Quality assessment of friction-stir-welded aluminum alloy welds via three-dimensional force signals

Ji-Hong Dong , Yi-Ming Huang , Jia-Lei Zhu , Wei Guan , Xu-Kai Ren , Huan-Wei Yu , Lei Cui

Advances in Manufacturing ›› 2024, Vol. 12 ›› Issue (1) : 61 -75.

PDF
Advances in Manufacturing ›› 2024, Vol. 12 ›› Issue (1) : 61 -75. DOI: 10.1007/s40436-023-00452-2
Article

Quality assessment of friction-stir-welded aluminum alloy welds via three-dimensional force signals

Author information +
History +
PDF

Abstract

An online detection technology must be developed for realizing the real-time control of friction stir welding. In this study, the three-dimensional force exerted on a material during friction stir welding was collected synchronously and the relationship between the forces and welding quality was investigated. The results indicated that the fluctuation period of the traverse force was equal to that of the lateral force during the stable welding stage. The phase difference between two horizontal forces was π/2. The values of the horizontal forces increased with welding speed, whereas their amplitudes remained the same. The proposed force model showed that the traverse and lateral forces conformed to an elliptical curve, and this result was consistent with the behavior of the measured data. The variational mode decomposition was used to process the plunge force. The intrinsic mode function that represented the real fluctuation in the plunge force varied at the same frequency as the spindle rotational speed. When tunnel defects occurred, the fluctuation period features were consistent with those obtained during normal welding, whereas the ratio parameter defined in this study increased significantly.

Keywords

Friction stir welding (FSW) / Three-dimensional force model / Variational mode decomposition / Tunnel defect

Cite this article

Download citation ▾
Ji-Hong Dong, Yi-Ming Huang, Jia-Lei Zhu, Wei Guan, Xu-Kai Ren, Huan-Wei Yu, Lei Cui. Quality assessment of friction-stir-welded aluminum alloy welds via three-dimensional force signals. Advances in Manufacturing, 2024, 12(1): 61-75 DOI:10.1007/s40436-023-00452-2

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Huang Y, Yuan Y, Yang L, et al. Real-time monitoring and control of porosity defects during arc welding of aluminum alloys. J Mater Process Technol, 2020, 286: 116832.

[2]

Huang Y, Wu D, Zhang Z, et al. EMD-based pulsed TIG welding process porosity defect detection and defect diagnosis using GA-SVM. J Mater Process Technol, 2017, 239: 92-102.

[3]

Wang G, Zhao Y, Hao Y. Friction stir welding of high-strength aerospace aluminum alloy and application in rocket tank manufacturing. J Mater Sci Technol, 2018, 34: 73-91.

[4]

Rajendran C, Srinivasan K, Balasubramanian V, et al. Effect of tool tilt angle on strength and microstructural characteristics of friction stir welded lap joints of AA2014-T6 aluminum alloy. Trans Nonferrous Met Soc China, 2019, 29(9): 1824-1835.

[5]

Ambrosio D, Dessein G, Wagner V, et al. On the potential applications of acoustic emission in friction stir welding. J Manuf Process, 2022, 75: 461-475.

[6]

Mishra D, Roy RB, Dutta S, et al. A review on sensor based monitoring and control of friction stir welding process and a roadmap to industry 4.0. J Manuf Process, 2018, 36: 373-397.

[7]

Chen S, Li H, Lu S, et al. Temperature measurement and control of bobbin tool friction stir welding. Int J Adv Manuf Technol, 2015, 86: 337-346.

[8]

Chen C, Kovacevic R, Jandgric D. Wavelet transform analysis of acoustic emission in monitoring friction stir welding of 6061 aluminum. Int J Mach Tools Manuf, 2003, 43: 1383-1390.

[9]

Soundararajan V, Valant M, Kovacevic R, et al. An overview of R&D work in friction stir welding at SMU. Metall Mater Eng, 2018, 12(4): 516-520.

[10]

Franke D, Rudraraju S, Zinn M, et al. Understanding process force transients with application towards defect detection during friction stir welding of aluminum alloys. J Manuf Process, 2020, 54: 251-261.

[11]

Sahu SK, Mishra D, Pal K, et al. Multi sensor based strategies for accurate prediction of friction stir welding of polycarbonate sheets. Proc Inst Mech Eng Part C J Mech Eng Sci, 2020, 235: 3252-3272.

[12]

Roy RB, Ghosh A, Bhattacharyya S, et al. Weld defect identification in friction stir welding through optimized wavelet transformation of signals and validation through X-ray micro-CT scan. Int J Adv Manuf Technol, 2018, 99: 623-633.

[13]

Mishra D, Gupta A, Raj P, et al. Real time monitoring and control of friction stir welding process using multiple sensors. CIRP J Manuf Sci Technol, 2020, 30: 1-11.

[14]

Trimble D, Monaghan J, O’Donnell GE. Force generation during friction stir welding of AA2024-T3. CIRP Annals, 2012, 61: 9-12.

[15]

Banik A, Deb Barma J, Saha SC. Effect of threaded pin tool for friction stir welding of AA6061-T6 at varying traverse speeds: torque and force analysis. Iran J Sci Technol Trans Mech Eng, 2019, 44: 749-764.

[16]

Guan W, Li D, Cui L, et al. Detection of tunnel defects in friction stir welded aluminum alloy joints based on the in-situ force signal. J Manuf Process, 2021, 71: 1-11.

[17]

Dong J, Huang Y, Zhu J, et al. Variation mechanism of three-dimensional force and force-based defect detection in friction stir welding of aluminum alloys. Materials, 2023, 16: 1312.

[18]

Dragomiretskiy K, Zosso D. Variational mode decomposition. IEEE Trans Signal Process, 2013, 62: 531-544.

[19]

Huang Y, Hou S, Xu S, et al. EMD-PNN based welding defects detection using laser-induced plasma electrical signals. J Manuf Process, 2019, 45: 642-651.

[20]

Heidarzadeh A, Mironov S, Kaibyshev R, et al. Friction stir welding/processing of metals and alloys: a comprehensive review on microstructural evolution. Prog Mater Sci, 2021, 117: 100752.

[21]

Meng X, Huang Y, Cao J, et al. Recent progress on control strategies for inherent issues in friction stir welding. Prog Mater Sci, 2021, 115: 100706.

[22]

Nandan R, DebRoy T, Bhadeshia H. Recent advances in friction-stir welding-process, weldment structure and properties. Prog Mater Sci, 2008, 53: 980-1023.

Funding

China Postdoctoral Science Foundation(2020M670651)

National Natural Science Foundation of China http://dx.doi.org/10.13039/501100001809(52075376)

AI Summary AI Mindmap
PDF

186

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/