 1050 3 73.1 313 3.6 x 10-7 8 140 1250 1 64.55 284 4.45 x 10-7 9 140 1450 2 74.3 328 3.76 x 10-7

The quality of the weld was assessed based on microhardness, corrosion resistance and steady state creep rate. Microhardness was measured based on the standard ASTM E8 by applying a load of 500 gf for a dwell period of 10 seconds. Corrosion resistance of the weld sample was measured by using IVIUM Electrochemical System. 3.5 % Nacl solution was used for conducting the corrosion test. Potentiodynamic polarization measurement was used to determine the corrosion data in terms of width of the passive region (∆Epit) given in equation 1:

∆Epit = Epit - Ecorr (mV) (1) Where Epit is the Pitting potential and Ecorr is the Corrosion potential. The steady state creep rate was measured by using the impression creep testing machine. The analysis was performed by applying a constant load of 173 MPa for 3600 s at a temperature of 473 K. Poulton’s solution was used as etchant for microstructural anlaysis.

3. Results and Discussion TOPSIS is multi criteria decision making method used to solve decision making problems. It gives a solution closer to the ideal solution based on the parameters we get based on experiment done and farther to the less ideal or negative ideal solution. The steps involved in TOPSIS analysis are as follows: Step 1: The first step involves the normalization of the attributes. The formula for calculating the normalized values is given by equation 2

Nxy = Axy / (∑A2xy) (2) Where x = 1…..n; y= 1…..m The calculated normalized values are presented in Table 4.

4

Table 4.Normalized Values Experiment

No. Microhardness

(Hv) Width of passive region

(mV) Steady state creep rate

(S-1) 1 74.8 361 3.81 x 10-7 2 72.3 352 4.11 x 10-7 3 70.33 304 4.15 x 10-7 4 77.23 381 2.89 x 10-7 5 72.13 321 4.24 x 10-7 6 68.9 309 3.65 x 10-7 7 73.1 313 3.6 x 10-7 8 64.55 284 4.45 x 10-7 9 74.3 328 3.76 x 10-7

Step 2: The weights are allocated based on the importance given to each output parameters. In this work equal weights are given to all the output parameters and hence the weight is chosen as 0.33. Step 3: Weighted normalized values are calculated by multiplying each column of normalized attributes with their respective weights. The weighted normalized values are calculated using the equation 3

Pxy = Qy Rxy (3) Table 5 shows the calculated weighted normalized values.

Table 5.Weighted Normalized Matrix Microhardness

(Hv) Width of Passive

region (mV)

Steady State Creep rate (S-1)

0.121729 0.129504 0.108138 0.117661 0.126275 0.116653 0.114455 0.109056 0.117788 0.125684 0.136678 0.082026 0.117384 0.115154 0.120342 0.112128 0.110849 0.103597 0.118963 0.112284 0.102178 0.105048 0.101881 0.126303 0.120916 0.117665 0.106719

Step 4: Determination of positive ideal and less ideal solutions based on the weighted normalized values where Z+ is idealized values and Z- is negative idealized values which are found using equations 4 and 5 respectively

Q+= {Z+1, ……., Z+x} (4) Where Z+y= {max (ZXY) if y ∈ Y; min (ZXY) if y ∈ Y’}

Q-= {Z-1, ………, Z-x} (5) Where Z-y= {max (ZXY) if y ∈ Y; min (ZXY) if y ∈ Y’} The positive ideal and negative ideal values after calculation are given in Table 6.

Table 6.Positive Ideal and Negative Ideal Values

Z+ 0.125684 0.136678 0.12630278 Z- 0.105048 0.101881 0.082025851

Step 5: This step helps in determining the separation from the ideal value The separation from the ideal alternative is given by equation 6

B+x = [Σ (Zy+– Zxy+) 2]0.5 (6) Where x = 1, …, n

5

Similarly, the separation from the negative ideal alternative given by equation 7

B- x = [Σ (Z’y – Z’xy)2 ]0.5 (7) Where x = 1, …, n The values which are obtained from these equations are tabulated and presented in Table 7

Table 7.Separation measure Experiment

No B+ B-

1 0.026828 0.038675

2 0.02151 0.040548 3 0.033791 0.033151 4 0.051659 0.040739 5 0.026649 0.038107

6 0.041907 0.020298 7 0.04041 0.023188 8 0.041124 0.039483 9 0.033338 0.029948

Step 6: Calculation of relative closeness and the same is calculated using equation 8 and tabulated in Table 8

VX = B- X / (B+X+ B- X ) (8)

Table 8.Relative Closeness value Experiment

Number Vx Rank

1 0.590424 2

2 0.653384 1

3 0.495221 4 4 0.440907 7 5 0.588464 3 6 0.326307 9 7 0.364607 8 8 0.489821 6 9 0.473219 5

The graph showing the relative closeness value with respect to different welding trial is shown in Figure 1.

Figure 1. Experimental Number vs Relative Closeness Graph

6

Mean response Table values were calculated based on relative closeness value and the same is presented in Table 9.

Table 9.Response Table Symbol Input

Parameters 1 2 3 Optimum

Level Max-Min Rank

A Welding Current

0.579677 0.451893 0.442549 A1 0.139 1

B Filler Spinning speed

0.465313 0.577223 0.431583 B2 0.138 2

C Filler spin diameter

0.468851 0. 522503

0. 482764 C2 0.028213 3

From Table 9, it can be inferred that Al B2 C2 is the optimized parameter combination. The optimized parameter combination is inside the L9 taguchi combination. Hence, the confirmation test is not needed.

