Welding Trajectory Research Articles

Manipulator Trajectory Planning Based on Clustering Curve Discretization and B‐Spline

ABSTRACTIn addressing the welding trajectory planning problem for a six‐axis robot under visual guidance, a method based on clustering and isometric image trajectory curve discretization is proposed to enhance welding trajectory precision and improve welding quality. Firstly, the kinematic model of the six‐axis robot is established using the D‐H parameter method. Subsequently, the discrete processing of the welding trajectory map recognized by an industrial camera and chord error calculation are performed. The discrete trajectory is then interpolated using fifth‐order non‐uniform B‐spline interpolation, with welding speed planning based on an S‐shaped speed planner. Finally, the planned trajectory is verified by welding simulation in MATLAB and V‐REP simulation. The simulation results show that the end trajectory of the manipulator is smooth and stable. Compared with the equal arc length discretization method, the discretization accuracy of the proposed method is improved by 19% (plane curve) and 10.7% (space curve), indicating that the proposed discretization method improves the end tracking accuracy of the six‐axis robot.

Advanced welding automation: Intelligent systems for multipass welding in Butt Double V-Groove and Tee Double Bevel configurations

The paper addresses the imperative shift towards automation in welding processes, leveraging advanced technologies such as industrial robotic systems. Focusing on the reconstruction and classification of weld joints, it introduces a methodology for automatic trajectory determination. Utilizing a laser profilometer mounted on the robot, weld joints are reconstructed in three dimensions, and spurious data is filtered out through signal processing. A classification algorithm, integrating signal processing and artificial intelligence, accurately categorizes joint profiles, including V-joints and single bevel T-joints. The proposed intelligent and adaptive system enhances welding automation by analyzing point cloud data from laser scanning to optimize welding trajectories. This study establishes a foundational framework for further refinement and broader application in welding automation.Key Points•Introduction of a methodology for automated trajectory determination in welding processes.•Utilization of laser scanning and signal processing for reconstruction and classification of weld joints.•Implementation of an intelligent and adaptive system to optimize welding trajectories.

A multi-objective trajectory planning approach for vibration suppression of a series–parallel hybrid flexible welding manipulator

Although the series–parallel hybrid welding manipulator has advantages over the traditional series manipulator, its must run continuously and smoothly according to a specific trajectory at the rated velocity. Considering a flexible manipulator with a complex series–parallel hybrid structure as the research object, this paper proposes a multi-objective approach to indirectly suppress the vibration. In view of the complex flexible mechanism with a multi-kinematic pair arrangement and a multi-motor drive, a system comprehensive elastodynamics model is established, and the effectiveness of the model is verified through the hammer test method. To achieve synchronous and coordinated planning of the end position and attitude, the spatial arc welding trajectory is planned on the basis of the synchronous planning controller and the dual-S velocity profile interpolation function. Then, a multi-objective optimization model considering the travel time, energy consumption and terminal amplitude fluctuations is proposed, the trajectory planning problem is transformed into a parameter optimization problem in the interpolation function, and then the elite non-dominated sorting genetic algorithm (NSGA-II) is employed to determine the optimal trajectory. The optimal trajectory thus obtained effectively suppresses the vibration of the system. Finally, compared with the classic fifth-degree polynomial trajectory planning method, the system vibration suppression effect is significant, which verifies the effectiveness and feasibility of the proposed method and provides a theoretical basis for further realizing real-time control.

Real-Time Space Trajectory Judgment for Industrial Robots in Welding Tasks

In welding tasks, the repeated positioning precision of robots can generally reach the micron level, but the data of each axis during each operation may vary. There may even be out-of-control situations where the robot does not run according to the set welding trajectory, which may cause the robot and equipment to collide and be damaged. Therefore, a real-time judgment method for the welding robot trajectory is proposed. Firstly, multiple sets of axis data are obtained by running the welding robot, and the phase of the data is aligned by using a proposed algorithm, and then the Kendall correlation coefficient is used to identify and remove weak axis data. Secondly, the mean of multiple sets of axis data with strong correlation is calculated as the standard trajectory, and the trajectory threshold of the robot is set using the μ ± nσ method based on the trajectory deviation judgment sensitivity. Finally, the absolute difference between the real-time axis trajectory and the standard trajectory is used to determine the deviation of the running trajectory. When the deviation reaches the threshold, a forewarning starts. When the deviation exceeds the threshold + σ, the robot is stopped. Take the six-axis welding robot as an example, by collecting the axis data of the robot running multiple times under the same conditions, it is proved that the proposed method can accurately warn the deviation of the running trajectory. The research results have important practical value for the prevention of welding robot accidents in industrial production.

