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The purpose of this paper is to describe work aimed to control the Al alloy welding penetration through the passive vision for welding robot.

First a passive vision system was established. The system can capture the Al alloy welding image. Based on the analysis of the characteristic of the welding image, the composite edge detectors were developed to recognize the shape of the weld seam and the weld pool. To realize the automatic control of the Al alloy‐weld process, the relation between the welding parameter and the quality of the weld appearance was established through the random welding experiment. The wire feed was chosen with PID controller adjusting the wire feed rate according to the weld gap variation.

This paper finds that the passive vision system can be captured the clear weld seam and weld pool image simultaneously. the method of composite edge detectors can be effectively and accurately recognize the weld seam edges. The wire feed rate controller ensured the welding robot to adjust the wire feed rate according to the gap variation.

This system has been applied to the industrial welding robot production.

The weld seam and weld pool image can be simultaneously captured by the passive vision system. The composite edge detectors have been developed for the passive vision method. The controller has been set up for Al alloy welding process based on the neural network.

The purpose of this paper was to use visual and arc sensors to simultaneously obtain the underwater wet welding information, and a weld seam-forming model was made to predict the weld seam's geometric parameters. It is difficult to obtain a fine welding quality in underwater welding because of the intense disturbances of the water environment. To automatically control the welding quality, the weld seam-forming model should first be established. Thus, the foundation was laid for automatically controlling the underwater welding seam-forming quality.

Visual and arc sensors were used simultaneously to obtain the weld seam image, current and voltage information; then signal algorithms were used to process the information, and the back propagation (BP) neural network was used to model the process.

Experiment results showed that the BP neural network model could precisely predict the weld seam-forming parameters of underwater wet welding.

A weld seam-forming model of underwater wet welding process was made; this laid the foundation for establishing a controller for controlling the underwater wet welding process automatically.

Reports on an aggressive project to develop an advanced, automated welding system, being completed at Babcock & Wilcox, CIM Systems. This system, the programmable automated welding system (PAWS), involves the integration of both planning and control technologies to address the needs of small batch robotic welding operations. PAWS is specifically designed to provide an automated means of planning, controlling, and evaluating critical welding situations in shipyard environments to improve productivity and quality. Five varieties (wall, lathe, floor mount, cantilevered, and gantry) of PAWS welding systems currently exist.

Welding process is a complicated process influenced by many interference factors, a single sensor cannot get information describing welding process roundly. This paper simultaneously uses different sensors to get different information about the welding process, and uses multi‐sensor information fusion technology to fuse the different information. By using multi‐sensors, this paper aims to describe the welding process more precisely.

Electronic and welding pool image information are, respectively, obtained by arc sensor and image sensor, then electronic signal processing and image processing algorithms are used to extract the features of the signals, the features are then fused by neural network to predict the backside width of weld pool.

Comparative experiments show that the multi‐sensor fusion technology can predict the weld pool backside width more precisely.

The multi‐sensor fusion technology is used to fuse the different information obtained by different sensors in a gas tungsten arc welding process. This method gives a new approach to obtaining information and describing the welding process.

Tube welding in the modern era of space applications requires a very high degree of quality in addition to productivity. Modern techniques in automated tube welding provide the tools necessary in today's requirement for high degree of control of quality in production. These tools are a by‐product of performing the automated process via computer control. Since all the pertinent parameters are resident within the computer memory, a reformating and recording of a current welding status and deviations from preprogramed commands is readily available. The nature of cyber‐based controlled systems provides production and quality control management with the tools necessary to carefully control the welding parameters that go into automated welding of tubing. Consequently, the detailed logging of the welding process is available for a post‐weld report. Most problems that occur in producing a quality product are absence of management tools to completely control the welding process. This is because of the nature or manner in which the welding process is performed by the operator and the quality procedures that are followed for recognizing problems and identifying the cause(s) of the problems. The following paper provides the techniques that are currently available for use in managing such production problems and successful case histories in the implementation of these cyber‐based tools.

Shielded metal arc welding (SMAW) is a typical manual process with many important but dangerous applications for the welder. The purpose of this paper is to present a methodology developed for execution time trajectory generation for robotic SMAW which offers greater safety and improved weld quality and repeatability.

The study presents a methodology developed for execution time trajectory generation for the robotic SMAW. In this methodology, while the electrode is melted the robot makes the diving movement, keeping the electric arc length constant. The trajectory is generated during execution time as a function of melting rate and independent of the welding speed, given by the welding parameters. The proposed methodology uses a variable tool center point (TCP) model where the covered electrode is considered a prismatic joint, whose displacement is determined by the melting rate.

The proposed methodology was implemented in a KUKA robot. The electrode melting rate was determined by measuring the arc voltage and the electrode holder trajectory was determined during the weld, keeping the arc length and the welding speed constant. All the obtained weld beads have the same aspect, showing the process repeatability.

