Skip to main content
SpringerLink
Log in

Deep Neural Network-Based Approach for Modeling, Predicting, and Validating Weld Quality and Mechanical Properties of Friction Stir Welded Dissimilar Materials

  • Machine Learning: Deformation Processes
  • Published:
JOM Aims and scope Submit manuscript

Abstract

The present investigation highlights the development of a suitable and novel deep neural network-based learning model for accurately predicting the weld quality and mechanical properties of difficult-to-join dissimilar materials. Optimized experimental welding parameters in the available literature were taken as input in the deep neural network (DNN). A feed-forward and back-propagated DNN was developed to apprehend the high non-complexity present in friction stir welding of dissimilar materials. Unlike most neural networks, activation functions were altered between layers, effectively capturing non-linearity. The developed model was used to design an experimental condition for dissimilar friction stir welding of aluminum and titanium. Microstructural characterization of the weld was performed to comprehend the influence of parameters on the quality of the joint produced. A close correlation between the machine-learning model and the experimental results was established. The coefficient of determination \(R^2\) between the predicted strength and the actual strength was 0.95 on the training dataset and 0.9 on the testing dataset. Similarly, \(R^2\) between the predicted strength and the actual strength for the experimental dataset was 0.91, thus making the model suitable for predicting experimental conditions and corresponding mechanical properties with the highest accuracy for any unknown dissimilar friction stir welds.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. G. Wang, Y. Zhao, and Y. Hao, J. Mater. Sci. Technol. 34(1), 73 (2018).

    Google Scholar 

  2. S.R. Nathan, A. Visvalingam Balasubramanian, G. Rao, T. Sonar, M. Ivanov, and K. Suganeswaran, Int. J. Lightweight Mater. Manuf. 6(4), 469 (2023).

    Google Scholar 

  3. C.B Smith, J.F Hinrichs, and W.A. Crusan. Robotic friction stir welding: the state of the art, in 4th Friction Stir Welding International Symposium, pp. 14–16, (2003)

  4. K.J. Colligan, The friction stir welding process: an overview, in Friction Stir Welding. ed. by D. Lohwasser, and Z. Chen. Woodhead Publishing Series in Welding and Other Joining Technologies. (Woodhead Publishing, Cambridge, 2010), pp. 15–41

  5. L.E. Murr, Friction-Stir Welding and Processing (Springer International Publishing, Cham, 2015), pp1–18.

    Google Scholar 

  6. Y. Mao, L.M. Ke, L. Fencheng, Q. Liu, C. Huang, and L. Xing, Mater. Des. 62, 334 (2014).

    Google Scholar 

  7. S. Amini, M. Amiri, and A.A. Barani, Int. J. Adv. Manuf. Technol. 76, 255 (2015).

    Google Scholar 

  8. Md. Reza-E-Rabby, and A. Reynolds, Procedia Eng. 90, 637 (2014).

    Google Scholar 

  9. P. Xue, D.R. Ni, D. Wang, B.L. Xiao, and Z.Y. Ma, Mater. Sci. Eng. A-struct. Mater. Prop. Microstruct. Process. 528, 4683 (2011).

    Google Scholar 

  10. M. Muthu, and V. Jayabalan, J. Mater. Process. Technol. 217, 105 (2015).

    Google Scholar 

  11. H. Bisadi, M. Akram Tavakoli, T. Sangsaraki, and K. Tour Sangsaraki, Mater. Des. 43, 80 (2013).

    Google Scholar 

  12. K.P. Mehta, and V.J. Badheka, Int. J. Adv. Manuf. Technol. 80, 2073 (2015).

    Google Scholar 

  13. M. Jayaraman, R. Sivasubramanian, V. Balasubramanian, and A.K. Lakshminarayanan, J. Sci. Ind. Res. 68, 36–43 (2009).

    Google Scholar 

  14. D.C. Montgomery, Design and Analysis of Experiments (John wiley & sons, Hoboken, NJ, 2017), pp1–31.

    Google Scholar 

  15. C. Rathinasuriyan and V.S.S. Kumar, Adv. Mater. Process. Technol. 7(4), 696 (2021).

    Google Scholar 

  16. S. Rajakumar, C. Muralidharan, and V. Balasubramanian, Proc. Inst. Mech. Eng., Part B: J. Eng. Manuf. 224(8), 1175 (2010).

