EFFECT OF FIT-UP CONDITIONS WHEN USING CONSTANT CURRENT CONTROL SYSTEMS FOR RESISTANCE SPOT WELDING

1993 ◽  
Author(s):  
Jerry E. Gould ◽  
Menachem Kimchi ◽  
Tom Mitchell
Keyword(s):  
Control Systems ◽  
Resistance Spot Welding ◽  
Spot Welding ◽  
Current Control ◽  
Constant Current ◽  
Resistance Spot

Experimental Characterization of MFDC/CHC Resistance Spot Welding

2005 ◽  
Author(s):  
Wei Li ◽  
Daniel Cerjanec ◽  
Gerald A. Grzadzinski
Keyword(s):  
Resistance Spot Welding ◽  
Spot Welding ◽  
New Technology ◽  
Welding Process ◽  
Current Control ◽  
Constant Current ◽  
Dramatic Improvement ◽  
Resistance Spot ◽  
Target Energy

Resistance spot welding is widely used for joining sheet metal materials in the automotive industry. Most of the existing resistance spot welding systems are single-phase AC-based, and with constant current control. The medium frequency DC (MFDC) system with constant heat control (CHC) is new technology in the resistance spot welding industry. This system promises dramatic improvement on the spot weld quality. However, many of the CHC features are still not known and need to be explored. This paper presents an experimental study on a new MFDC/CHC spot welding system. The goal is to characterize the MFDC/CHC welding process in terms of the process parameters, including the target energy level, force, and welding time. Real time process signals such as force, current, and tip voltage were also collected and analyzed to compare with the traditional spot welding process.

Advanced Resistance Spot Welding Technologies: New Machine, Adaptive Control and FEM Simulation

2008 ◽  
Vol 580-582 ◽  
pp. 367-370 ◽  
Author(s):  
Yong Joon Cho ◽  
In Sung Chang ◽  
Heui Bom Lee
Keyword(s):  
Adaptive Control ◽  
Resistance Spot Welding ◽  
Spot Welding ◽  
Advanced Technology ◽  
Constant Current ◽  
Automotive Application ◽  
Application Process ◽  
Current Regulation ◽  
Resistance Spot ◽  
Welding Machine

While resistance spot welding (RSW) has been the most successful sheet metal joining process in automotive industry, there are still certain quality and reliability issues due to the control system and its application process. Some weld spots have hard-to-reach areas and new materials make the process more complicated resulting in new challenges for quality welds. Recently, a new welding machine called one-sided RSW was introduced to make a weld of hard-to-reach areas in automotive application. Intelligent current regulation algorithm of welding machine timer helps to control welding current and time with more stablility than conventional constant current regulation. Newly developed RSW simulation tool is another advanced technology to improve weld quality and production stability. In this research, advanced RSW technologies, including one-sided RSW, intelligent adaptive control, and simulation of RSW process, are discussed with various automotive applications.

SIMULATION AND DESIGN OF A CONSTANT-CURRENT-CONTROLLED SPOT WELDING INVERTER WITH THE FUZZY NEURAL NETWORK

2011 ◽  
Vol 20 (05) ◽  
pp. 821-834
Author(s):  
YONG ZHANG ◽  
HUA BAI ◽  
YONGQING CAI ◽  
TIEJUN MA ◽  
HONGXIA XIE
Keyword(s):  
Neural Network ◽  
Resistance Spot Welding ◽  
Spot Welding ◽  
Fuzzy Neural Network ◽  
Constant Current ◽  
Duty Ratio ◽  
Resistance Spot ◽  
Feed Forward Network ◽  
Fuzzy Neural ◽  
Welding Inverter

Resistance spot welding is a major metal connecting method in vehicle and other domestic electronic domains. Among all the welding techniques, the spot welding inverter is an important direction at the present time. The high nonlinearity and strongly coupled multiple parameters in the resistance spot welding process challenge the classical control theory based on some specific conditions and ideal assumptions, which in real practice obstacle the high-quality welding. This paper put the fuzzy neural network into a constant-current-controlled spot welding inverter, where the welding current peak and its variation are adopted as the input parameters and the duty ratio of the switches is regarded as the output. Eventually a five-layer feed-forward network was constructed, back propagation (BP) algorithm was applied to revise the adjustable parameters in the network, and a mathematical model was established to obtain the training samples serving for the network. The ultimate precision could reach 1.75%, the relative control error is 2.28% with strong external disturbances, the overmodulation is 3.35%, and the total modulating period is seven switching period, which indicated that the proposed algorithm has good performance.

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