Thermal Modeling Based on the Energy per Unit Length of Weld in Friction Stir Welding

2018 ◽  
Vol 13 (7) ◽  
pp. 1019-1027 ◽  
Author(s):  
Qipeng Liu ◽  
Naijian Gu ◽  
Yuehua Gao ◽  
Xinhua Yang
Keyword(s):  
Friction Stir Welding ◽  
Unit Length ◽  
Thermal Modeling ◽  
Friction Stir

scholarly journals Impact of Friction Stir Welding (FSW) Process Parameters on Thermal Modeling and Heat Generation of Aluminum Alloy Joints

2016 ◽  
Vol 29 (9) ◽  
pp. 869-883 ◽  
Author(s):  
Saad B. Aziz ◽  
Mohammad W. Dewan ◽  
Daniel J. Huggett ◽  
Muhammad A. Wahab ◽  
Ayman M. Okeil ◽  
...  
Keyword(s):  
Aluminum Alloy ◽  
Friction Stir Welding ◽  
Heat Generation ◽  
Process Parameters ◽  
Thermal Modeling ◽  
Friction Stir

Thermal Modeling of Overlap Friction Stir Welding for Al-Alloys

2001 ◽  
Vol 10 (2) ◽  
pp. 91-105 ◽  
Author(s):  
Mir Zahedul H Khandkar ◽  
Jamil A Khan
Keyword(s):  
Friction Stir Welding ◽  
Thermal Modeling ◽  
Al Alloys ◽  
Friction Stir

Friction Stir Welding of Sc-Modified Al-Zn-Mg-Cu Alloy Extrusions

2008 ◽  
Author(s):  
Carter Hamilton ◽  
Andrew Sommers ◽  
Oleg Senkov
Keyword(s):  
Friction Stir Welding ◽  
Thermal Model ◽  
Elevated Temperatures ◽  
Unit Length ◽  
Empirical Relationship ◽  
Solidus Temperature ◽  
Welding Temperature ◽  
Friction Stir ◽  
Cu Alloy ◽  
Residual Properties

Small additions of scandium to Al-Zn-Mg-Cu 7000 series alloys can significantly improve mechanical properties and augment the strength retention at low and elevated temperatures. This research program evaluates the residual properties of Sc-modified Al-Zn-Mg-Cu alloy extrusions joined through friction stir welding (FSW). Mechanical and corrosion testing were performed on the baseline material and on panels friction stir welded at 175, 225, 250, 300, 350 and 400 RPM (all other weld parameters held constant). A thermal model of friction stir welding is developed that utilizes an energy-based scaling factor to account for tool slip. The proposed slip factor is derived from an observed, empirical relationship between the ratio of the maximum welding temperature to the solidus temperature and energy per unit length of weld. The thermal model successfully predicts the maximum welding temperatures over a range of energy levels, and the mechanical and corrosion behavior is correlated to the temperature distribution predicted by the model.

Thermal Modeling for Control of Friction Stir Welding Process in Automated Manufacturing

2011 ◽  
Author(s):  
Iraj Mantegh
Keyword(s):  
Neural Network ◽  
Friction Stir Welding ◽  
Weld Seam ◽  
Control Method ◽  
Thermal Modeling ◽  
Welding Process ◽  
Transfer Model ◽  
Friction Stir ◽  
Friction Stir Welding Process ◽  
Joining Process

Friction stir welding is a patented joining process invented in 1991 at The Welding Institute in Cambridge, UK, and further developed to the stage suitable for production. In this process, a wear resistant rotating tool is used to join sheet and plate with different materials such as aluminum, copper, lead, magnesium, zinc, and titanium. This work studies the thermal characteristics of this process and provides a modeling technique based on Neural Network that can be used for real-time control. A thermal feed-back control method is presented to control the process. Using some thermal modeling for the heat distribution during friction stir welding process, this paper displays the complexity of obtaining an accurate design for the thermal feed back control. A three-dimensional transient heat transfer model is developed here for a sequential joining process (Friction Stir Welding-FSW) applied on aluminum parts. A neural network is created based on a set of experiments to predict the spatial and temporal variations in the temperature over the weld seam for different set of input variables. The model includes the dynamic and friction behavior of the rotating spindle and the thermal behaviors of the weld components involved. The significance of this modeling approach is that it captures the movement of the spindle, simulating a sequential joining process along a continuous weld seam. The modeling results are compared with experimental data obtained by thermocouples and infrared camera, and accurately predict the trend of variations in weld temperature. A fuzzy-logic based controller is proposed to regulate the FSW process parameters to maintain the weld temperature within the margin required to ensure the weld quality. This modeling and control system can have applications in manufacturing aluminum parts in automotive and aerospace industry.

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