Multi Response Optimization of Submerged Friction ...

Proceedings of the ASME 2015 International Mechanical Engineering Congress and Exposition IMECE2015 November 13-19, 2015, Houston, Texas

IMECE2015-50353

MULTI RESPONSE OPTIMIZATION OF SUBMERGED FRICTION STIR WELDING PROCESS PARAMETERS USING TOPSIS APPROACH SENTHIL KUMAR VELUKKUDI SANTHANAM Associate Professor, Department of Mechanical Engineering, College of Engineering - Guindy Campus, Anna University, Chennai, Tamilnadu, India [email protected]

LOKESH RATHINARAJ PG – Student, Department of Mechanical Engineering, College of Engineering - Guindy Campus, Anna University, Chennai, Tamilnadu, India [email protected]

RATHINASURIYAN CHANDRAN Research Scholar, Department of Mechanical Engineering, College of Engineering - Guindy Campus, Anna University, Chennai, Tamilnadu, India [email protected]

SHANKAR RAMAIYAN Research Scholar, Department of Mechanical Engineering, College of Engineering - Guindy Campus, Anna University, Chennai, Tamilnadu, India [email protected]

ABSTRACT Friction stir welding (FSW) is a solid-state welding process which is used to join high-strength aircraft aluminum alloys and other metallic alloys which are difficult to weld by conventional fusion welding. In this paper, AA6063-O alloy of 6mm thickness was taken and friction stir welded under the water in order to improve the joint properties. The process parameters considered as rotational speed, welding speed and tool pin profiles (cylindrical, threaded and tapered) are optimized with multi response characteristics including hardness, tensile strength and % elongation. In order to solve a multi response optimization problem, the traditional Taguchi approach is insufficient. To overcome this constraint, a multi criteria decision making approach, namely, techniques for order preference by similarity to ideal solution (TOPSIS) is applied in the present study [13]. The optimal result indicates that the multi response characteristics of the AA6063-O during the submerged friction stir welding process can be enhanced through the TOPSIS approach. The Analysis of Variance (ANOVA) was carried out to investigate the significant parameter for the submerged friction stir welding process. The mechanical properties of the submerged FSW are compared with normal FSW joints

conventional fusion welding. FSW is considered as a most significant development in metal joining in a decade. In friction stir welding, a rotating non consumable tool having pin and shoulder is inserted into the edges of the plates to be welded and travelled along the line of the joint. The friction between the tool and the work piece causes the heat required to weld the joints [12]. During the friction stir welding of heat-treatable aluminum alloys, the coarsening and dissolution of the strengthening precipitates lead to a degradation in joint properties [3]. The strength of the normal friction stir welded joints can be improved by accelerating the heat dissipation. Benavides et al. [4] analyzed the residual grain sizes and microstructures in FSW of AA2024 at room temperature (30°C) and at low temperature (-30°C). The central weld zone grain size and the maximum weld temperature attained for FSW at low temperature is very much less when compared to FSW at room temperature. Fratini et al. [5] developed FSW joints under three different conditions such as free air, forced air and with water flowing across the surface of the joint. There is a significant improvement in the mechanical properties of the joint when the external refrigerants are used and the best is achieved with water. In order to take full advantage, the whole work piece is immersed in water during the welding and the process is named as Submerged Friction Stir Welding [6]. The following authors [7-11] carried out FSW in submerged condition and obtained better results when compared to normal FSW joints. The maximum peak temperature of submerged joint is very much lower when compared to the normal FSW joint.

INTRODUCTION Friction stir welding (FSW) is a solid-state welding process which is used to join high-strength aircraft aluminum alloys and other metallic alloys which are difficult to weld by

