Finite Element Modelling and Updating of Friction Stir Welding (FSW) Joint for Vibration Analysis

• Main Authors: Siti Norazila, Zahari, M. S. M., Sani, M., Ishak
• Format: Conference or Workshop Item
• Language: English; English
• Published: EDP Sciences 2017
• Subjects: TJ Mechanical engineering and machinery
• Online Access: http://umpir.ump.edu.my/id/eprint/14370/1/Finite%20Element%20Modelling%20and%20Updating%20of%20Friction%20Stir%20Welding%20%28FSW%29%20Joint%20for%20Vibration%20Analysis.pdf; http://umpir.ump.edu.my/id/eprint/14370/7/fkm-2017-shahrir-Finite%20element%20modelling.pdf; http://umpir.ump.edu.my/id/eprint/14370/; http://dx.doi.org/10.1051/matecconf/20179001021

Friction stir welding of aluminium alloys widely used in automotive and aerospace application due to its advanced and lightweight properties. The behaviour of FSW joints plays a significant role in the dynamic characteristic of the structure due to its complexities and uncertainties therefore the representation of an accurate finite element model of these joints become a research issue. In this paper, various finite elements (FE) modelling technique for prediction of dynamic properties of sheet metal jointed by friction stir welding will be presented. Firstly, nine set of flat plate with different series of aluminium alloy; AA7075 and AA6061 joined by FSW are used. Nine set of specimen was fabricated using various types of welding parameters. In order to find the most optimum set of FSW plate, the finite element model using equivalence technique was developed and the model validated using experimental modal analysis (EMA) on nine set of specimen and finite element analysis (FEA). Three types of modelling were engaged in this study; rigid body element Type 2 (RBE2), bar element (CBAR) and spot weld element connector (CWELD). CBAR element was chosen to represent weld model for FSW joints due to its accurate prediction of mode shapes and contains an updating parameter for weld modelling compare to other weld modelling. Model updating was performed to improve correlation between EMA and FEA and before proceeds to updating, sensitivity analysis was done to select the most sensitive updating parameter. After perform model updating, total error of the natural frequencies for CBAR model is improved significantly.