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.

Visual Tracking System for Welding Seams

To track the narrow butt welding seams in container manufacture, a visual tracking system based on smart camera is proposed in this paper. A smart camera is used as the sensor to detect the welding seam. The feature extraction algorithm is designed with the consideration of the characteristics of the smart caraera, which is used to compute the error between the welding torch and the welding seam. Visual control system based on image is preseated, which employs a programmable controller to control a stepper motor to eliminate the tracking error detected by the smart camera. Experiments are conducted to demonstrate the effectiveness of the vision system.

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 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.

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.

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.

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.

Welding Deviation Detection Algorithm Based on Extremum of Molten Pool Image Contour

The welding deviation detection is the basis of robotic tracking welding, but the on-line real-time measurement of welding deviation is still not well solved by the existing methods. There is plenty of information in the gas metal arc welding（GMAW） molten pool images that is very important for the control of welding seam tracking. The physical meaning for the curvature extremum of molten pool contour is revealed by researching the molten pool images, that is, the deviation information points of welding wire center and the molten tip center are the maxima and the local maxima of the contour curvature, and the horizontal welding deviation is the position difference of these two extremum points. A new method of weld deviation detection is presented, including the process of preprocessing molten pool images, extracting and segmenting the contours, obtaining the contour extremum points, and calculating the welding deviation, etc. Extracting the contours is the premise, segmenting the contour lines is the foundation, and obtaining the contour extremum points is the key. The contour images can be extracted with the method of discrete dyadic wavelet transform, which is divided into two sub contours including welding wire and molten tip separately. The curvature value of each point of the two sub contour lines is calculated based on the approximate curvature formula of multi-points for plane curve, and the two points of the curvature extremum are the characteristics needed for the welding deviation calculation. The results of the tests and analyses show that the maximum error of the obtained on-line welding deviation is 2 pixels（0.16 ram）, and the algorithm is stable enough to meet the requirements of the pipeline in real-time control at a speed of less than 500 mm/min. The method can be applied to the on-line automatic welding deviation detection.

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.

Comparison of experimental methods to evaluate seam welds quality in extruded profiles

The wedge and bulge expansion tests were compared in the assessment of the seam welds strength in a tubular profile extruded at two ram speeds.In the wedge test,the expansion was determined by moving a conical punch into the tube until the specimen fracture.In the bulge test,a hydrostatic tensile stress state was applied by expanding the specimen with an internal rubber plug.The two methodologies were compared in terms of load and hoop strain at fracture and by detecting the fracture morphology and location.Then,the effect of a number of design parameters was investigated in order to evaluate the robustness of the standard testing conditions.For both tests,ductile fractures appeared in the seam welds location,but the bulge test was more robust and conservative with respect to the wedge test,showing less scattered data.Thus,the performances of a second die for the tube profile,designed to optimize the seam welds quality,have been successfully assessed by the bulge test and results compared to those achieved by a novel numerical quality index,coming to a final good matching.

Effects of induction heating on properties of X80 longitudinal submerged-arc welding line pipe

The local induction heating process and the whole induction heating process were adopted to develop the X80 Ф1219 mm × 22 mm induction bends in this paper. After the tempering of the bends, the effect of induction heating on the properties of X80 longitudinal submerged-arc welding line pipe was studied. The results show that for the X80 bend manufactured via the local induction heating, both the tensile strength and yield ratio of unquenehed straight tangents are higher than those of the quenched bend area. The toughness of straight tangents is lower than that of the bend area. The strength and toughness of straight tangents do not match pegrectly that of the bend area. The induction heating can improve effectively the toughness of weld pipe, reduce the tensile strength and yield ratio to some extent. The whole induction heating makes the match of properties between the straight tangents and the bend area reasonable, thus it is an optimum process for the manufacturing of X80 induction bend.

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.

Spline-based automatic path generation of welding robot

This paper presents a flexible method for the representation of welded seam based on spline interpolation. In this method, the tool path of welding robot can be generated automatically from a 3D CAD model. This technique has been implemented and demonstrated in the FANUC Arc Welding Robot Workstation. According to the method, a software system is developed using VBA of SolidWorks 2006. It offers an interface between SolidWorks and ROBOGUIDE, the off-line programming software of FANUC robot. It combines the strong modeling function of the former and the simulating function of the latter. It also has the capability of with on-line robot. The result data have shown its high accuracy and strong reliability in experiments. This method will improve the intelligence and the flexibility of the welding robot workstation.

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.

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.

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.

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%.

Edge detection and its application to recognition of arc weld image

Abstract Image sensor has been one of the key technologies in intellectualized robotics welding. Edge detection plays an important role when the vision technology is applied in intellectualized welding robotics technologies. There are all kinds of noises in welding environment. The algorithms in common use cannot be applied to the recognition of welding environment directly. The edge of images can be fell into four types. The weld images are classified by the characteristic of welding environment in this paper. This paper analyzes some algorithms of edge detection according to the character of welding image, some relative advantages and disadvantages are pointed out when these algorithms are used in this field, and some suggestions are given. The feature extraction of weld seam and weld pool are two typical problems in the realization of intellectualized welding. Their edge features are extracted and the results show the applicability of different edge detectors. The trndeoff between precision and calculated time is also considered for different application.