Research on slope algorithm based laser welding guidance technology for 3D vision inspection of thin-walled parts

Visual guidance technology advances quickly and so do the technical requirements for weld trajectory, which needs to fulfill performance indices for high precision, anti-interference and quick processing. In this study, the approach of getting 3D point cloud data by directly scanning the workpiece to be welded with a 3D scanner is used because it is more precise and reliable than processing gray-layer data obtained from a depth camera, turning it into a 3D point cloud. The weld boundary is retrieved by slope calculation for the butt weld after preprocessing the three-dimensional point cloud data (cut, down-sampling, and abnormal value removal). The center point is then determined. After that, a welding trajectory is produced by fitting the center point, which is transmitted back to the actuator to guide its welding work and realize the identification and tracking of the weld.

A novel model-based welding trajectory planning method for identical structural workpieces

Welding robots have been widely used with the development of manufacturing industry. At present, welding trajectory planning and programming of welding robots are performed separately for similar (structural) workpieces with different dimensions or deformations. Besides, the welding torch pose planning using conventional robot programming methods is time-consuming for workpieces with complex welding trajectories. To realize automatic planning of the welding torch pose for similar workpieces and improve the programming efficiency of welding robots, we propose a novel model-based welding trajectory planning method for welding robots. We create a basic model (point set) containing information on welding seam trajectory planning for similar workpieces. The welding seam trajectory extraction of similar workpieces is achieved by the non-rigid point set registration of the feature point cloud of similar workpieces with the basic model. Then, the welding torch pose planning is implemented by the geometric projection method of the local point cloud of the welding seam trajectory. Finally, the results of offline experiments and online experiments demonstrate the effectiveness of the proposed method.

Research on welding seam tracking algorithm for automatic welding process of X-shaped tip of concrete piles using laser distance sensor

AbstractThe manufacturing of the X-shaped tip of prestressed centrifugal concrete piles is nowadays done half automatically by combining the manual worker and the automatic welding robot. To make this welding process full automatically, the welding seam tracking algorithm is considered. There are many types of sensors that can be used to detect the welding seam such as vision sensor, laser vision sensor, arc sensor, or touch sensor. Each type of sensor has its advantages and disadvantages. In this paper, an algorithm for welding seam tracking using laser distance sensor is proposed. Firstly, the fundamental mathematics theory of the algorithm is presented. Next, the positioning table system supports the procedure is designed and manufactured. The object of this research is the fillet joint because of the characteristics of the X-shaped tip of the concrete piles. This paper proposes a new method to determine the welding trajectory of the tip using laser distance sensor. After that, the experimental results are received to verify the proposed idea. Finally, the improved proposal of the algorithm is considered to increase the accuracy of the suggested algorithm.

A novel approach for robotic welding trajectory recognition based on pseudo-binocular stereo vision