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The tensile strength and hardness value of submerged joints are far better than the normal FSW joints. Sanjay Kumar et al. [12] done a multi response optimization of process parameters for FSW of dissimilar aluminium alloys using Grey Relational Analysis (GRA) and Taguchi approach. Rotational speed, tool tilt angle, types of tool pin profile were chosen as process parameters and the tensile strength, % elongation were chosen as output response. A confirmation test was conducted using the optimum process condition in order to show the improvement in mechanical properties and to ensure the robustness of GRA approach. Yuvaraj et al. [13] carried out optimization of abrasive water jet machining process parameters using Technique for Order Preference by Similarity Ideal Solution (TOPSIS) approach. The confirmation experiment indicates that the multi response characteristics of the AA5083-H32 during the abrasive water jet machining process can be enhanced through the TOPSIS method. Previous studies have highlighted the advantages of submerged friction stir welding over normal FSW joints regarding the strength improvement. From the viewpoint of application, it would be more important to optimize the submerged FSW for maximum mechanical properties of the joints. Nevertheless, there is a limited work on this area and the optimization of tool pin profiles for submerged FSW has not been carried on till date. Therefore, AA 6063 alloy was friction stir welded under a submerged condition with various tool pin profiles in the present work and the multi response optimization of process parameters is carried out using TOPSIS approach.

inside the acrylic tank and the tank is clamped to the FSW machine bed. In order to avoid distortion during welding, the work piece is clamped properly to the fixture. The welded sample is shown in Figure 2.

Fig. 1: Submerged FSW experimental setup

EXPERIMENTAL PROCEDURE In this experiment, AA6063-O alloy of 150 mm in length, 75 mm in width and 6 mm in thickness is used. The chemical compositions and mechanical properties are listed in Table 1 and Table 2. As per Taguchi’s method, the degree of freedom of selected orthogonal array should be greater than or equal to the total degree of freedom required for the experiment. Therefore an L9 orthogonal array having 8 (9-1) degree of freedom was used for conducting trials.

Fig. 2: Submerged friction stir welded samples The process parameters considered for the optimization of submerged FSW process were rotational speed, welding speed and tool pin profile. The other parameters such as water head of 50mm, tool tilt angle of 2° and plunge depth of 5mm are maintained as constant in order to limit the study. The process parameters and its various levels are shown in Table 3.

TABLE 1. CHEMICAL COMPOSITION OF AA6063-O (IN WEIGHT %) Si

Pb

Fe

Cu

Ni

Mn

Zn

Mg

Al

0.48

0.17

0.35

0.75

0.72

0.63

0.14

0.69

Bl

TABLE 3. PROCESS PARAMETERS AND THEIR LEVELS Level Process Factor Parameter 1 2 3 Tool pin A Cylindrical Threaded Tapered profile Tool B rotational 800 1000 1200 speed (rpm) Welding C speed 60 120 180 (mm/min)

TABLE 2. MECHANICAL PROPERTIES OF AA6063-O UTS YS Elongation Hardness N/mm2 105

N/mm2 85

% 12.2

Hv 41

The various tool pin profiles, including a simple cylindrical, a threaded pin and a tapered pin were fabricated using H13 tool steel and hardened up to 60 HRC. The dimensions of the tools are summarized and shown in Table 4.

In submerged condition, butt welds were made along the longitudinal direction using FSW machine. The whole setup is shown in Figure 1. The fixture made up of mild steel is placed

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TABLE 4. DIMENSIONS OF THE TOOL PIN PROFILE Small Big diameter Pitch of diameter of Description of the pin of the pin the pin the pin (mm) (mm) (mm)

S. No

Shoulder diameter (mm)

Pin length (mm)

1

Cylindrical

5

5

None

5

15

2

Threaded

5

5

1

5

15

3

Tapered

5

2

None

5

15

Tensile tests of the base metals and the welded joints were carried out by using a universal testing machine at ambient temperature. Smooth tensile test specimens were prepared using wire cut EDM according to ASTM E8. The configuration and size of the transverse tensile specimens are shown in Figure 3.

Ex. No

TABLE 5. L9 ORTHOGONAL ARRAY WITH RESPONSE Micro Percentage Input Parameters UTS Hardness Elongation A

B

C

(Res 1)

(Res 2)

(Res 3)

Pin profile

Rpm

1 2 3 4 5

Cylindrical Cylindrical Cylindrical Threaded Threaded

800 1000 1200 800 1000

mm/ min 60 120 180 120 180

MPa

Hv

%

44.5 45.73 46.32 49.65 43.02 47.53

4.95 5.67 6.27 16.84 4.31

60

70 72 76 112 70 110

6

Threaded

1200

7

Tapered

800

180

85

47.04

9.18

48.35

14.00

58.54

21.53

(All dimensions in mm)

8

Tapered

1000

60

108

Fig. 3: Configuration and size of tensile test specimens [14]

9

Tapered

1200

120

139

The micro hardness value of the welded joint was measured at a spacing of 1mm across the entire region of FSW joint using a Vickers indenter with a load of 9.8 N and a dwell time of 15 s [15]. The L9 orthogonal array with process parameters and output responses is shown in Table 5.

