Quality Prediction Of Friction Stir Weld Joints On AA 5052 H32 Aluminium Alloy Using Fuzzy Logic Technique

Available online at www.sciencedirect.com

ScienceDirect Materials Today: Proceedings 5 (2018) 12124–12132

www.materialstoday.com/proceedings

ICMMM - 2017

Quality Prediction Of Friction Stir Weld Joints On AA 5052 H32 Aluminium Alloy Using Fuzzy Logic Technique S. Shanavas a*, J. Edwin Raja Dhas b a

Research Scholar, Department of Mechanical Engineering, Noorul Islam University, Tamil Nadu 629180, India. b Deputy Director - Admissions, Noorul Islam University, Tamil Nadu 629180, India.

Abstract This paper addresses the development of fuzzy model for weld quality prediction. Significant friction stir welding parameters affecting the weld quality are tool pin geometry, tool rotational speed, welding speed, and tool tilt angle. Welding experiment is performed on AA 5052-H32 aluminium plates by central composite design to attain maximum tensile strength of the weld joint. Quality of weld measured in terms of tensile strength and percentage elongation is predicted using fuzzy logic and the results are compared with statistical analysis. Confirmatory experimental results show that the fuzzy model can predict an adequate output with less error than statistical analysis. © 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Materials Manufacturing and Modelling (ICMMM - 2017).

Keywords: Friction stir welding; Aluminium alloy 5052; Tensile strength; Fuzzy logic; Response surface method.

1. Introduction High strength, low weight and corrosion resistance are the predominant properties of aluminium alloy which replace steel by the alloy in several structural and automotive applications [1]. The welding of aluminium and its alloys by fusion welding was quite difficult, since aluminium may react with oxygen and nitrogen, leads to defects. The problem was overcome by the invention of a solid state welding process called Friction stir welding (FSW).

* Corresponding author. Tel.: +91 94005 29829; fax: +91 0470 2640605. E-mail address: [email protected]

2214-7853© 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Materials Manufacturing and Modelling (ICMMM - 2017).

S. Shanavas and J. Edwin Raja Dhas / Materials Today: Proceedings 5 (2018) 12124–12132

12125

FSW process uses a non consumable tool, which rotates, then plunges and finally moves transverse to generate frictional heat in the adjoining surfaces for joining the surfaces. It was invented in 1991 by Thomas et al [2]. The process can produce high strength and defect free welded joints of aluminium alloy [3]. Aerospace, marine, and automobile industries require aluminium alloy, which offers high strength, and high corrosion resistance to industrial and marine environment. AA 5052 H32 is a typical 5xxx series aluminium alloy, which is strain hardened and it exhibits high resistance to corrosion [4,5,6]. Welded joints of these alloys are also highly corrosion resistant [7]. Several parameters are being influencing the strength of welded joints by FSW. Tool pin geometry, tool rotational speed, welding speed, tool tilt angle, are the major factors for generating the heat and stirring action for joining the material effectively [7-23]. Several works on the influence of pin geometry on weld strength of aluminium alloys by FSW has been carried out [8-14]. Flat surfaces tool pin profiles make eccentricity during FSW process and the eccentricity increases the shear stress and thereby increase friction and the heat generated [8]. The increase in shearing force happens within the weld metal and does not at the tool and metal interface. Pins having tapered profile distribute the bending stress due to drag, which helps to resist fracture. A tapered pin structure also distribute the vertical pressure evenly along the pin [13]. The tapered pin give better strength and uniform length throughout the length of plate by proper design. By tapered design even at high rotational speed, tool wear can be minimized [14]. Compared to straight pin, tapered pin can plunge smoothly into the intersecting surfaces. Considering the advantages of both flat surface pins and tapered pins, tapered flat surface pins were used in this study to compare with cylindrical tapered pins. Some studies were done on FSW between AA 5052-O aluminium alloy plates [7,15-17]. The effect of tool geometry on heat generation during FSW of aluminium AA5052-H32 alloy was studied [16]. Studies on friction stir weld joint between AA5052-H32 aluminium Alloy and HSLA steel were also carried out [17]. Researchers usually prefer mathematical modeling technique or soft computing technique to relate welding parameters with response. Slight variations in one welding parameter will have an effect on the weld performance measure like tensile strength. Input and output parameter can be correlated by various mathematical modeling techniques. But they require enormous processing power, and are often far too slow for real time simulations. They does not guarantee optimal or near optimal solution for particular machine or environment. Hence soft computing techniques such as fuzzy logic system, neural network system, and expert system emerged. Among the mathematical technique, nowadays researchers prefer Response surface method using regression modeling [1923] to predict the response by empirical relationship, due to its wide advantages over other techniques. Though mathematical modeling technique do not ensure optimal or near optimal solution for particular machine or environment, soft computing techniques normally requiring human intelligence address the problem [23]. Fuzzy logic is a soft computing technique which is based on fuzzy set theory. It can deal with even imprecise data in an organized manner. This paper predicts the quality of friction stir weld joints of AA 5052 H32 aluminium alloy using fuzzy logic technique and the results are compared with statistical analysis. 2. Experimental procedure The key factors which influence the joint properties and the ranges of those factors of AA 5052-H32 aluminium alloy are shown in Table 1. A central composite circumscribed design matrix with four factors, five levels and 31 runs by response surface method were used for the study. Several trial experiments were carried out to set the limits of all selected key factors. The treatments shown in Table 2 were used as input data for both mathematical modeling and soft computing technique. Table 1. Key factors and their levels Ser. No.

