Real-Time Robotic Control System for Titanium Gas Metal Arc Welding

2005 ◽  
Author(s):  
Robert Kline-Schoder
Keyword(s):  
Control System ◽  
Real Time ◽  
Arc Welding ◽  
Gas Metal Arc Welding ◽  
Robotic Control ◽  
Metal Arc Welding

Real-time sensing of gas metal arc welding process – A literature review and analysis

2021 ◽  
Vol 70 ◽  
pp. 452-469
Author(s):  
Yongchao Cheng ◽  
Rui Yu ◽  
Quan Zhou ◽  
Heming Chen ◽  
Wei Yuan ◽  
...  
Keyword(s):  
Literature Review ◽  
Real Time ◽  
Arc Welding ◽  
Gas Metal Arc Welding ◽  
Welding Process ◽  
Metal Arc Welding ◽  
Arc Welding Process

Real-time sensing and monitoring in robotic gas metal arc welding

2006 ◽  
Vol 18 (1) ◽  
pp. 303-310 ◽  
Author(s):  
C S Wu ◽  
J Q Gao ◽  
J K Hu
Keyword(s):  
Real Time ◽  
Arc Welding ◽  
Gas Metal Arc Welding ◽  
Metal Arc Welding

Virtual Reality Welder Training

2006 ◽  
Vol 22 (03) ◽  
pp. 126-138 ◽  
Author(s):  
Nancy C. Porter ◽  
J. Allan Cote ◽  
Timothy D. Gifford ◽  
Wim Lam
Keyword(s):  
Neural Network ◽  
Virtual Reality ◽  
Real Time ◽  
Arc Welding ◽  
Gas Metal Arc Welding ◽  
Travel Speed ◽  
Virtual Reality Technology ◽  
Metal Arc Welding ◽  
Welding Torch

Based on the orientation and travel speed of a welding torch, virtual reality technology simulates gas metal arc welding in near-real time using a neural network.

Dynamic Modeling and Adaptive Control of the Gas Metal Arc Welding Process

1994 ◽  
Vol 116 (3) ◽  
pp. 405-413 ◽  
Author(s):  
Jae-Bok Song ◽  
David E. Hardt
Keyword(s):  
Adaptive Control ◽  
Control System ◽  
Arc Welding ◽  
Gas Metal Arc Welding ◽  
Welding Process ◽  
Weld Bead ◽  
Adaptive Control System ◽  
Bead Width ◽  
Metal Arc Welding ◽  
Arc Welding Process

Control of the welding process is a very important step in welding automation. Since the welding process is complex and highly nonlinear, it is very difficult to accurately model the process for real-time control. In this research, a discrete-time transfer function matrix model for gas metal arc welding process is proposed. This empirical model takes the common dynamics for each output and inherent process and measurement delays into account. Although this linearized model is valid only around the operating point of interest, the adaptation mechanism employed in the control system render this model useful over a wide operating range. Since welding is inherently a nonlinear and multi-input, multi-output process, a multivariable adaptive control system is used for high performance. The process outputs considered are weld bead width and depth, and the process inputs are chosen as the travel speed of the torch and the heat input. A one-step-ahead (or deadbeat) adaptive control algorithm combined with a recursive least-squares methods for on-line parameter estimation is implemented in order to achieve the desired weld bead geometries. Control weighting factors are used to maintain the stability and reduce excessive control effort. Some guidelines for the control design are also suggested. Command following and disturbance rejection properties of the adaptive control system for both SISO and MIMO cases are investigated by simulation and experiment. Although a truly independent control of the outputs is difficult to implement because of a strong output coupling inherent in the process, a control system for simultaneous control of bead width and depth was successfully implemented.

Virtual Reality Welder Training

2005 ◽  
Author(s):  
Nancy C. Porter ◽  
J. Allan Cote ◽  
Timothy D. Gifford ◽  
Wim Lam
Keyword(s):  
Neural Network ◽  
Virtual Reality ◽  
Real Time ◽  
Arc Welding ◽  
Gas Metal Arc Welding ◽  
Travel Speed ◽  
Virtual Reality Technology ◽  
Metal Arc Welding ◽  
Welding Torch

Based on the orientation and travel speed of a welding torch, virtual reality technology simulates gas metal arc welding in near-real time using a neural network.

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