Study of image processing for V-shape groove and robotic weld seam tracking based on laser vision

Single-stripe laser was applied to acquire V-shape groove contour information. Most of arc light and splash noise was removed and stripe laser image was kept by wavelet transform. Half-threshold algorithm was used for image segmentation and stripe laser image was gotten after refining. Weld seam center position was identified and extracted by extreme curvature corner detection method. The location of torch was detected to accord the frequency of computer program with robot program and serial communication program. The tracking experiments of sidelong, reflex and curve weld line show that the system can meet the demand of the tracking precision under normal welding conditions.

Weld Seam Deviation Prediction of Gas Metal Arc Welding Based on Arc Sound Signal

Weld seam deviation prediction is the key to weld seam tracking control, which is of great significance for realizing welding automation and ensuring welding quality. Aiming at the problem of weld seam deviation prediction in GMAW (gas metal arc welding), a method of weld seam deviation prediction based on arc sound signal is proposed. By analyzing the feature of the arc sound signal waveform, the time domain feature of the arc sound signal is extracted. The wavelet packet analysis method is used to analyze the time-fre- quency domain feature of the arc sound signal, and the wavelet packet energy feature is extracted. The time domain feature and wavelet packet energy feature are used to establish the feature vector, and the BP (back propagation) neural network is used to realize the weld seam deviation prediction. The results show that the method proposed in this paper has a good weld seam deviation prediction effect, with a mean absolute error of 0.234 mm, which provides a new method for GMAW weld seam recognition.

Weld seam edge extraction in X-ray images using an improved competitive fuzzy detector

The characteristics of X-ray testing image are analyzed and improved competitive fuzzy edge detection is described. This algorithm takes into account maximizing objective function to estimate the edge intensity at first. Then, according to the new edge patterns, learning vector quantization neural network is applied to each edge pixel according to its assigned class. At last the thinning algorithms is run to get the one-pixel wide edge image. Experimental results show that the proposed method can better improve the efficiency of weld seam image processing.

Kalman filter of the force signal of identifying weld seam in remote teaching

For reasons of the vibration of robot, the rough surface of weld seam and electromagnetic disturbance of welding machine, the force signals of identifying weld seam become unstable. The position error of remote teaching point is too big to meet teaching requirements in remote welding. The force signals of identifying weld seam can be filtered by Kalman. The force signals of identifying weld seam of next teaching point is accurately predicted according to predicting algorithms, such as the equation of the state, the equation of the observation, the gain matrix of the filter and the covariance matrix of predicting state. The experimental results show that the precision of identifying weld seam is improved by Kalman.filter.

Weld seam recognition algorithm based on HSI space for pipeline ultrasonic flaw inspection

In order to realize automatic weld seam tracking for pipeline ultrasonic flaw inspection, an image processing algorithm based on HSI color space was presented. Since the color tones of weld seam were different from the parent metal, weld seam images were transformed to HSI color space. In the HSl colar space, the weld seam and base metal area can be apparently distinguished. By using this image processing algorithm, the edges and centerline of pipeline weld seam can be correctly extracted. An industrial application system was developed based on the image processing algorithm, and the image processing time is less than 70 ms and the accuracy of weld seam recognition is better than 2mm.

Inflection point identifying of weld seam based on force sensing in tele-teaching

Weld seam inflection points are inevitable in tele-teaching process on many welding occasions. The inflection points identified accurately is one of the prerequisites of ensuring tele-teaching precision. On the basis of the inflection point characters, the concept of inflection point direction coefficient is proposed, the human-simulation intelligent control model of inflection point is established. The algorithms above, the inflection point identifying of box workpiece can be well performed. The experimental results show that the identifying average error of inflection point can be reduced to less than O. 5 mm by using optimal treatment of robot off-line programming system. The identifying control can automatically identify weld seam inflection points which can meet tele-teaching requirements.

