scholarly journals Thermal Modeling and Analysis of Friction Stir Welding

2011 ◽  
Vol 1 (9) ◽  
pp. 68-70
Author(s):  
Rankit Patel ◽  
◽  
Bindu Pillai
Keyword(s):  
Friction Stir Welding ◽  
Thermal Modeling ◽  
Modeling And Analysis ◽  
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

scholarly journals Dynamic Modeling and Analysis of a Horizontal Operating 3-Axis Machine for Friction Stir Welding

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 129874-129882
Author(s):  
M. Tuffaha ◽  
A. Bazoune ◽  
F. Al-Badour ◽  
N. Merah ◽  
A. Shuaib
Keyword(s):  
Friction Stir Welding ◽  
Dynamic Modeling ◽  
Modeling And Analysis ◽  
Friction Stir

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

Analytical Modeling and Analysis of the Matter Flow during Friction Stir Welding

2019 ◽  
Vol 297 ◽  
pp. 1-16
Author(s):  
Zineelabidine Harchouche ◽  
Mokhtar Zemri ◽  
Abdelkader Lousdad
Keyword(s):  
Friction Stir Welding ◽  
Material Flow ◽  
Solid Phase ◽  
Welding Process ◽  
Computational Time ◽  
Modeling And Analysis ◽  
Friction Stir ◽  
Tool Pin ◽  
Welding Processes ◽  
Matter Flow

Friction stir welding is a solid-phase welding process based on the mixing of the pasty material in the stirred zone. The main advantage of this technique is the ability to weld metal alloys which are generally difficult to weld by conventional welding processes. In this paper an analytical model is proposed for the description in 2D the distribution of the material (fluid) flow in the vicinity of the tool pin during friction stir welding process "FSW". For this reason, the analytical solutions are built on the basis of traditional problem of mechanics of the fluids which is used to solve the equation associated with this problem. Furthermore, the aim is to make an analytical study of these aspects for a better understanding of this phenomenon. This method provides a reduction in computational time compared to those required for finite or differential elements methods. Moreover, it highlights on the effects of the different parameters on the material flow during welding.

Modeling and Analysis of Friction Stir Welding and Underwater Friction Stir Welding of Aluminium Alloy: A Review

2017 ◽  
Vol 867 ◽  
pp. 127-133 ◽  
Author(s):  
S. Shanavas ◽  
J. Edwin Raja Dhas
Keyword(s):  
Friction Stir Welding ◽  
Aluminium Alloy ◽  
Welded Joints ◽  
Energy Efficient ◽  
Modeling And Analysis ◽  
Friction Stir ◽  
Surface Method ◽  
Underwater Friction Stir Welding ◽  
Solid State Joining ◽  
Joining Process

The welding of aluminium and its alloys was a great challenge for researchers and technologists till 1991. Friction stir welding (FSW), a relatively new solid state joining process was first patented in 1991 by Thomas et. al. from ‘The welding Institute (TWI), England. Later its application found in various industries like aerospace, marine, automobile, etc. due to its high quality joints. The technique is energy efficient, ecofriendly and versatile too. In this review article, the modeling and analysis of friction stir welding and underwater friction stir welding (UFSW) of aluminium alloy are addressed. Commonly used method for modeling and analysis of welded joints such as Taguchi method and Response surface method (RSM) are considered for the review. Finally an attempt has been made to compare UFSW welded joints with FSW welded joints.

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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