Key factors

Symbols

Unit

Levels (-2)

(-1)

(0)

(+1)

(+2)

1

Pin geometry (Tapered)

G

-

Hexagon

Pentagon

Square

Cylindrical

Triangular

2

Tool rotational speed

R

RPM

400

500

600

700

800

3

Welding speed

S

mm/min

45

55

65

75

85

4

Tool tilt angle

A

Degree

0.5

1

1.5

2

2.5

12126

S. Shanavas and J. Edwin Raja Dhas / Materials Today: Proceedings 5 (2018) 12124–12132

The upper and lower limits of factors were coded as +2 and -2 respectively. The intermediate values were determined using the relationship, Xi = 2 [2X – (Xmax + Xmin)] / (Xmax – Xmin)]

(1)

where, Xmin and Xmax are the lowest and highest levels of the variable, X is any value of the variable from Xmin to Xmax, and Xi is the expected coded value of variable X. Table 2. Designed experimental matrix and results Exp. Trial

Factor

UTS (Mpa)

%E

Weld efficiency (%)

-1

196.09

20.28

90.76

-1

184.97

16.25

85.61

-1

193.99

18.75

89.78

G

R

S

A

1

-1

-1

-1

2

+1

-1

-1

3

-1

+1

-1

4

+1

+1

-1

-1

184.27

16.81

85.29

5

-1

-1

+1

-1

193.48

20.50

89.55

6

+1

-1

+1

-1

179.48

15.16

83.07

7

-1

+1

+1

-1

190.16

16.19

88.01

8

+1

+1

+1

-1

175.69

13.72

81.32

9

-1

-1

-1

+1

196.95

25.16

91.15

10

+1

-1

-1

+1

194.34

24.53

89.95

11

-1

+1

-1

+1

198.80

24.69

92.01

12

+1

+1

-1

+1

196.84

26.06

91.10

13

-1

-1

+1

+1

198.78

21.09

92.00

14

+1

-1

+1

+1

195.39

21.91

90.43

15

-1

+1

+1

+1

198.64

20.97

91.94

16

+1

+1

+1

+1

196.62

21.28

91.00

17

-2

0

0

0

194.72

16.31

90.12

18

+2

0

0

0

182.56

15.22

84.49

19

0

-2

0

0

190.21

17.16

88.04

20

0

+2

0

0

191.48

16.59

88.62

21

0

0

-2

0

197.58

24.28

91.45

22

0

0

+2

0

191.81

19.19

88.78

23

0

0

0

-2

175.09

13.03

81.04

24

0

0

0

+2

196.79

25.03

91.08

25

0

0

0

0

199.55

24.05

92.36

26

0

0

0

0

200.32

23.13

92.71

27

0

0

0

0

201.42

24.28

93.22

28

0

0

0

0

200.03

24.50

92.58

29

0

0

0

0

199.13

25.94

92.16

30

0

0

0

0

202.04

25.09

93.51

31

0

0

0

0

199.43

25.47

92.30

S. Shanavas and J. Edwin Raja Dhas / Materials Today: Proceedings 5 (2018) 12124–12132

12127

AA 5052-H32 aluminium alloy plates having 6 mm thickness were machined to 150 mm x 50mm size in such a way that the 150 mm side was cut in line with the rolling direction of the plate. Wire brushing was employed for cleaning the surfaces for welding. The plates were welded along the rolling direction in a single pass, using tapered tool pin profiles such as cylindrical tapered, hexagon tapered, pentagon tapered, square tapered, and triangular tapered (Fig. 1). Thirty one joints were fabricated in this investigation as per the designed matrix (Fig. 2). ASTM – E8 standard [29] was followed for conducting the tensile strength test. The tests were carried using UTM (DAKUTB 9103) with 100 KN capacity.

