Shanben Chen

Acquisition of Weld Seam Dimensional Position Information for Arc Welding Robot Based on Vision Computing

Abstract Recognition and identification of weld environment and seam dimensional position by computer vision is a key technology for developing advanced autonomous welding robot. Aiming at requirements for recognition of weld seam image characteristics, this paper first presents an improved algorithm of subpixel edge detection based on Zernike moments. Comparing with the Ghosal’s original algorithm, the improved algorithm deals with mask effect and first derivative model on edge gradient direction so that it has the strong robust to noise, self-thinning ability and higher locating precision. An algorithm based on ZMs to extract line is also proposed, the comparative results with SHT and RHT show the method has the highest calculation speed and accuracy. The stereovision technology is developed to identify dimensional position of weld seam by computing dimensional coordinates of the weld seam. According to characteristics of weld seam, view field scope model and stereovision model based on baseline are studied and a stereo matching method is presented. In order to evaluate the algorithms and models presented in this paper, a welding robot systems with single camera fixed on the weld torch end-effector has been established for the robot to identify the dimensional position of typical weld seam by one-item and two-position method. The experiment results on S-shape and saddle-shape weld seams show that the vision computing method developed in this paper can be used for acquiring weld seam dimensional position information in welding robot system. Thus the welding path is mapped before the welding operation is executed.

Robotic Welding Systems with Vision-Sensing and Self-learning Neuron Control of Arc Welding Dynamic Process

This paper addresses the vision sensing and neuron control techniques for real-time sensing and control of weld pool dynamics during robotic arc welding. Current teaching playback welding robots are not provided with this real-time function for sensing and control of the welding process. In our research, using composite filtering technology, a computer vision sensing system was established and clear weld pool images were captured during robotic-pulsed Gas Tungsten Arc Welding (GTAW). A corresponding image processing algorithm has been developed to pick up characteristic parameters of the weld pool in real-time. Furthermore, an ANN model of the weld pool dynamic process of robotic-pulsed GTAW was developed. Based on neuron self-learning PSD controller design, the real-time control of weld pool dynamics during the pulsed GTAW process has been realized in robotic systems.

Real-Time Seam Tracking Technology of Welding Robot with Visual Sensing

A seam tracking system with visual sensing free from calibration was developed for the robot applied in gas tungsten arc welding. A visual sensor with double-layer filter system was researched for the different levels of the welding current. An image processing in C+ + language was developed to extract the seam trajectory and the offset of the torch to the seam in the welding images of aluminum alloys plates with grooves. The rectifying rule of the robot used in this paper was found based on the experimental data, and the seam tracking controller was also analyzed and constructed. The experimental results on straight line seam and curve seam demonstrated the efficiency of the proposal seam tracking system.

Autonomous Acquisition of Seam Coordinates for Arc Welding Robot Based on Visual Servoing

Autonomous acquisition of seam coordinates is a key technology for developing advanced welding robot. This paper describes a position-based visual servo system for robotic seam tracking, which is able to autonomously acquire the seam coordinates of the planar butt joint in the robot base frame and plan the optimal camera angle before welding. A six-axis industrial robot is used in this system, which has an interface for communicating with the master computer. The developed visual sensor device is briefly presented that allows the charge-coupled device (CCD) cameras to rotate about the torch. A set of robust image processing algorithms are proposed so that no special requirements of light source are needed in this system. The feedback errors of this servo system are defined according to the characteristics of the seam image, and the robust tracking controller is developed. Both the image processing program and tracking control program run on the master computer. The experimental results on straight line seam and curve seam show that the accuracy of the seam coordinates acquired with this method is more adequate for high quality welding process.

Closed-Loop Control of Robotic Arc Welding System with Full-penetration Monitoring

The real-time detection of the state of the gap and weld penetration control are two fundamental issues in robotic arc welding. However, traditional robotic arc welding lacks external information feedback and the function of real-time adjusting. The objective of this research is to adopt new sensing techniques and artificial intelligence to ensure the stability of the welding process through controlling penetration depth and weld pool geometry. A novel arc welding robot system including function modules (visual modules, data acquisition modules) and corresponding software system was developed. Thus, the autonomy and intelligence of the arc welding robot system is realized. Aimed at solving welding penetration depth, a neural network (NN) model is developed to calculate the full penetration state, which is specified by the back-side bead width (Wb), from the top-side vision sensing technique. And then, a versatile algorithm developed to provide robust real-time processing of images for use with a vision-based computer control system is discussed. To this end, the peak current self adaptive regulating controller with weld gap compensation was designed in the robotic arc welding control system. Using this closed-loop control experiments have been conducted to verify the effectiveness of the proposed control system for the robotic arc welding process. The results show that the standard error of the Wb is 0.124 regardless of the variations in the state of the gap.

A robust algorithm for weld seam extraction based on prior knowledge of weld seam

– 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.

The autonomous detection and guiding of start welding position for arc welding robot

Purpose – The recognition and positioning of start welding position (SWP) is the first step and one of the key technologies to realize autonomous robot welding. The purpose of this paper is to describe a method developed to accomplish successful autonomous detection and guiding of SWP.Design/methodology/approach – The images of workpieces are snapped by charge coupled device (CCD) cameras in a relative large range without additional light. The recognized methods of SWP are analyzed according to the given definition. A two‐step method named “coarse‐to‐fine” is proposed to recognize the SWP accurately. The first step is to solve the curve functions of seam and workpieces boundaries by fitting. The intersection point is regarded as initial value of SWP. The second step is to establish a small window that takes the initial value of SWP as centre. Then, the SWP is obtained exactly by corner detection in the window. Both the abundant information of original image and the structured information of recognized ima...

Binocular vision system for both weld pool and root gap in robot welding process

Purpose – 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.Design/methodology/approach – 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.Findings – A solid foundation for seam tracking and penetration control of robot welding process can be established based on the binocular vision sensor.Practical implications – The results show that the algor...

Research on the Real-time Tracking Information of Three-dimension Welding Seam in Robotic GTAW Process Based on Composite Sensor Technology

Aiming at the shortcomings of teaching-playback robot that can’t track the three-dimensional welding seam in real time during GTAW process, this paper designed a set of composite sensor system for tracking the three-dimensional welding seam based on visual sensor and arc sensor technology, which can effectively acquire three-dimensional welding seam information, such as clear images of seam and pool and stable arc voltage signals. The characteristic values of weld image and arc voltage signals were accurately extracted by using proper processing algorithm, and the experiments have been done to verify the precision of processing algorithms. The results demonstrate that the error is very small, which is accurate enough to meet the requirements of the subsequent real-time tracking and controlling during the welding robot GTAW process.

Autonomous Detection of Weld Seam Profiles via a Model of Saliency-Based Visual Attention for Robotic Arc Welding

This paper presents a method of autonomously detecting weld seam profiles from molten pool background in metal active gas (MAG) arc welding using a novel model of saliency-based visual attention. First, a vision sensor based on structured light is employed to capture laser stripes and molten pools simultaneously in the same frame. Second, to effectively detect the weld seam profile from molten pool background for next autonomous guidance of initial welding positions and seam tracking, a model of visual attention based on saliency is proposed. With respect to the enhanced effect of saliency, the proposed model is much better than the classic models in the field. According to the comprehensive saliency map created by the proposed model, the weld seam profile can be extracted after threshold segmentation and clustering are applied to it in turn. Third, different weld seam images are used to demonstrate the robustness of the proposed methodology and last, to evaluate the performance of the proposed method, a measure called profile extraction rate (PER) is computed, which shows that the extracted weld seam profile can basically meet the requirements of seam tracking and the guidance of welding torches.