    Google Scholar 

  17. S.M. Senthil, R. Parameshwaran, S. Ragu Nathan, M. Bhuvanesh Kumar, and K. Deepandurai, Struct. Multidiscip. Optim. 62(3), 1117 (2020).

    Google Scholar 

  18. G. Buffa, L. Fratini, and F. Micari, J. Manuf. Process. 14(3), 289 (2012).

    Google Scholar 

  19. R. Palanivel, I. Dinaharan, and R.F. Laubscher, J. Braz. Soc. Mech. Sci. Eng. 41(2), 1 (2019).

    Google Scholar 

  20. N.D. Ghetiya, and K.M. Patel, Procedia Technol. 14, 274 (2014).

    Google Scholar 

  21. H. Okuyucu, A. Kurt, and E. Arcaklioglu, Mater. Des. 28(1), 78 (2007).

    Google Scholar 

  22. S. Verma, M. Gupta, and J.P. Misra, MethodsX 5, 1048 (2018).

    Google Scholar 

  23. T.A. Shehabeldeen, M.A. Elaziz, A.H. Elsheikh, O.F. Hassan, Y. Yin, X. Ji, X. Shen, and J. Zhou, IEEE Access 8, 79896 (2020).

    Google Scholar 

  24. T. Medhi, S.A.I. Hussain, B.S. Roy, and S.C. Saha, Appl. Soft Comput. 104, 107190 https://doi.org/10.1016/j.asoc.2021.107190 (2021).

    Article  Google Scholar 

  25. P.C. Pendharkar, IEEE Trans. Knowl. Data Eng. 17, 1379 (2005).

    Google Scholar 

  26. R.S. Mishra and Z.Y. Ma, Mater. Sci. Eng. R. Rep. 50(1–2), 1 (2005).

    Google Scholar 

  27. V. Msomi and S. Mabuwa, Adv. Ind. Manuf. Eng. 1, 100002 https://doi.org/10.1016/j.aime.2020.100002 (2020).

    Article  Google Scholar 

  28. S. Mabuwa and V. Msomi, Mater. Today: Proc. 45, 5400 (2021).

    Google Scholar 

  29. H.H. Jadav, V. Badheka, D.K. Sharma, and G. Upadhyay, Mater. Today: Proc. 43, 84 (2021).

    Google Scholar 

  30. M. Gomathisankar, M. Gangatharan, and P. Pitchipoo, Mater. Today: Proc. 5(6), 14397 (2018).

    Google Scholar 

  31. S. Rathore, R.K.R. Singh, and K.L.A. Khan, Mater. Today: Proc. 43, 326 (2021).

    Google Scholar 

  32. M. Marichamy and S. Babu, Mater. Today: Proc. 39, 1527 (2021).

    Google Scholar 

  33. J. John, S.P. Shanmuganatan, and M.B. Kiran, Mater. Today: Proc. 5(11), 25458 (2018).

    Google Scholar 

  34. M. Makeshkumar, S.R. Surender, R. Madesh, M. Sasi Kumar, and K.P. Shankar, Mater. Today: Proc. 45, 8054 (2021).

    Google Scholar 

  35. M. Shunmugasundaram, A. Praveen Kumar, N.K. Amudhavalli, and S. Sivasankar, Mater. Today: Proc. 27, 1258 (2020).

    Google Scholar 

  36. K.T. Thilagham and S. Muthukumaran, Mater. Today: Proc. 27, 2260 (2020).

    Google Scholar 

  37. K.T. Thilagham and S. Muthukumaran, Mater. Today: Proc. 27, 2513 (2020).

    Google Scholar 

  38. R. Saravanakumar, T. Rajasekaran, and M. Dhanasekaran, Mater. Today: Proc. 47, 7006 (2021).

    Google Scholar 

  39. A. Kar, S. Suwas, and S.V. Kailas, Mater. Sci. Eng.: A 733, 199 (2018).

    Google Scholar 

  40. J.-W. Choi, H. Liu, and H. Fujii, Mater. Sci. Eng., A 730, 168 (2018).

    Google Scholar 

  41. A. Asmare, R. Al-Sabur, and E. Messele, Metals 10(11), 1480 (2020).

    Google Scholar 

  42. X. Peng, L. Canyi, Z. Yi, and H. Tang, IEEE Trans. Neural Netw. Learn. Syst. 29(1), 218–224 (2016).

    Google Scholar 

  43. S. Bhowmik, R. Panua, D. Debroy, and A. Paul, J. Energy Resour. Technol.-Trans. ASME 139, 042201 https://doi.org/10.1115/1.4035886 (2017).