4. ANOVA Anova was used to find the most influencing parameter on the overall objective function. The values that are obtained from ANOVA are tabulated in Table 10.

Table 10.Significance of the parameters from ANOVA Source DF Adj SS Adj MS F-value Percentage

contribution A 2 0.03522 0.01761 2.08 47.16 B 2 0.034873 0.017436 2.06 46.74 C 2 0.004651 0.002326 0.27 6.12

Error 2 0.016951 0.008476 Total 8 0.091695

From Table 10 it is observed that welding current is the most influencing parameter on the overall objective function followed by Filler spinning speed and filler spin diameter. Figure 2 a & b shows the macro and microstructure of the weldment processed at an optimized condition, i.e., welding current of 120 Amps, the filler spinning speed of 1250 rpm and the filler spinning diameter of 2 mm .

Figure 2. (a) Macro structure of the optimized weld (b) Microstructure of the optimized weld

It is evident from Figure 1b that the transformation of grains shape from cellular to equiaxed dendrites ensued at the middle of the weld (FZ) from the base metal. The typical directionally solidified microstructure was attained in the middle of the weld bead due to the difference in solidification velocity

7

and the formation of equiaxed dendrites at the weld FZ was evident owing to the optimization of Spin-Arc GMAW process parameters.

5. Conclusions Spin arc welding process was successfully utilized for joining Al 5083 alloys and the following observations were made. The weld possessed very good corrosion and mechanical properties required for marine, automobile industries. The optimized parameter combination identified through TOPSIS technique is current: 120 A, Filler spinning speed: 1250 rpm, Filler spinning diameter: 2 mm. Based on ANOVA, it is understood that Welding current is the most influencing parameter on the overall objective function.

6. References

1. Cai, D., Han, S., Zheng, S., Luo, Z., Zhang, Y., & Wang, K. (2018). Microstructure and corrosion resistance of Al5083 alloy hybrid plasma-MIG welds. Journal of Materials Processing Technology, 255, 530-535.

2. Liu, Y., Wang, W., Xie, J., Sun, S., Wang, L., Qian, Y., ... & Wei, Y. (2012). Microstructure and mechanical properties of aluminum 5083 weldments by gas tungsten arc and gas metal arc welding. Materials Science and Engineering: A, 549, 7-13.

3. Borrego, L. P., Costa, J. D., Jesus, J. S., Loureiro, A. R., & Ferreira, J. M. (2014). Fatigue life improvement by friction stir processing of 5083 aluminium alloy MIG butt welds. Theoretical and applied fracture mechanics, 70, 68-74.

4. Bisadi, H., Tavakoli, A., Sangsaraki, M. T., & Sangsaraki, K. T. (2013). The influences of rotational and welding speeds on microstructures and mechanical properties of friction stir welded Al5083 and commercially pure copper sheets lap joints. Materials & Design, 43, 80-88.

5. KumarSingh, S., Tiwari, R. M., Kumar, S., & Kumar, S. (2018). Mechanical properties and micrstructure of Al-5083 by TIG. Materials Today: Proceedings, 5(1), 819-822.

6. Umar, M., & Sathiya, P. (2018). Effect of Pulse Duration on Corrosion and Impression Creep Properties of AA5083‐H111 Al–Mg Alloy Weldments Processed by P‐GTAW. Advanced Engineering Materials, 20(6), 1701147.

7. Umar, M., & Sathiya, P. (2019). Influence of melting current pulse duration on microstructural features and mechanical properties of AA5083 alloy weldments. Materials Science and Engineering: A, 746, 167-178

8. Heirani, F., Abbasi, A., & Ardestani, M. (2017). Effects of processing parameters on microstructure and mechanical behaviors of underwater friction stir welding of Al5083 alloy. Journal of Manufacturing Processes, 25, 77-84.

9. Bunaziv, I., Akselsen, O. M., Salminen, A., & Unt, A. (2016). Fiber laser-MIG hybrid welding of 5 mm 5083 aluminum alloy. Journal of Materials Processing Technology, 233, 107-114.

10. Poonguzhali., Deepan Bharathi Kannan., Umar., & Sathiya. Application of ANN Modelling and GA Optimization for Improved Creep and Corrosion Properties of Spin-Arc Welded AA5083-H111 Alloy. Russian Journal of Non-Ferrous Metals, 61(2), 188–198.

11. Mahidhar, V., Sampreet, K. R., Kannan, R., & Kannan, T. D. B. (2019). Parameter optimization in laser welding of Hastelloy C-276 using TOPSIS.

12. Sampreet, K. R., Mahidhar, V., Kannan, R., & Kannan, T. D. B. (2020). Optimization of parameters in Nd: YAG laser welding of Ti-6Al-4V using TOPSIS. Materials Today: Proceedings, 21, 244-247.

13. Srinivasan, L., Chand, K. M., Kannan, T. D. B., Sathiya, P., & Biju, S. (2018). Application of GRA and TOPSIS optimization techniques in GTA welding of 15CDV6 aerospace material. Transactions of the Indian Institute of Metals, 71(2), 373-382.