Discerning Weld Seam Pro les from Strong Arc Background for the Robotic Automated Welding Process via Visual Attention Features

In the robotic welding process with thick steel plates,laser vision sensors are widely used to profile the weld seam to implement automatic seam tracking.The weld seam profile extraction(WSPE)result is a crucial step for identifying the feature points of the extracted profile to guide the welding torch in real time.The visual information processing system may collapse when interference data points in the image survive during the phase of feature point identification,which results in low tracking accuracy and poor welding quality.This paper presents a visual attention featurebased method to extract the weld seam profile(WSP)from the strong arc background using clustering results.First,a binary image is obtained through the preprocessing stage.Second,all data points with a gray value 255 are clustered with the nearest neighborhood clustering algorithm.Third,a strategy is developed to discern one cluster belonging to the WSP from the appointed candidate clusters in each loop,and a scheme is proposed to extract the entire WSP using visual continuity.Compared with the previous methods the proposed method in this paper can extract more useful details of the WSP and has better stability in terms of removing the interference data.Considerable WSPE tests with butt joints and T-joints show the anti-interference ability of the proposed method,which contributes to smoothing the welding process and shows its practical value in robotic automated welding with thick steel plates.

WeldNet:A voxel-based deep learning network for point cloud annular weld seam detection

Weld seam detection is an important part of automated welding.At present,few studies have been conducted on annular weld seams,and a lot of defects exist in the point cloud model of the tube sheet obtained by RGB-D cameras and photography methods.Aiming at the above problems,this paper proposed an annular weld seam detection network named WeldNet where a voxel feature encoding layer was adaptively improved for annular weld seams,the sparse convolutional network and region proposal network(RPN)were used to detect annular weld seam position,and an annular weld seam detection loss function was designed.Further,an annular weld seam dataset was established to train the network.Compared with the random sampling consistency(RANSAC)method,WeldNet has a higher detection accuracy,as well as a higher detection success rate which has increased by 23%.Compared with U-Net,WeldNet has been proven to achieve a better detection result,and the intersection over the union of the weld seam detection is improved by 17.8%.

An adaptive thresholding for weld seam image recognition in real time

Image prooessing of wehl seam in real time is an importunity to make welding rohot be able to track weld seam. The algorithm described in this paper combines some image technologies, such as modified Sobel edge detector and Hough transformation function, and especially the thresholds for image processing are ore aled adaptively by Ineans of a neural network. aests proved that this algorithm has a high reliability and rapidity in distinguishing the position of weld seam even with noises. The algorithm can be used ac the basic program .for robot to track welding seam and furthermore for calculating 3 dimensional information plan robot movement automatically.

Automation Technology in Welding Seam Welding Machining Design

With the continuous development of society,the gradual improvement of mechanical automation technology has been introduced into the production of many enterprises in China,which has had a great impact on the increase of China′s gross national product.Applying mechanical automation technology greatly saves labor cost,improves working efficiency and the production level of enterprises,effectively promotes the development of enterprises,and increases the enterprise′s income.In addition,mechanical automation technology is closely related to product production⁃related technology and plays a very important role in the innovation of enterprise products.This article will briefly introduce the design and application of automation technology in welding seam welding design,and understand the development background,research status,current application and simulation design.

Seam Tracking and Visual Control for Robotic Arc Welding Based on Structured Light Stereovision

A real-time arc welding robot visual control system based on a local network with a multi-level hierarchy is developed in this paper. It consists of an intelligence and human-machine interface level, a motion planning level, a motion control level and a servo control level. The last three levels form a local real-time open robot controller, which realizes motion planning and motion control of a robot. A camera calibration method based on the relative movement of the end-effector connected to a robot is proposed and a method for tracking weld seam based on the structured light stereovision is provided. Combining the parameters of the cameras and laser plane, three groups of position values in Cartesian space are obtained for each feature point in a stripe projected on the weld seam. The accurate three-dimensional position of the edge points in the weld seam can be calculated from the obtained parameters with an information fusion algorithm. By calculating the weld seam parameter from position and image data, the movement parameters of the robot used for tracking can be determined. A swing welding experiment of type V groove weld is successfully conducted, the results of which show that the system has high resolution seam tracking in real-time, and works stably and efficiently.

