scholarly journals Development of a real time quality monitoring device for resistance spot welding

2008 ◽  
Author(s):  
Yoke Rung Wong
Keyword(s):  
Real Time ◽  
Resistance Spot Welding ◽  
Spot Welding ◽  
Quality Monitoring ◽  
Monitoring Device ◽  
Resistance Spot

scholarly journals Enhancement in Quality Estimation of Resistance Spot Welding Using Vision System and Fuzzy Support Vector Machine

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1380
Author(s):  
Dima Younes ◽  
Essa Alghannam ◽  
Yuegang Tan ◽  
Hong Lu
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Real Time ◽  
Resistance Spot Welding ◽  
Spot Welding ◽  
Failure Modes ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Quality Estimation ◽  
Resistance Spot

The current nondestructive testing methods such as ultrasonic, magnetic, or eddy current signals, and even the existing image processing methods, present certain challenges and show a lack of flexibility in building an effective and real-time quality estimation system of the resistance spot welding (RSW). This paper provides a significant improvement in the theory and practices for designing a robotized inspection station for RSW at the car manufacturing plants using image processing and fuzzy support vector machine (FSVM). The weld nuggets’ positions on each of the used car underbody models are detected mathematically. Then, to collect perfect pictures of the weld nuggets on each of these models, the required end-effector path is planned in real-time by establishing the Denavit-Hartenberg (D-H) model and solving the forward and inverse kinematics models of the used six-degrees of freedom (6-DOF) robotic arm. After that, the most frequent resistance spot-welding failure modes are reviewed. Improved image processing methods are employed to extract new features from the elliptical-shaped weld nugget’s surface and obtain a three-dimensional (3D) reconstruction model of the weld’s surface. The extracted artificial data of thousands of samples of the weld nuggets are divided into three groups. Then, the FSVM learning algorithm is formed by applying the fuzzy membership functions to each group. The improved image processing with the proposed FSVM method shows good performance in classifying the failure modes and dealing with the image noise. The experimental results show that the improvement of comprehensive automatic real-time quality evaluation of RSW surfaces is meaningful: the quality estimation could be processed within 0.5 s in very high accuracy.

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