Owing to its low productivity, robotic SMAW is only suitable to certain applications.

With this methodology, the TCP will always be located at the tip of the electrode (melting front), allowing one to program the welding speed independently of the electrode diving speed. The diving movement is automatically performed by the robot during the welding.

Robotic SMAW allows dangerous applications such as underwater welding and hot tapping of pipes without human intervention during the weld.

The purpose of this study is to reduce the cracks, pores and unfused defects in arc welding, improve the crystalline structure of the weld, refine its grains and improve the mechanical properties.

Taking E690 marine steel as the research object, the experiment adopts a new process method of laser forging coupled arc welding. Welding for comparative experiments. Experiments show that the “V”-shaped groove arc welding process has a larger fusion area, but has pores, the arc current is 168 A, the arc voltage is 28 V and the welding speed is 600 mm/min.

It can be seen from tensile tests that the coupling welding process has the highest tensile strength and yield strength, 872 MPa and 692 MPa, respectively, and the fracture elongation is 29.29%. The single-beam laser forging coupled arc welding process has a distance of laser and wire of 6–8 mm, a laser wavelength of 1,064 nm and the highest weld fusion ratio. The microhardness test shows that the average hardness of single-beam laser forging in the weld zone is 487.54 HV, which is 10.30% higher than that of arc welding. The average hardness in the fusion zone is 788.08 HV, which is 14.52% higher than that of the arc welding process.

The originality of the experiment: proposed a new process method of coupling arc repair for offshore steel forging; adopted a new process method of simultaneous coupling of single-beam short-pulse laser, double-beam short-pulse laser and arc welding; and obtained effect of pulsed laser and arc composite repair on porosity and fusion of E690 marine steel welds.

The purpose of this paper is to analyze the technology of capturing and processing weld images in real‐time, which is very important to the seam tracking and the weld quality control during the robotic gas tungsten arc welding (GTAW) process.

By analyzing some main parameters on the effect of image capturing, a passive vision sensor for welding robot was designed in order to capture clear and steady welding images. Based on the analysis of the characteristic of the welding images, a new improved Canny algorithm was proposed to detect the edges of seam and pool, and extract the seam and pool characteristic parameters. Finally, the image processing precision was verified by the random welding experiments.

It was found that the seam and pool images can be clearly acquired by using the passive vision system, and the welding image characteristic parameters were accurately extracted through processing. The experiment results show that the precision range of the image processing can be controlled about within ±0.169 mm, which can completely meet the requirement of real‐time seam tracking for welding robot.

This system will be applied to the industrial welding robot production during the GTAW process.

It is very important for the type of teaching‐playback robots with the passive vision that the real‐time images of seam and pool are acquired clearly and processed accurately during the robotic welding process, which helps determine follow‐up seam track and the control of welding quality.

The purpose of this paper is to develop a kind of low cost measuring system based on binocular vision sensor to detect both the weld pool geometry and root gap simultaneously for robot welding process.

Two normal charge coupled device cameras are used for capturing clear images from two directions; one of them is used to measure the root gap and another one is used to measure the geometric parameters of the weld pool. Efforts are made from both hardware and software aspects to decrease the strong interferences in pulsed gas tungsten arc welding process, so that clear and steady images can be obtained. The grey level distribution characteristics of root gap edge and weld pool edge in images are analyzed and utilized for developing the image processing algorithms.

A solid foundation for seam tracking and penetration control of robot welding process can be established based on the binocular vision sensor.

The results show that the algorithms can extract the root gap edges and the contour of weld pool effectively, and then some geometric parameters can be calculated from the results.

The binocular vision system provides a new method for sensing of robot welding process.

The purpose of this paper is to introduce structured light image processing technology into pipeline welding automation projects, and develop a vision‐based pipeline girth‐welding robot. The welding torch can accurately track the weld and complete the omni‐orientation welding automatically.

Weld image processing adopts the base theory including Laplacian of Gaussian filter, neighbourhood mean filter, largest variance threshold segmentation and morphologic, etc. obtains good effect of weld recognition.

The paper uses a vision sensor to achieve the weld character's recognition and extraction, directly control the robot tracking weld to complete automation welding. Compared with the existing pipeline welding devices, it does not need the lay orbit or plot tracking mark, which can shorten the assistant time to improve the productivity.

The research findings can satisfy the need of whole‐directional automation welding for large diameter transportation pipe's circular abutting weld. It fits for the automation welding for the long‐distance transportation pipe of petroleum, natural gas, and water.

Aiming at the character recognition and extract of V‐type weld, the method combining the neighbourhood mean filter algorithm with the largest variance threshold segmentation is proposed to obtain the quick weld image processing speed.