    Article  Google Scholar 

  44. A. Barakat, and P. Bianchi, SIAM J. Optim. 31(1), 244 (2021).

    MathSciNet  Google Scholar 

  45. I. Sutskever, J. Martens, G. Dahl, and G. Hinton. On the importance of initialization and momentum in deep learning, in International conference on machine learning, pp. 1139–1147. PMLR, (2013)

  46. D. Hunter, Y. Hao, M.S. Pukish III, J. Kolbusz, and B.M. Wilamowski, IEEE Trans. Ind. Inf. 8(2), 228 (2012).

    Google Scholar 

  47. Q. Wang, Y. Ma, K. Zhao, and Y. Tian, Ann. Data Sci. 9, 1 (2020).

    Google Scholar 

  48. K. He, X. Zhang, S. Ren, and J. Sun. Delving deep into rectifiers: surpassing human-level performance on imagenet classification, in Proceedings of the IEEE international conference on computer vision, pages 1026–1034, (2015)

  49. V.R. Malik, and K.K. Saxena, Adv. Mater. Process. Technol. 8, 2156 (2022).

    Google Scholar 

  50. V. Malik, and S.V. Kailas, J. Mater. Process. Technol. 258, 80 (2018).

    Google Scholar 

  51. A. Kar, S.V. Kailas, and S. Suwas, Trans. Indian Inst. Met. 72, 1533 (2019).

    Google Scholar 

  52. A. Kar, S. Malopheyev, S. Mironov, R. Kaibyshev, S. Suwas, and S.V. Kailas, Mater. Charact. 171, 110791 (2021).

    Google Scholar 

  53. A. Kar, S. Suwas, and S.V. Kailas, Metall. Mater. Trans. A. 50, 5153 (2019).

    Google Scholar 

  54. A. Kar, D.K. Yadav, S. Suwas, and S.V. Kailas, Mater. Charact. 164, 110371 (2020).

    Google Scholar 

Download references

Funding

Not applicable.

Author information

Author notes

  1. Amlan Kar and Zafar Alam have contributed equally to this work.

Authors and Affiliations

  1. Department of Mechanical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Sardar Patel Nagar, Dhanbad, Jharkhand, 826004, India

    Shrushti Maheshwari, Zafar Alam & Lalan Kumar

  2. Arbegast Materials Processing and Joining Laboratory (AMP), South Dakota School of Mines & Technology, 501 E St Joseph St, Rapid City, SD, 57701, USA

    Amlan Kar

Authors
  1. Shrushti Maheshwari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amlan Kar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zafar Alam
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lalan Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zafar Alam.

Ethics declarations

Conflict of interest

There is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix 1: Dataset 1

Sl. No.

Rotation speed (RPM)

Weld speed (mm/min)

Thickness of workpiece (mm)

Base material strength (MPa)

1

800

70

6

243

2

1600

180

3

210

3

800

70

6

327

4

1000

80

6

243

5

1000

90

3

210

6

1400

60

3

210

7

1100

60

6

94.1

8

800

40

3.42

450

9

1400

180

3

210

10

1000

90

3

210

11

1400

120

3

210

12

1600

90

3

210

13

1200

40

6

105

14

1200

150

3

210

15

900

37.5

5

310

16

1200

30

3

210

17

1600

135

6

408

18

670

60

6

408

19

1000

70

6

243

20

560

50

10

228

21

1000

80

6

327

22

800

75

3

465

23

2400

240

1.6

472

24

800

60

3

210

25

800

90

6

243

26

1600

150

3

210

27

1000

330

3

210

28

1400

150

3

210

29

1600

180

3

210

30

600

100

2

105

31

1000

100

6

408

32

1200

90

6

243

33

1000

120

3

210

34

900

50

10

228

35

1500

40

3

281

36

1100

20

6

572

37

1500

40

6.35

330

38

1600

90

3

210

39

1200

180

3

210

40

900

47.5

5

310

41

1000

60

6

408

42

1600

100

6

408

43

1000

180

3

210

44

1800

30

3

210

45

800

40

3.42

450

46

670

100

6

408

47

1200

120

3

210

48

1000

40

6.35

330

49

1600

30

3

210

50

710

63

6

310

Sl. No.

