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The purpose of this paper is to provide a modified welding image feature extraction algorithm for rotating arc narrow gap metal active-gas welding (MAG) welding, which is significant for improving the accuracy and reliability of the welding process.

An infrared charge-coupled device (CCD) camera was utilized to obtain the welding image by passive vision. The left/right arc position was used as a triggering signal to capture the image when the arc is approaching left/right sidewall. Comparing with the conventional method, the authors’ sidewall detection method reduces the interference from arc; the median filter removes the welding spatter; and the size of the arc area was verified to reduce the reflection from welding pool. In addition, the frame loss was also considered in the authors’ method.

The modified welding image feature extraction method improves the accuracy and reliability of sidewall edge and arc position detection.

The algorithm can be applied to welding seam tracking and penetration control in rotating or swing arc narrow gap welding.

The modified welding image feature extraction method is robust to typical interference and, thus, can improve the accuracy and reliability of the detection of sidewall edge and arc position.

This paper aim to build an information fusion model that can predict the bottom shape of welding groove for better welding quality control. Arc sensor is widely used in seam tracking due to its simplicity and good accessibility, but it heavily relies on the bottom shape of the groove. It is necessary to identify the welding groove bottom state. Therefore, arc sensor information and vision sensing information were fused by the rough set (RS) method to predict the groove state, which will lay the foundation for better welding quality control.

First, a multi-sensor information system was established, which included an arc sensing component and a vision sensing component. For the arc sensing system, the current waveform in each rotating period was obtained and divided into 12 parts to calculate variables representing the variation of arc length. For the vision sensing system, images were obtained by passive vision when the arc was near the groove sidewall. The positions of the sidewall and the arc were calculated to get the weld deviation which was unrelated with the bottom groove state. Second, experimental data were generated by workpiece with various bottom shapes. At last, the RS method was adopted to fuse the arc sensing and the vision information, and a rule-based model with good prediction ability was obtained.

By fusing arc sensing and vision sensing information, an RS-based model was built to predict the welding groove state.

The RS modeling method was used to fuse arc sensing information and vision sensing information to build a model that predicts groove bottom state. The arc sensing information represented the arc length variation, while the vision sensing information contains the seam deviation which was unrelated with the bottom groove state. The RS model gives satisfactory prediction results and can be applied to weld quality control.

The purpose of this study is to establish a method for accurately extracting torch and seam features. This will improve the quality of narrow gap welding. An adaptive deflection correction system based on passive light vision sensors was designed using the Halcon software from MVtec Germany as a platform.

This paper proposes an adaptive correction system for welding guns and seams divided into image calibration and feature extraction. In the image calibration method, the field of view distortion because of the position of the camera is resolved using image calibration techniques. In the feature extraction method, clear features of the weld gun and weld seam are accurately extracted after processing using algorithms such as impact filtering, subpixel (XLD), Gaussian Laplacian and sense region for the weld gun and weld seam. The gun and weld seam centers are accurately fitted using least squares. After calculating the deviation values, the error values are monitored, and error correction is achieved by programmable logic controller (PLC) control. Finally, experimental verification and analysis of the tracking errors are carried out.

The results show that the system achieves great results in dealing with camera aberrations. Weld gun features can be effectively and accurately identified. The difference between a scratch and a weld is effectively distinguished. The system accurately detects the center features of the torch and weld and controls the correction error to within 0.3mm.

An adaptive correction system based on a passive light vision sensor is designed which corrects the field-of-view distortion caused by the camera’s position deviation. Differences in features between scratches and welds are distinguished, and image features are effectively extracted. The final system weld error is controlled to 0.3 mm.

This paper aims to develop a method to extract the weld seam from the welding image.

The initial step is to set the window for the region of the weld seam. Filter and edge‐operator are then applied to acquire edges of images. Based on the prior knowledge about characteristics of the weld seam, a series of routines is proposed to recognize the seam edges and calculate the seam representation.

The proposed method can be used to extract seams of different deviations from noise‐polluted images efficiently. Besides, the method is low time‐consuming and quick enough for real time processing.

Weld seam extraction is the key problem in passive vision based seam tracking technology. The proposed method can extract the weld seam even when the image is noisy, and it is quick enough to be applied in seam tracking technology. The method is expected to improve seam tracking results.

A useful method is developed for weld seam extraction from the noise‐polluted image based on prior knowledge of weld seam. The method is robust and quick enough for real time processing.

Reviews the Essen Schweissen und Schneiden cutting and welding trade show, identifies mounting trend for electric servo guns to replace pneumatic welding guns, reports on growing interest in aluminium welding and outlines the different solutions proposed, assesses the latest developments in remote laser welding technology.

This paper reviews the Weldex exhibition held at the NEC in Birmingham in November 2003. Highlights of the show included VirtualArc from ABB, which is able to predict weld conditions using physics of the arc, the hybrid MSG‐laser welding process from Cloos and a new spot weld‐specific Motoman robot. Several manufacturers displayed off‐the‐shelf “plug and weld” cells including Cloos, Fanuc and Autotech Robotics. Servo controlled guns were also featured by Motoman Robotics and Rexroth Bosch. The latter also promoted its ultrasonic weld monitoring systems that integrate with Rexroth's medium frequency welding systems for high quality assurance spot welding. Finally, a new laser seam tracker from Micro Epsilon is described.

This paper aims to solve the problem that the changing of groove size and assembly gap would affect the precision of the multi-pass path planning and the welding quality and realize the automatic welding of a thick plate.

First, a double-sided double arc welding (DSAW) system with a self-designed passive vision sensor was established, then the image of the groove was captured and the characteristic parameters of groove were extracted by image processing. According to the welding parameters and the extracted geometry size, multi-pass path planning was executed by the DSAW system.

A DSAW system with a self-designed passive vision sensor was established which can realize the welding thick plate by double-sided double arc by two robots. The clear welding image of the groove was acquired, and an available image processing algorithm was proposed to accurately extract the characteristic parameters of the groove. According to the welding parameters and the extracted geometry size, multi-pass path planning can be executed by the DSAW system automatically.

Gas metal arc welding is used for root welding and filler passes in DSAW. Multi-pass path planning for thick plate by Double-sided Double Arc Welding (DSAW) based on vision sensor was proposed.

In this report, accounts will be presented on the experience obtained from approximately 100 practical applications of industrial robots. The industrial robots used derive partly from the company's own production as well as from other domestic and foreign robot manufacturers.

There is a baffling number of sensors suitable for the automatic control of welding. Research at The Welding Institute (TWI) should help industry to make the right choice of sensor for a given requirement and application.

Reviews the Essen Schweissen und Schneiden cutting and welding trade show, assesses the latest developments in remote laser welding technology, identifies mounting trend for electric servo guns to replace pneumatic welding guns, reports on growing interest in aluminium welding and outlines the different solutions proposed.