14.87

STEP 1 In TOPSIS, the units of all criteria are eliminated and it has been converted into normalized value. The normalized value (rij) is obtained using the equation (1). The normalised performance values are shown in Table 6. 𝑋𝑖𝑗 (1) 𝑟 = i=1,2,3…9;j=1,2,3

TOPSIS TOPSIS was developed by Hwang and Yoon based on the concept that the chosen parameter should have the shortest distance from the best solution and the longest distance from the worst solution [16]. In TOPSIS approach, a specific weight is given to output responses in order to rank them. The steps involved in TOPSIS are given below.

𝑖𝑗

2 √∑𝑚 𝑖=1 𝑋𝑖𝑗

Where, i = number of alternatives (trials) j = number of criteria (Output responses) xij= represents the actual value of the ith value of jth experimental run.

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TABLE 6. NORMALIZED PERFORMANCE VALUE Experiment

UTS

Hv

%

1

0.2420

0.3087

0.1342

2

0.2489

0.3173

0.1537

3

0.2628

0.3214

0.1698

4

0.3873

0.3445

0.4560

5

0.2420

0.2985

0.1167

6

0.3804

0.3298

0.4027

7

0.2939

0.3264

0.2486

8

0.3734

0.3354

0.3791

9

0.4807

0.4062

0.5831

No

TABLE 8. POSITIVE-IDEAL AND NEGATIVE-IDEAL SOLUTIONS Experiment UTS Hv % No 1 0.0798 0.1018 0.0443 2 0.0821 0.1047 0.0507 3 0.0867 0.1060 0.0560 4 0.1278 0.1136 0.1504 5 0.0798 0.0985 0.0385 6 0.1255 0.1088 0.1329 7 0.0970 0.1077 0.0820 8 0.1232 0.1107 0.1251 9 0.1586 0.1340 0.1924 S+ 0.1586 0.1340 0.1924 S0.0798 0.0985 0.0385

STEP 4 The separation of each alternative from positive ideal solution (S+) and negative ideal solution (S-) is found as per equation (4) and equation (5),

STEP 2 The weighted normalized value (vij) is calculated by multiplying the normalized value by its associated weights and is shown in equation (2), vij =wj * rij i= 1,2,3…9; j=1,2,3

(4)

𝐷𝑖+ = √∑9𝑖=1(𝑣𝑖𝑗 − 𝑠𝑗+ )2

i = 1, 2…9. j=1, 2, 3

(2)

STEP 5 The closeness coefficient value of each alternative (Ci) is calculated as shown in equation (6), 𝐷− (6) Ci= 𝑖

TABLE 7. WEIGHTED NORMALIZED VALUE Ex No UTS HV % 1

0.0798

0.1018

0.0443

2

0.0821

0.1047

0.0507

3

0.0867

0.1060

0.0560

4

0.1278

0.1136

0.1504

5

0.0798

0.0985

0.0385

6

0.1255

0.1088

0.1329

7

0.0970

0.1077

0.0820

8

0.1232

0.1107

0.1251

9

0.1586

0.1340

0.1924

𝐷𝑖_ +𝐷𝑖+

The closeness coefficient values are shown in Table 9. TABLE 9. CLOSENESS COEFFICIENT VALUE

STEP 3 +

Then the positive ideal solution (S ) and negative ideal solution (S-) has been calculated using equation (3), S+ = {(Max (vij) | j  J), (Min (vij) | j  J′) | i=1,2…9}

(5)

𝐷𝑖− = √∑9𝑖=1(𝑣𝑖𝑗 − 𝑠𝑗− )2

Here, equal weightage is given to all the responses [17]. Therefore, wj=0.33. The weighted normalised values are shown in Table 7.

