Intergranular Corrosion Following Friction Stir Welding of Aluminum Alloy 7075-T651

CORROSION ◽  
1999 ◽  
Vol 55 (12) ◽  
pp. 1127-1135 ◽  
Author(s):  
J. B. Lumsden ◽  
M. W. Mahoney ◽  
G. Pollock ◽  
C. G. Rhodes
Author(s):  
Lihua Gong ◽  
Weimin Guo ◽  
Yang Li

Abstract The intergranular corrosion behavior of 6061 aluminum alloy welded joints produced by metal inert gas welding and friction stir welding was studied. The microstructure of the welded joints and the intergranular corrosion morphology of the cross-section were analyzed by optical microscopy. The results show that the most sensitive area of intergranular corrosion is the partially melted zone of the metal inert gas welding, and the maximum corrosion depth is about seven times that of the base metal, followed by the unmixed zone. The heat affected zone has the lowest sensitivity. Although the welding seam corroded seriously, general corrosion played a leading role. With the high heat input of metal inert gas welding, the sensitivity to intergranular corrosion in the partially melted zone increased significantly, while other zones had little change. For friction stir welding joints, the heat affected zone suffered from the most severe corrosion, and the nugget zone the least. However, the difference is not apparent. The susceptibility to intergranular corrosion of friction stir welding joints is weaker than that of metal inert gas welding joints but more severe than the base metal.


2014 ◽  
Vol 57 ◽  
pp. 146-155 ◽  
Author(s):  
Yong Zhao ◽  
Lilong Zhou ◽  
Qingzhao Wang ◽  
Keng Yan ◽  
Jiasheng Zou

Materials ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3496
Author(s):  
Haijun Wang ◽  
Diqiu He ◽  
Mingjian Liao ◽  
Peng Liu ◽  
Ruilin Lai

The online prediction of friction stir welding quality is an important part of intelligent welding. In this paper, a new method for the online evaluation of weld quality is proposed, which takes the real-time temperature signal as the main research variable. We conducted a welding experiment with 2219 aluminum alloy of 6 mm thickness. The temperature signal is decomposed into components of different frequency bands by wavelet packet method and the energy of component signals is used as the characteristic parameter to evaluate the weld quality. A prediction model of weld performance based on least squares support vector machine and genetic algorithm was established. The experimental results showed that, when welding defects are caused by a sudden perturbation during welding, the amplitude of the temperature signal near the tool rotation frequency will change significantly. When improper process parameters are used, the frequency band component of the temperature signal in the range of 0~11 Hz increases significantly, and the statistical mean value of the temperature signal will also be different. The accuracy of the prediction model reached 90.6%, and the AUC value was 0.939, which reflects the good prediction ability of the model.


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