Rotation speed (RPM)

Weld speed (mm/min)

Thickness of workpiece (mm)

Base material strength (MPa)

51

800

180

3

210

52

1200

37.5

5

310

53

1000

90

3

465

54

800

40

3.42

450

55

670

135

6

408

56

1600

60

3

210

57

800

40

3.42

450

58

1000

60

3

210

59

1200

80

6

327

60

500

100

2

105

61

1800

60

3

210

62

1000

135

6

408

63

1800

120

3

210

64

1200

90

3

210

65

1200

120

3

210

66

1800

180

3

210

67

800

180

3

210

68

1200

80

6

243

69

1200

120

1.6

472

70

1200

70

6

243

71

1200

60

3

210

72

1200

90

3

210

73

700

60

6

572

74

450

40

6

310

75

800

90

3

210

76

1000

30

6.35

330

77

900

42.5

5

310

78

1600

120

3

210

79

1400

90

3

210

80

1000

120

3

210

81

1400

90

3

210

82

1400

47.5

5

310

83

700

30

10

228

84

1800

90

3

210

85

800

40

3.42

450

86

800

80

6

327

87

800

90

3

210

88

800

150

3

210

89

1200

90

6

327

90

900

30

10

228

91

700

50

10

228

92

800

150

3

210

93

560

40

6

310

94

1200

60

3

210

95

1000

60

6

408

96

1600

150

3

210

97

670

100

6

408

98

710

40

6

310

99

1500

20

6.35

330

100

560

30

10

228

Sl. No.

Rotation speed (RPM)

Weld speed (mm/min)

Thickness of workpiece (mm)

Base material strength (MPa)

101

1200

30

6.35

330

102

1800

180

1.6

472

103

1400

42.5

5

310

104

1200

30

3

210

105

1200

40

6

105

106

1000

70

6

327

107

1200

20

6.35

330

108

800

120

3

210

109

1400

30

3

210

110

560

20

6

310

111

1200

180

3

210

112

1800

30

3

210

113

710

20

6

310

114

800

80

4

497

115

1000

150

3

210

116

1800

150

3

210

117

900

40

10

228

118

450

63

6

310

119

1400

120

3

210

120

600

60

3

465

121

800

80

6

243

122

1400

60

3

465

123

1400

37.5

5

310

124

800

90

6

327

125

1000

100

4

497

126

800

120

3

210

127

800

100

2

105

128

700

100

2

105

129

700

40

6

572

130

1600

30

3

210

131

1800

90

3

210

132

670

60

6

408

133

1000

30

3

465

134

1200

150

3

210

135

700

40

10

228

136

1400

60

3

210

137

1000

180

3

210

138

900

40

5

186

139

1600

60

6

408

140

1800

120

3

210

141

1600

100

6

408

142

1600

120

3

210

143

1000

150

3

210

144

1800

180

3

210

145

1600

135

6

408

146

1600

60

3

210

147

1600

60

6

408

148

1400

30

3

210

149

800

60

3

210

150

1500

30

6.35

330

Sl. No.

Rotation speed (RPM)

Weld speed (mm/min)

Thickness of workpiece (mm)

Base material strength (MPa)

151

1800

150

3

210

152

1800

60

3

210

153

1200

47.5

5

310

154

1000

20

6.35

330

155

1200

40

6.35

330

156

1000

100

6

408

157

800

30

3

210

158

900

100

2

105

159

1200

42.5

5

310

160

1000

60

3

465

161

1000

90

6

243

162

1400

180

3

210

163

1000

60

3

210

164

1200

70

6

327

165

1000

30

3

210

166

800

45

3

465

167

670

135

6

408

168

450

20

6

310

169

1400

150

3

210

170

1000

90

6

327

171

800

30

3

210

172

560

40

10

228

Appendix 2: Dataset 2

Sl. No.

Rotation speed (RPM)

Weld speed (mm/min)

Thickness of workpiece (mm)

Base material strength (MPa)

1

1000

90

3.5

105

2

560

50

3.5

320

3

1000

90

3.5

310

4

800

40

3.5

105

5

1000

60

3.5

320

6

2400

240

3.2

105

7

1400

150

3.5

105

8

800

150

3.5

105

9

1000

150

3.5

320

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Maheshwari, S., Kar, A., Alam, Z. et al. Deep Neural Network-Based Approach for Modeling, Predicting, and Validating Weld Quality and Mechanical Properties of Friction Stir Welded Dissimilar Materials. JOM 75, 4562–4578 (2023). https://doi.org/10.1007/s11837-023-06121-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11837-023-06121-w

Search

Navigation