Fig. 1. Tool pin geometry used

Fig. 2. Photographic view of FS welded joints at different FSW parameter combinations.

3. Construction of proposed Fuzzy logic model Fuzzy logic-based technique has been developed to model input-output relationships of friction stir welding process. Fuzzy logic model for predicting tensile strength which is a good indicator of weld quality is developed. Friction stir welding factors such as tool pin geometry, tool rotational speed, welding speed, and tool tilt angle are used as input crisp parameters to train and test the proposed model. Development of fuzzy logic model undergoes through four stages such as fuzzification of variables, creation of fuzzy rule base, updating fuzzy inference engine and defuzzification of variables. The output crisp variables are ultimate tensile strength and percentage elongation. The data for developing the proposed fuzzy logic model is obtained from experimentation. Data set used to train the proposed model is taken from Table 2. Triangular shaped membership functions are assigned for both input as well

12128

S. Shanavas and J. Edwin Raja Dhas / Materials Today: Proceedings 5 (2018) 12124–12132

as output crisp variables. The input and output variables are fuzzified before forming rules. The developed mamdani fuzzy model for weld quality prediction is shown in Fig. 3. Rule viewer of the developed model is displayed in Fig. 4.

Fig. 3. Developed fuzzy model for weld quality prediction

Fig. 4. Rule Viewer fuzzy model for weld quality prediction

S. Shanavas and J. Edwin Raja Dhas / Materials Today: Proceedings 5 (2018) 12124–12132

12129

4. Developing the statistical model The response function, such as ultimate tensile strength (UTS) and percentage elongation (%E) of the welded joints are the functions of the tool pin geometry (G), tool rotational speed (R), welding speed (S) and tool tilt angle (A). It can be mathematically explicit as UTS = f (G, R, S, A) %E = f (G, R, S, A)

(2) (3)

The statistical regression equation [16] used to form the response function ‘Y’ is [30], Y = b0 + ∑ bixi + ∑ biixi2 + ∑ bijxixj

(4)

5. Results and discussion 5.1. Checking the performance of fuzzy model Data obtained from experimentation through response surface methodology helps to train the fuzzy model for the prediction of ultimate tensile strength and percentage elongation of friction stir weld joint. The performance of the developed fuzzy model is analyzed in terms of accuracy with the results obtained from confirmatory experiments. Table 3. Validation of the developed model by conformity experiments – UTS Trial no.

UTS (MPa)

Welding parameters

Experimental result

% Error

Predicted result Fuzzy model

Regression model

Fuzzy model

Regression model

187.09

192.43

192.45

2.77

2.79

0.5

194.79

192.68

186.07

1.09

4.69

-0.5

1.5

194.93

190.29

199.19

2.44

2.14

-0.5

1.5

0.5

196.82

201.20

189.55

2.18

3.84

0.5

-0.5

1.5

187.48

189.49

191.72

1.06

2.21

P

N

S

A

1

0

-1.5

1.5

-0.5

2

1

1.5

0.5

3

2

0.5

4

-1

5

-2

Table 4. Validation of the developed model by conformity experiments – % E Trial no.

Percentage elongation (% E)

Welding parameters

Experimental result

% Error

Predicted result Fuzzy model

Regression model

Fuzzy model

Regression model

19.03

18.45

20.25

3.14

6.02

0.5

18.75

19.37

18.02

3.20

4.05

-0.5

1.5

21.91

21.54

22.47

1.72

2.49

-0.5

1.5

0.5

20.13

20.88

19.11

3.59

5.34

0.5

-0.5

1.5

18.29

17.71

19.01

3.28

3.79

P

N

S

A

1

0

-1.5

1.5

-0.5

2

1

1.5

0.5

3

2

0.5

4

-1

5

-2

12130

S. Shanavas and J. Edwin Raja Dhas / Materials Today: Proceedings 5 (2018) 12124–12132

For that experiments were carried out to judge the validity of the developed model. Five different welds were prepared using unlike values of factors other than those used in experimental matrix and their ultimate tensile strength and elongation were determined. The data set in Table 3 and 4 is used for the validation of the built up fuzzy model. The predicted values from fuzzy model are observed very closer to the experimental values without appreciable error. 5.2. Checking the adequacy of the regression model The statistical result shown in Table 5 gives higher R-square value and adjusted R-square value for the ultimate tensile strength and percentage elongation respectively, indicates a very high degree of a match exists between the predicted empirical relationship and experimental value. This validates the experimental value. The suitability of the developed models was tested by the analysis of variance technique (ANOVA), which is furnished in Table 6. The result reveals that the calculated F-ratio value of the tested model is very higher than the tabulated F-ratio value at a confidence level of 95%, indicates that the developed models are adequate. Table 5. Statistical results of the developed regression model Response function

