Real-Time Quality Estimation of Resistance Spot Welding Based on Electrode Displacement Characteristics and HMM

2009 ◽  
Author(s):  
Xianfeng Wang ◽  
Guoxiang Meng ◽  
Qian Ye ◽  
Wenhua Xie ◽  
Zhengjin Feng
Keyword(s):  
Real Time ◽  
Resistance Spot Welding ◽  
Spot Welding ◽  
Quality Estimation ◽  
Resistance Spot ◽  
Electrode Displacement

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.

Quality Estimation of the Resistance Spot Welding Based on Hopfield Network

2011 ◽  
Vol 189-193 ◽  
pp. 3364-3369
Author(s):  
Hong Jie Zhang ◽  
Yan Yan Hou
Keyword(s):  
Resistance Spot Welding ◽  
Spot Welding ◽  
Fuzzy Theory ◽  
Welding Process ◽  
Hopfield Network ◽  
Quality Estimation ◽  
Resistance Spot ◽  
Displacement Signal ◽  
Electrode Displacement ◽  
Estimation System

Lots of dynamic information, which can directly or indirectly reflect the quality of welded spot, is included within the electrode displacement signal of resistance spot welding process. In this research, the displacement signal is monitored and mapped into a 15×25 element bipolarized matrix by means of some method of fuzzy theory. Some welded spots are classified into five classes according to the prototype pattern matrixes. An effective RSW quality estimation system is developed based on Hopfield network when taking the tensile shear strength of the welded spot joint as the estimation index of welded spot quality. The results of cross-validation test shows that the Hopfield network can satisfactorily accomplish the task of classification of the welded spot and has board application prospects.

Dynamic Electrode Force and Displacement in Resistance Spot Welding of Aluminum

2004 ◽  
Vol 126 (3) ◽  
pp. 605-610 ◽  
Author(s):  
C. T. Ji, ◽  
Y. Zhou,
Keyword(s):  
Resistance Spot Welding ◽  
Spot Welding ◽  
Welding Current ◽  
Monitoring And Control ◽  
Resistance Spot ◽  
Nugget Diameter ◽  
Electrode Force ◽  
Rate Of Increase ◽  
Process Monitoring And Control ◽  
Electrode Displacement

Dynamic electrode displacement and force were characterized during resistance spot welding of aluminum alloy 5182 sheets using a medium-frequency direct-current welder. It was found that both electrode displacement and force increased rapidly at the beginning of the welding stage and then at a reducing rate. Rates of increase in electrode displacement and force were both proportional to welding current. And both electrode displacement and force experienced a sudden drop when weld metal expulsion occurred. However, the rate of increase in electrode displacement did not reach zero during welding even for joints with sufficient nugget diameter, while electrode force peaked when a large nugget diameter was produced. Possible strategies for process monitoring and control were also discussed.

Expulsion Intensity Monitoring and Modeling in Resistance Spot Welding Based on Electrode Displacement Signals

2020 ◽  
Author(s):  
Yu-Jun Xia ◽  
Yan Shen ◽  
Lang Zhou ◽  
Yong-Bing Li
Keyword(s):  
Resistance Spot Welding ◽  
Spot Welding ◽  
Low Carbon Steel ◽  
Sheet Thickness ◽  
High Strength Steels ◽  
High Strength ◽  
Material Strength ◽  
Low Carbon ◽  
Resistance Spot ◽  
Electrode Displacement

Abstract Weld expulsion is one of the most common welding defects during resistance spot welding (RSW) process especially for high strength steels (HSS). In order to control and eventually eliminate weld expulsion in production, accurate assessment of the expulsion severity should be the first step and is urgently required. Among the existing methods, real-time monitoring of RSW-related process signals has become a promising approach to actualize the online evaluation of weld expulsion. However, the inherent correlation between the process signals and the expulsion intensity is still unclear. In this work, a commonly used process signal, namely the electrode displacement and its instantaneous behavior when expulsion occurs are systematically studied. Based upon experiments with various electrodes and workpieces, a nonlinear relation between the weight of expelled metal and the sudden displacement drop accompanied by the occurrence of weld expulsion is observed, which is mainly influenced by electrode tip geometry but not by material strength or sheet thickness. The intrinsic relationship between this specific signal feature and the magnitude of expulsion is further explored through geometrical analysis, and a modified analytical model for online expulsion evaluation is finally proposed. It is shown that the improved model could be applied to domed electrodes with different tip geometries and varying workpieces ranging from low carbon steel to HSS. The error of expulsion estimation could be limited within ±20.4 mg (±2σ) at a 95% confidence level. This study may contribute to the online control of weld expulsion to the minimum level.

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