(3)

S- = {(Min (vij) | j  J), (Max (vij) | j  J′) | i=1,2…9}

Experiment No

D+ i

D− i

Ci

Rank

1

0.1708

0.0066

0.0376

8

2

0.1636

0.0138

0.0781

7

3

0.1566

0.0202

0.1145

6

4

0.0558

0.1227

0.6870

2

5

0.1764

0

0

9

6

0.0726

0.1053

0.5919

3

7

0.1291

0.0476

0.2696

5

8

0.0795

0.0976

0.5510

4

9

0

0.1764

1

1

STEP 6 From the closeness coefficient value, the mean effect for each level of process parameters was calculated and shown in Table 10. In addition, a graph was plotted as shown in Figure 4. Considering the highest Ci value in Table 9 and the marked

Where, J is a set of beneficial attributes and J′ is a set of nonbeneficial attributes. The S+ and S- values are shown in Table 8.

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the output response. The percentage of contribution of the various process parameters shown in Figure 5.

points in Figure 4, the determined optimal combination is tapered pin profile, rotational speed of 1200 rpm and welding speed of 120 mm/min.

Error 3%

TABLE 10. AVERAGE CLOSENESS COEFFICIENT VALUE Level Factor

Level 1

Tool pin profile 0.0767

Rotational speed 0.3314

Welding speed 0.3935

Level 2

0.4263

0.2097

0.5883

Level 3

0.6068

0.5688

0.1280

Welding speed 33%

Tool pin profile

44% Rotational speed 20%

Fig.5: % Contribution of Process Parameters COMPARISON WITH NORMAL FSW Once the optimal process parameters (tapered pin profile, 1200 rpm, and 120 mm/min) were found, AA6063-O alloy was friction stir welded under normal condition. The mechanical properties of base metal, normal FSW joints and submerged FSW joints were compared and it was shown in Table 12. It was found that the better results were obtained with submerged FSW. Fig.4: Main effect of closeness coefficient value

TABLE 12. COMPARISON OF NORMAL FSW AND SUBMERGED FSW S.NO Condition UTS Hardness % (MPa) (Hv) elongation 1 Base metal 105 41 8.68

ANOVA In order to analyze the significance and the contribution of each parameter to the closeness coefficient value, ANOVA was carried out [18] and is shown in Table 11. This analysis was carried out for a level of significance of 5 %, i.e. for 95 % confidence level.

0.9873

Normal FSW

116

48.85

17.96

3

Submerged FSW

139

58.54

21.53

CONCLUSION In this work, FSW of AA6063-O alloy was carried out under submerged condition. Three different tool types were associated with welding parameters to analyze the effect of combined factors on the mechanical strength such as UTS, percentage of elongation and hardness. Based on the L9 orthogonal array, an experimental plan was conducted and the data taken from the experiments were analyzed using TOPSIS approach. The following conclusions were drawn.  The optimum process parameter combinations such as tapered pin profile, rotational speed of 1200 rpm and welding speed of 120 mm/min yield higher closeness coefficient value.  The percentage of contribution of submerged FSW process parameters was assessed using ANOVA. It was found that the tool pin profile, rotational speed and

TABLE 11. ANOVA OF CLOSENESS COEFFICIENT VALUE Degrees Mean Process Sum of FFactor of sum of Parameters squares test freedom squares Tool pin A 0.4358 2 0.2179 14.04 profile Rotational B 0.2001 2 0.1006 6.45 speed Welding C 0.3203 2 0.1601 10.32 speed Error 0.0310 2 0.0155 Total

2

8

Generally, when F>4, it means that the change in the process parameter has a substantial influence on the quality characteristic [19]. Therefore, the tool pin profile, the rotational speed and the welding speed have the significant influence on

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



welding speed contributes 44%, 20% and 33% respectively. From ANOVA, it was observed that all the process parameters have significant influence on the output response. The mechanical properties of normal FSW and submerged FSW joints were compared and it was found that the results of submerged FSW is 20% higher than the normal FSW. In future, the microstructural analysis of submerged FSW joints will be carried out.