R-Square value

Adjusted R-Square value

Standard error

UTS

0.975

0.953

1.6

%E

0.945

0.896

1.318

Table 6. ANOVA results of the developed regression model Response function

UTS

%E

Type

Sum of squares

Degree of freedom

Mean squares

F ratio calculated

tabulated

Regression

1601.74

14

114.41

44.66

2.37

Error

40.99

16

2.562

-

-

Regression

477.338

14

34.096

19.62

2.37

Error

27.809

16

1.738

-

-

6. Conclusions The conclusions made from the study on quality prediction of friction stir weld joints on AA 5052 H32 aluminium alloy using fuzzy logic technique are as follows: 1) The fuzzy logic model and regression model were developed for the prediction of the response function such as ultimate tensile strength and percentage elongation of the welded plates. 2) The developed model by regression analysis can be used very effectively for the prediction of the responses of the joints at a confidence level of 95%. 3) Five different welds were prepared using unlike values of factors other than those used in experimental matrix and their ultimate tensile strength and elongation were determined for the validation of developed models. 4) The result shows that the fuzzy model can predict an adequate output with less than 4% error, whereas the regression model predicts the necessary output with less than 7%. Hence the fuzzy model can be recommended for the prediction of the weld quality for any parameters within the range in the reasonable time.

S. Shanavas and J. Edwin Raja Dhas / Materials Today: Proceedings 5 (2018) 12124–12132

12131

References [1]

A. Grimm, S. Schulze, A. Silva, G. Gobel, J. Standfuss, B. Brenner, E. Beyer, U. Fussel, Friction stir welding of light metals for industrial applications, Materials Today: Proceedings. 2S ( 2015 ) 169 – S178.

[2]

R. S. Mishra, Z. Y. Ma, Friction sir welding and processing, Material Science and Engineering R. 50 (2005) 1-78.

[3]

A. Anand, A. Khajuria, Welding processes in marine application: A review, International Journal of Mechanical Engineering and Robotics Research. 2, (2015) 215-225.

[4]

Seong-Jong KIM, Seok-Ki JANG, Min-Su HAN, Jae-Cheul PARK, Jae-Yong JEONG, Sang-Ok CHONG, Mechanical and electrochemical characteristics in sea water of 5052-O aluminum alloy for ship, Trans. Nonferrous Met. Soc. China. 23 (2013) 636−641.

[5]

S. Gudic, L. Vrsalovic, M. Kliskic, I. Jerkovic, A. Radonic, M. Zekic, Corrosion inhibition of

AA 5052 aluminium alloy in NaCl

solution by different types of honey, International Journal of Electrochemical Science. 11 (2016) 998-1011. [6]

Abhijeet Bhowmik, Dilip Mishra, A comprehensive study of an aluminum alloy AL-5052, Advance Physics Letter. 3 (2016) 20-22.

[7]

Raza Moshwan, Farazila Yusof, M. A. Hassan, S. M. Rahmat, Effect of tool rotational speed on force generation, microstructure and

[8]

K. Elangovan, V. Balasubramanian, Influence of tool pin profile and welding speed on the formation of friction stir processing zone in

mechanical properties of friction stir welded AA 5052-O alloy, Materials and Design. 66 (2015) 118–128. AA2219 aluminium alloy, Journal of Materials Processing Technology. 200 (2008) 163-175. [9]

P. Prasanna, C. H. Penchalayya, D. Anandamohana Rao, Effect of tool pin profiles and heat treatment process in the friction stir welding of AA 6061 aluminium alloy, American Journal of Engineering Research. 2 (2013) 7-15.