For plane welds, positioning seam, positioning arc-starting point, positioning arc-extinguishing point, and programming trajectory are often achieved through inefficient teaching-playback mode. For solving this series of problems, a novel approach of 3D welding trajectory recognition based on pseudo-binocular stereo vision is proposed to endow robots with autonomy in the welding non-standard plane welds or batch plane welds. Firstly, a 3D coordinate recognition model of pseudo-binocular stereo vision based on a single CCD camera is innovatively constructed to accurately identify 3D coordinates of spatial points in the robot base coordinate system, and the average identification accuracy of the model is 0.2251 mm, which is sufficient to satisfy welding requirements; Secondly, positioning seam, positioning arc-starting point, positioning arc-extinguishing point, identifying type of weld, and identifying ROI are transformed into a unified YOLOv5′s inference task, which greatly improves the robot's ability to perceive spatial objects; On this basis, algorithms such as Sobel operator, modified template matching algorithm, point cloud filtering, B-spline curve fitting realize the recognition and 3D reconstruction of welding trajectory from irresistible, dynamic and irregular interference information. Notably, compared with the traditional image matching algorithm, the modified template matching algorithm improves the correct matching rate of pixels from 86.667 % to 100 %. To evaluate the feasibility of the novel approach, the accuracy and robustness are verified using experiments on the S-shaped weld and the folded linear weld. The results demonstrate the proposed pseudo-binocular vision welding strategy give the robot the autonomy of welding plane welds in the actual industrial environment.

Versatile robotic welding system integrating laser positioning, trajectory fitting and real-time tracking

The three welding modes of laser positioning, trajectory fitting and real-time tracking are key links for flexible robotic welding, because they directly affect the adaptability of welding robots for complex structural parts. Nevertheless, there are few studies on the integration of laser positioning, trajectory fitting and real-time tracking welding. Aiming at the drawback of that the robot cannot efficiently match the corresponding welding process between different working conditions such as short seam, spatial curve seam and long seam welding, a control scheme that can integrate the above three welding modes is proposed in this paper. First, the welding requirements of laser positioning, trajectory fitting and real-time tracking are transformed into a unified 3D coordinate recognition task for integrated welding, so the identification of welding points under three different processes can be obtained through an identical inference. The next, given the fast inference speed of YOLOv5, a joint ROI extraction algorithm with YOLOv5 as the core is applied. To compensate for YOLOv5′s inability to directly locate seam centers, iterative centerline unbiased detector (ICUD) and adaptive feature extraction algorithm (AFEA) are proposed without the need for complex image pre-processing and with very strong immunity to strong exposure and strong arc spatter. Finally, teaching trajectory correction model, B-spline curve fitting model, and 3D coordinate real-time tracking model are presented to enhance the adaptability of the welding robot and to flexibly match the appropriate welding mode in various working conditions. Experimental results indicate that the welding trajectory is basically consistent with the seam centerline when laser positioning and trajectory fitting welding. In real-time tracking welding, the real-time deviation is mainly controlled within the range of ± 0.2 mm, and the average error is 0.1160 mm.

Intelligent and Adaptive System for Welding Process Automation in T-Shaped Joints

The automation of welding processes requires the use of automated systems and equipment, in many cases industrial robotic systems, to carry out welding processes that previously required human intervention. Automation in the industry offers numerous advantages, such as increased efficiency and productivity, cost reduction, improved product quality, increased flexibility and safety, and greater adaptability of companies to market changes. The field of welding automation is currently undergoing a period of profound change due to a combination of technological, regulatory, and economic factors worldwide. Nowadays, the most relevant aspect of the welding industry is meeting customer requirements by satisfying their needs. To achieve this, the automation of the welding process through sensors and control algorithms ensures the quality of the parts and prevents errors, such as porosity, unfused areas, deformations, and excessive heat. This paper proposes an intelligent and adaptive system based on the measurement of welding joints using laser scanning and the subsequent analysis of the obtained point cloud to adapt welding trajectories. This study focuses on the optimization of T-joints under specific welding conditions and is intended as an initial implementation of the algorithm, thus establishing a basis to be worked on further for a broader welding application.