[10] Rathinasuriyan, C., VS Senthil Kumar, and AvinGanapathiShanbhag. "Radiography and Corrosion Analysis of Sub-merged Friction Stir Welding of AA6061-T6 alloy." Procedia Engineering 97 (2014): 810-818. [11] Lokesh, R.,Senthil Kumar, V.S., Rathinasuriyan, C. and Sankar, R., “Optimization of process parameters tool pin profile, rotational speed and welding speed for submerged friction stir welding of AA6063 alloy.” International Journal of Technical and Research Applications12 (2015): 35-38. [12] Kumar, Sanjay, and Sudhir Kumar. "Multiresponse optimization of process parameters for friction stir welding of joining dissimilar Al alloys by gray relation analysis and Taguchi method." Journal of the Brazilian Society of Mechanical Sciences and Engineering (2014): 110. [13] Yuvaraj, N., and M. Pradeep Kumar. "Multiresponse Optimization of Abrasive Water Jet Cutting Process Parameters Using TOPSIS Approach." Materials and Manufacturing Processes just-accepted (2014). [14] Puviyarasan, M., and VS Senthil Kumar. "Optimization of friction stir process parameters in fabricating AA6061/SiCp composites." Procedia Engineering 38 (2012): 1094-1103. [15] Zhang, Yu, et al. "Microstructural characteristics and mechanical properties of Ti–6Al–4V friction stir welds." Materials Science and Engineering: A 485.1 (2008): 448-455. [16] Yoon, K. Paul, and Ching-Lai Hwang. Multiple attribute decision making: an introduction. Vol. 104. Sage Publications, 1995. [17] Shojaeefard, Mohammad Hasan, Mostafa Akbari, and ParvizAsadi. "Multi objective optimization of friction stir welding parameters using FEM and neural network." International Journal of Precision Engineering and Manufacturing15.11 (2014): 2351-2356. [18] Elangovan, Sooriyamoorthy, K. Prakasan, and V. Jaiganesh. "Optimization of ultrasonic welding parameters for copper to copper joints using design of experiments." The International Journal of Advanced Manufacturing Technology51.1-4 (2010): 163-171. [19] Yang, WH P., and Y. S. Tarng. "Design optimization of cutting parameters for turning operations based on the Taguchi method." Journal of Materials Processing Technology 84.1 (1998): 122-129.

ACKNOWLEDGMENT The authors gratefully acknowledge the Science &Engineering Research Board, Department of Science and Technology (SERB-DST), New Delhi, India, for their financial assistance to conduct the research work through project no.SR/S3/MERC/0092/2011. REFERENCES [1] Mishra, Rajiv Sharan, and Z. Y. Ma. "Friction stir welding and processing."Materials Science and Engineering: R: Reports 50.1 (2005): 1-78. [2] Johnsen, Mary Ruth. "Friction stir welding takes off at Boeing." Welding Journal78.2 (1999): 35-39. [3] Fonda, R. W., and J. F. Bingert. "Microstructural evolution in the heat-affected zone of a friction stir weld." Metallurgical and materials transactions A 35.5 (2004): 1487-1499. [4] Benavides, Sa, et al. "Low-temperature friction-stir welding of 2024 aluminum." Scriptamaterialia 41.8 (1999): 809-815. [5] Fratini, L., G. Buffa, and R. Shivpuri. "In-process heat treatments to improve FS-welded butt joints." The International Journal of Advanced Manufacturing Technology 43.7-8 (2009): 664670. [6] Rathinasuriyan, C. and VS Senthil Kumar. “Submerged Friction Stir Welding and Processing: Insights of Other Researchers.” International Journal of Applied Engineering Research10 (2015): 6229-6235. [7] Liu, Hui-Jie, et al. "Mechanical properties of underwater friction stir welded 2219 aluminum alloy." Transactions of Nonferrous Metals Society of China 20.8 (2010): 1387-1391. [8] Zhang, Hui-jie, Hui-jie Liu, and Y. U. Lei. "Thermal modeling of underwater friction stir welding of high strength aluminum alloy." Transactions of Nonferrous Metals Society of China 23.4 (2013): 1114-1122. [9] Zhang, Huijie, and Huijie Liu. "Mathematical model and optimization for underwater friction stir welding of a heat-treatable aluminum alloy." Materials & Design 45 (2013): 206-211.

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