[10] S. M. Bayazid, H. Farhangi, A. Ghahramani, Effect of Pin Profile on Defects of Friction Stir Welded 7075 Aluminum Alloy, Procedia Materials Science. 11 ( 2015 ) 12 – 16. [11] D. Venkateswarlu, N. R. Mandal, M. M. Mahapatra, S. P. Harsh, Tool Design Effects for FSW of AA7039, Welding Research. 92 (2013) 41 – 47. [12] S. Ugender, A. Kumar, A. Somi Reddy, Experimental investigation of tool geometry on mechanical properties of friction stir welding of AA 2014 aluminium alloy, Procedia Materials Science. 5 (2014) 824-831. [13] Mel Schwartz, Innovations in Materials Manufacturing, Fabrication, and Environmental Safety, CRC Press, (2010) [14] Ashish Bist, J. S. Saini, Bikramjit Sharma, A review of tool wear prediction during friction stir welding of aluminium matrix composite, Transactions of Nonferrous Metals Society of China. 26 (2016) 2003–2018. [15] Yong-Jai Kwon, Seong-Beom Shim, Dong-Hwan Park, Friction stir welding of 5052 aluminum alloy plates, Transactions of Nonferrous Metals of China. 19 (2009) 23-27. [16] Leonardo N. Tufaro, Ivan Manzoni, Hernan G. Svoboda, Effect of Heat Input on AA5052 Friction Stir Welds Characteristics, Procedia Materials Science. 8 ( 2015 ) 914 – 923. [17] K. K. Ramachandran, N. Murugan, S. Shashi kumar, Tool Design Friction Stir Welding of Aluminum Alloy AA5052and HSLA Steel, Welding Research. 94 ( 2015 ) 291 – 300. [18] G. Elatharasan, V. S. Senthil Kumar, An experimental analysis and optimization of process parameter on friction stir welding of AA 6061T6 aluminium alloy using RSM, Procedia Engineering. 64 (2013) 1227-1234. [19] Ramanjaneyulu KadaganchI, Madhusudhan Reddy Gankidi, Hina Gokhale, Optimization of process parameters of aluminum alloy AA 2014-T6 friction stir welds by response surface methodology, Defence Technology. 11 (2015) 209-219. [20] G. Rambabu, D. Balaji Naik, C. H. Venkata Rao, K. Srinivasa Rao, G. Madhusudan Reddy, Optimization of friction stir welding parameters for improved corrosion resistance of AA 2219 aluminium alloy joints, Defence Technology. 11 (2015) 330-337. [21] Binoy Krishna Biswas, Pradip Kumar Pal, and Asish Bandyopadhyay, Optimization of process parameters for flux cored arc welding of boiler quality steel using response surface methodology and grey-based Taguchi methods, International Journal of Materials, Mechanics and Manufacturing. 4 (2016) 8-16. [22] S. T. Selvamani, S. Divagar, M. Vigneshwar, Application of response surface methodology (RSM) in friction welding AISI 1020 grade steel joints, International Journal of Engineering Research in Africa. 16 (2015) 38-44.

12132

S. Shanavas and J. Edwin Raja Dhas / Materials Today: Proceedings 5 (2018) 12124–12132

[23] R. Teti and S.R.T. Kumara, “Intelligent computing methods for manufacturing systems”, Annals of the CIRP, vol. 46, pp.629-652, 1997. [24] D.T. Pham and P.T.N. Pham, “Artificial intelligence in engineering”, International Journal of Machine Tools and Manufacture, vol. 39, pp. 937–949, 1999. [25] I.S Kim, J.S Son, C.E Park, C.W Lee, Yarlagadda and K.D.V Prasad, “A study on prediction of bead height in robotic arc welding using a neural network,” Journal of Materials Processing Technology vol. 131 pp. 229–234,2002. [26] R. Palanivel, R. F. Laubscher, I. Dinaharan, N. Murugan, Tensile strength prediction of dissimilar friction stir-welded AA6351–AA5083 using artificial neural network technique, Journal of the Brazilian Society of Mechanical Sciences and Engineering. 38 (2016) 1647–1657. [27] N. D. Ghetiya, K.M. Patel, Prediction of tensile strength in friction stir welded aluminium alloy using artificial neural network, Procedia Technology. 14 (2014) 274 – 281. [28] Hasan Okuyucu, Adem Kurt, Erol Arcaklioglu, Artificial neural network application to the friction stir welding of aluminum plates, Materials & Design. 28 (2007) 78–84. [29] ASTM E8/E8M-09. Standard test methods for tension testing of metallic materials1, ASTM International. (2010). [30] Douglas C. Montgomery, Design and Analysis of Experiments, Eight edition, John Wiley and Sons, New York, 2013. [31] Jianbin Chen, Xinjian Yuan, Zhan Hu, Changzheng Sun, Yanxin Zhang, Yuxuan Zhang, Microstructure and mechanical properties of resistance-spot-welded joints for A5052 aluminum alloy and DP 600 steel, Materials Characterization. 120 (2016) 45-52.