Passive machine vision-based defect classification in tungsten inert gas welding on SS304 using AI-based gradient descent algorithm

In modern digitization, safety industries demand flaw-free and high-integrity welds, due to part localization on high uncertainty makes automation a challenging task. Integrating robotic welding with high-value manufacturing sector makes volume rise through pre-programmed repetitive performance on desired welding space. Further, the establishment of fully automated robotic-based welding operations with different configurations on controlled localization is the least available due to unviable cost. Hence, the proposed work concentrates on internet of things (IoT)-driven robotic tungsten inert gas (TIG) welding on stainless steel (SS) 304 by incorporating online programming (OP) with visual control schemes to classify the nature of weld quality using an artificial neural network. Continuous sensory-guided techniques with IoT high-level operator interface affords automated welding planning by using feature matching strategies. Area scan-based complementary metal oxide semiconductor (CMOS) cameras have been used to capture passive vision real-time images of the weld species for defect classification. In real-time, discrete reference workpiece and image feature extraction processes seem to be complicated in an unstructured welding environment. Hence, the present idea will enhance the efficiency of high product variance, but the accuracy of the automation relies on the weld image database. The proposed method (a) adaptively regulates its welding parameter variation on the welding trajectory path, (b) adapts and produces precise good weld workpieces with a 92% production rate with flaw-free welds and (c) initiates the automatic tuning of robot kinematics through IoT closed-loop online external control strategy. The proposed research experimental results confirm that fully automated IoT-driven robotic TIG welding affords good welding with an 88% improved quality rate through online passive vision-based image feature analysis.

Research on quality and performance of FRM laser welded joints with different shaft length ratios elliptical trajectory

The emerging Flexible Ring Mode(FRM) laser technology has shown great potential for welding copper and its alloys with high reflectivity and has been widely used. The design of the welding trajectory significantly affects the porosity and performance of welded joints. This paper first selects the ellipse and its parameters from the three trajectories by numerical simulation method, then studies the appearance forming and counts the weld porosity through the longitudinal and cross-section developing quality, and finally uses a high-speed camera to monitor the copper Behavior of molten pool and spot oscillation process at the front of the plate. It is found that the trajectory of circular motion has a more stable heating process and has a preheating function for the center of the weld. Compared with linear welding, orbital welding achieves penetration of weld seam and forms better weld seam. Laser confocal tests show that S4(a1b0.5) has the most negligible variation in weld appearance and the best formability. S3(a0.5b0.5) has the lowest porosity, which also explains the mechanism of laser welding trajectory stirring the molten pool and pooling of pores under hydrostatic pressure. The tensile test shows that: S3 has the highest tensile strength (215.35 MPa), 89.79% of the base material. But its strain is only 50% of that of the S4 sample with almost the same tensile strength (215.03 MPa). Therefore, this paper proposes that an elliptical welding trajectory can be adopted to obtain welded joints with better tensile strength and plasticity, with the long axis along the welding direction and the short axis perpendicular to the welding direction. This study provides theoretical guidance for the optimal oscillation mode selection for tunable ring mode laser welding of highly reflective materials.

Welding Robot Design with Machine Learning Based Intelligent Vision System

The use of welding technologies in the manufacturing sector plays a very important role and increases its popularity thanks to developing technologies. Welding technologies are used in almost every field where production takes place, and the speed and efficiency of welding technologies have increased in these sectors in recent years. The fact that artificial intelligence techniques are at the forefront and the efficient use of these techniques together with sensors has led to development in welding technology. Thus, welding robots emerged with the support of robots with artificial intelligence techniques, and adaptive systems that can adapt to different types of workpieces working autonomously in the manufacturing sector are shaping the sector. Despite these developments, non-autonomous systems are still used today by teaching the welding points to the robots by the operators. Along with the concept robotic system to be designed and implemented within the scope of this study, it is planned to determine the welding trajectory autonomously with artificial intelligence techniques, and to perform the welding process by following the welding trajectory by the robot.

A constant plunge depth control strategy for robotic FSW based on online trajectory generation

Robotic friction stir welding (RFSW) usually comes with a huge upsetting force, and the stiffness of the welding system distributes unevenly over the position, which leads to a large deviation of the plunge depth of the tool at the end of the robot. The conventional constant distance tracking control suffers from the problem of unsmooth compensation leading to the vibration of the robot and thus degrading the weld quality. For this problem, a constant plunge depth control based on online trajectory generation for RFSW is studied, which can generate an accurate welding trajectory according to the rough initial reference path and smoothly compensate for the plunge deviation. Initially, three laser-ranging sensors are utilized to measure the pose deviation of the tool in real-time and generate the ideal welding trajectory according to the projection vector method. Then, a deformation compensation model is established to realize the real-time prediction of the correct value. To ensure the smoothness and rapidity of the dynamic tracking process of displacement deviation, we adopt an online trajectory generator as the core of optimization control to meet the process constraints such as speed, acceleration, and jerk during the compensation process. Finally, simulation and experiment are carried out. The results show that the proposed method can effectively reduce the vibration caused by compensation during the welding process and reduce flash, which can improve the welding quality.

