A real-time quality monitoring framework for steel friction stir welding using computational intelligence

2015 ◽  
Vol 20 ◽  
pp. 137-148 ◽  
Author(s):  
Ali Baraka ◽  
George Panoutsos ◽  
Stephen Cater
Keyword(s):  
Friction Stir Welding ◽  
Real Time ◽  
Computational Intelligence ◽  
Quality Monitoring ◽  
Friction Stir ◽  
Monitoring Framework

scholarly journals Study on Quality Prediction of 2219 Aluminum Alloy Friction Stir Welding Based on Real-Time Temperature Signal

Materials ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3496
Author(s):  
Haijun Wang ◽  
Diqiu He ◽  
Mingjian Liao ◽  
Peng Liu ◽  
Ruilin Lai
Keyword(s):  
Aluminum Alloy ◽  
Friction Stir Welding ◽  
Prediction Model ◽  
Real Time ◽  
Support Vector ◽  
Weld Quality ◽  
Prediction Ability ◽  
Friction Stir ◽  
Temperature Signal ◽  
2219 Aluminum Alloy

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.

Effect of process parameters on temperature and force distribution during friction stir welding of armor-marine grade aluminum alloy

2020 ◽  
Vol 235 (1-2) ◽  
pp. 144-154 ◽  
Author(s):  
Shubham Verma ◽  
Joy Prakash Misra
Keyword(s):  
Friction Stir Welding ◽  
Real Time ◽  
Process Parameters ◽  
Tilt Angle ◽  
Welding Speed ◽  
Low Cost ◽  
Maximum Force ◽  
Maximum Temperature ◽  
Force Distribution ◽  
Friction Stir

This research investigates the effect of process parameters on real-time temperature and forces distribution during friction stir welding of AA7039. Experiments are conducted at different rotational speed, welding speed, and tilt angle conditions. For the experimentation, a low-cost real-time force-measuring fixture is indigenously developed in-house. However, eight K-type L-shaped thermocouples are used to examine the real-time temperature distribution. The forces in the z-direction are of a higher magnitude than the x-direction. The maximum force in the z-direction of 3.25 kN is witnessed for 2° tilt angle and a minimum of 2.1 kN for 26 mm/min of welding speed. The maximum force in the x-direction of 0.97 kN is obtained at 2° tilt angle and a minimum of 0.27 kN is obtained at 1.3° tilt angle. The maximum temperature of 390 °C is observed at 1812 r/min, whereas a minimum of 283 °C is observed at 43 mm/min of welding speed. The variations in temperature and force distribution during friction stir welding are also evaluated by utilizing two phenomenological models.

The use of neural network and discrete Fourier transform for real-time evaluation of friction stir welding

2011 ◽  
Vol 11 (8) ◽  
pp. 4839-4846 ◽  
Author(s):  
Enkhsaikhan Boldsaikhan ◽  
Edward M. Corwin ◽  
Antonette M. Logar ◽  
William J. Arbegast
Keyword(s):  
Neural Network ◽  
Fourier Transform ◽  
Friction Stir Welding ◽  
Real Time ◽  
Discrete Fourier Transform ◽  
Friction Stir ◽  
Time Evaluation

scholarly journals Force feedback-based quality monitoring of the friction stir welding process utilizing an analytic algorithm

2020 ◽  
Author(s):  
P. Rabe ◽  
A. Schiebahn ◽  
U. Reisgen
Keyword(s):  
Friction Stir Welding ◽  
Monitoring System ◽  
Force Feedback ◽  
Weld Seam ◽  
Position Control ◽  
Welding Process ◽  
Quality Monitoring ◽  
Welding Parameters ◽  
Process Data ◽  
Friction Stir

AbstractThe friction stir welding (FSW) process is known as a solid-state welding process, comparatively stable against external influences. Therefore, the process is commonly used with fixed welding parameters, utilizing axial force control or position control strategies. External and internal process disturbances introduced by workpiece, gap tolerance, tool wear, or machine/tool inadequacies are rarely monitored, and conclusions about the weld seam quality, based on the recorded process data, are not drawn. This paper describes an advancement, improving on research into the correlation of process force feedback events or gradual force changes and the resulting weld seam characteristics. Analyzing the correlation between examined weld sections and high-resolution rate force data, a quality monitoring system based on an analytic algorithm is described. The monitoring system is able to accurately distinguish sound welds from such with internal (void) and external (flash) defects.

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