Microstructural and mechanical properties of ZA10 alloy tubes and their weld seams prepared by Conform continuous extrusion

In the present work,Zn-10 Al-2 Cu-0.05 Ti(ZA10)alloy tubes with a diameter of 12.5 mm and wall thickness of 1.2 mm were fabricated by one-pass and double-pass Conform continuous extrusion.A stabilizing heat treatment[350℃,30 min(furnace cooling)+120℃,12 h(air cooling)]was also applied to some of the double-pass tubes to improve the quality of their weld seams.The yield strength,ultimate tensile strength,elongation and expansion ratio of the one-pass continuous extrusion tube were 268.4 MPa,294.3 MPa,13.8%and 5.5%,respectively.Double-pass continuous extrusion improved these values to 278.4 MPa,317.2 MPa,15.4%and 11.4%,respectively.Double-pass tubes also had fewer aggregations of Al-αprecipitates along the welding seam,which improved seam quality and caused cracks to appear in the matrix,away from the weld-affected zone,during expansion testing.Heat-treated double-pass tubes exhibited superior yield strength(283.9 MPa)and ultimate tensile strength(328.5 MPa)but lower elongation(10.2%)and expansion ratios(10.3%).Additionally,the heat-treated tubes exhibited markedly lower elongation at room temperature due to the remarkable blockage of dislocation motions by fine-scale lamellar(α+η)eutectoid structures and a lower size effect when stretched.

Vision Sensing-Based Online Correction System for Robotic Weld Grinding

The service cycle and dynamic performance of structural parts are afected by the weld grinding accuracy and surface consistency. Because of reasons such as assembly errors and thermal deformation, the actual track of the robot does not coincide with the theoretical track when the weld is ground ofine, resulting in poor workpiece surface quality. Considering these problems, in this study, a vision sensing-based online correction system for robotic weld grinding was developed. The system mainly included three subsystems: weld feature extraction, grinding, and robot real-time control. The grinding equipment was frst set as a substation for the robot using the WorkVisual software. The input/output (I/O) ports for communication between the robot and the grinding equipment were confgured via the I/O mapping function to enable the robot to control the grinding equipment (start, stop, and speed control). Subsequently, the Ethernet KRL software package was used to write the data interaction structure to realize realtime communication between the robot and the laser vision system. To correct the measurement error caused by the bending deformation of the workpiece, we established a surface profle model of the base material in the weld area using a polynomial ftting algorithm to compensate for the measurement data. The corrected extracted weld width and height errors were reduced by 2.01% and 9.3%, respectively. Online weld seam extraction and correction experiments verifed the efectiveness of the system’s correction function, and the system could control the grinding trajectory error within 0.2 mm. The reliability of the system was verifed through actual weld grinding experiments. The roughness, Ra, could reach 0.504 µm and the average residual height was within 0.21 mm. In this study, we developed a vision sensing-based online correction system for robotic weld grinding with a good correction efect and high robustness.

The characteristics of welding currents in a rotating arc leadtandem GMAW process and the weld-seam tracking

Tandem gas metal arc welding （ T-GMAW） process shows a high deposition rate that up to three times o f the single electrode GMAW, so the welding speed could be significantly increased in this process. However, the majority o f this process applications are based on the pre-programmed robotic welding, which does not allow them to track the seam real-time during welding. Rotating arc sensor, sensing the seam position by detecting the changing of welding currents, has been widely adopted in the automatic robot welding process. It is proposed in this paper to integrate the rotating arc sensor with a trailing torch to develop a new approach of rotating arc lead tandem gas metal arc welding （RLT-GMAW） process. The characteristics of the welding currents in the proposed new welding process were firstly studied, and then a self-turning fuzzy control seam tracking strategy was developed for the mobile robot automatic welding. The experimental results showed that the proposed RLT-GMAW process had an excellent seam tracking performance and high welding deposition rate. Even if there were some electromagnetic interactions between the two arcs, the deviation of the welding seam could also be reflected by the fluctuation of the welding currents on the leading arc once the correct welding parameters were selected. Based on the detected deviation, the welding tracking experiments showed that the proposed self-turning fuzzy controller had a good performance for the RLT-GMAW process seam tracking.

Seam Tracking Technology for Hyperbaric Underwater Welding

Automatic weld seam tracking technology to be used in hyperbaric underwater damaged pipeline repair welding is much more important, because of poor bevel preparation and severe working condition. A weld seam tracking system based on digital signal processing（DSP） passive light weld image processing technology has been established. A convenient charge coupled device（CCD） camera system was used in the high pressure environment with the help of an aperture and focus altering mechanism to guarantee overall image visibility in the scope of pressure below 0.7 MPa. The system can be used in the hyperbaric environment to pick up the real welding image of both the welding arc and the welding pool. The newly developed DSP technology was adopted to achieve the goal of system real time characteristics. An effective weld groove edge recognition technique including narrow interesting window opening, middle value wave filtering, Sobel operator weld edge detecting and edge searching in a defined narrow area was proposed to remove the guide error and system accuracy was ensured. The results of tracking simulation and real tracking application with arc striking have proved the validity and the accuracy of the mentioned system and the image processing method.

Friction assisted solid state lap seam welding and additive manufacturing method

This paper describes results of seam welding of relatively high temperature melting materials, AISI 304, C-Mn steels, Ni-based alloys, CP Cu, CP Ni, Ti6Al4V and relatively low temperature melting material, AA6061. It describes the seam welding of multi-layered similar and dissimilar metallic sheets. The method described and involved advancing a rotating non-consumable rod(CP Mo or AISI 304) toward the upper sheet of a metallic stack clamped under pressure. As soon as the distal end of the rod touched the top portion of the upper metallic sheet, an axial force was applied. After an initial dwell time, the metallic stack moved horizontally relative to the stationery non-consumable rod by a desired length, thereby forming a metallurgical bond between the metallic sheets. Multi-track and multi-metal seam welds of high temperature metallic sheets, AISI 304, C-Mn steel,Nickel-based alloys, Cp Cu, Ti6Al4V and low temperature metallic sheets, AA6061 were obtained. Optical and scanning electron microscopy examination and 180 degree U-bend test indicated that defect free seam welds could be obtained with this method. Tensile- shear testing showed that the seam welds of AISI 304, C-Mn steel, Nickel-based alloy were stronger than the starting base metal counterparts while AA6061 was weaker due to softening. The metallurgical bonding at the interface between the metallic sheets was attributed to localized stick and slip at the interface, dynamic recrystallization and diffusion. The method developed can be used as a means of welding, cladding and additive manufacturing.

Arc sensing system for automatic weld seam tracking(Ⅰ)——Mathematic model

Arc sensor, one of the most popular and effective sensors for automatic seam tracking of arc welding, is systematically studied. A mathematic model of the static and the dynamic properties of the sensor derived on the basis of control theory and experimental method are described. The model extends our understanding of arc sensor from qualitative level to quantitative level. This is important for its design and further development.

Seam tracking control for mobile welding robot based on vision sensor

To solve the seam tracking problem of mobile welding robot,a new controller based on the dynamics of mobile welding robot was designed using the method of backstepping kinematics into dynamics.A self-turning fuzzy controller and a fuzzy-Gaussian neural network(FGNN) controller were designed to complete coordinately controlling of cross-slider and wheels.The fuzzy-neural control algorithm was described by applying the Gaussian function and back propagation(BP) learning rule was used to tune the membership function in real time by applying the FGNN controller.To make the tracking more quickly and smoothly,the neural network controller based on dynamic model was designed,which utilized self-learning and self-adaptive ability of the neural network to deal with the partial uncertainty and the disturbances of the parameters of the robot dynamic model and real-time compensate the dynamics coupling.The results show that the selected control input torques make the system globally and asymptotically stable based on the Lyapunov function selected out;the accuracy of the proposed controller tracing is within ±0.4 mm and can satisfy the requirements of practical welding project.

Arc sensing system for automatic weld seam tracking (Ⅱ)——Signal processing

Due to violent disturbance of the welding arc, signal processing is the key problem of application of the sensor. By means of the new technique the arc sensor can recognize not only V-groove but also lap joint and butt joint. The sensor has good recognition ability even for welding process with very large current disturbance, e.g. pulsed arc and short circuit welding process, etc. The proposed technique is developed on the basis of modern digital filtering theory and mathematic transformation of the signals from time domain into frequency domain.

Lap joining of titanium alloy using laser welding and resistance seam welding combined process

Titanium alloy lap joints were performed by combined laser welding and resistance seam welding process. The welding characteristics of this combined process were investigated compared with that of laser welding. The experimental results indicate that the combined process welded joint has larger weld width at the lap surface. The joint tensile shear force of combined process is 2. 5 times that of laser welding. There are some pores around the lap surface in laser welded joint, and most pores can be eliminated by resistance seam welding process. Metallographic examinations of combined process welded joint reveal that the microstructure in heat-affected zone （HAZ） and weld zone has the acicular martensite morphology, which causes that the microhardness in HAZ and weld zone increases compared with the base metal, and the microhardness in weld zone is highest.