A point cloud-based welding trajectory planning method for plane welds

Plane welds are a common type of weld in industrial sites. When the welding robot welds multiple types of plane welds at the same time, the traditional teaching and programming modes become relatively complicated. Therefore, in order to solve the problem of automatic robot welding of various types of plane welds, taking plane V-type butt, plane I-type butt, and plane lap welds as examples, this paper proposes a plane weld extraction method based on 3D vision. Firstly, in order to realize the line and plane segmentation of the workpiece point cloud, we establish the concept of plane point cloud density. Secondly, to segment workpiece edge lines, an iterative segmentation algorithm based on RANSAC is proposed. Then, based on the geometric features of the workpiece, a method for extracting weld feature points based on centroid positioning is proposed. Finally, the least squares method is used to fit the feature points of the welding seam to complete the welding trajectory planning. The experimental results show that the method can well solve the problem of automatic planning welding trajectory of plane V-type butt, plane I-type butt, and plane lap welds, to realize that the welding robot can simultaneously weld various plane welds without teaching and programming.

Trajectory planning and trajectory correction method of welding system based on artificial intelligence

The welding seam data of robot welding is one of the fundamental parameters to measure whether the welding equipment meets the actual production requirements, and the welding accuracy is the key to judging the welding data. The purpose of this paper is to study the trajectory planning and trajectory correction methods of welding systems based on artificial intelligence. In order to verify the feasibility and reliability of the trajectory planning algorithm for the corrugated plate welding equipment and the workpiece welding trajectory correction method on the container welding production line, the trajectory planning experimental verification and trajectory correction experimental verification are carried out in this chapter. Through experiments and experimental data analysis, it is verified that the trajectory planning algorithm for corrugated plate welding special equipment and the workpiece welding trajectory correction method on the container welding production line meet the actual production requirements, and can effectively solve the problems caused by manual welding and positioning in the current production process. Deviation leads to problems such as unqualified welding quality. When the displacement is 5mm, the detection error in the X-axis direction, the detection error in the Y-axis direction and the combined error are all less than 0.8mm.

Automatic seam detection of welding robots using deep learning

Welding robots are employed to improve the welding efficiency and quality of steel structures. However, the complexity and diversity of weldments hinder the ability to detect weld seams. To address this limitation, this paper presents a vision-based model that uses a deep learning network combined with the symbol-patching method to plan welding trajectories for welding robots. Semantic straight lines are detected by the stacked hourglass network, and welding paths are determined by assistant symbols with geometric information. Additionally, the image stitching algorithm is used to obtain a broad view of the seam image for subsequent welding processes. The proposed method achieves 90.6% of recall under different lighting conditions. Furthermore, comparative experimental results indicate that the proposed method is robust and accurate for seam detection and localization.

A vision-based method for narrow weld trajectory recognition of arc welding robots

A vision-based method for narrow weld trajectory recognition of arc welding robots

Research on trajectory planning of sub-assembly and unit-assembly robot in shipbuilding industry

In shipbuilding, welds in sub-assemblies and unit-assemblies are long and various type. The robot arm needs the gantry movement to cooperate with the welding operation. However, there are joint redundancy and double-arm coordination problems, which cause difficulties in the welding trajectory planning. This paper proposes a strategy of decoupling the dual-arm system into a single-arm system for separate planning and prioritizing joint motion, and conducts welding trajectory planning research based on Matlab’s Simscape Multibody. Firstly, the 3D model of the welding workstation is established, its structure, the operation mode of the joint link and the degree of freedom are analyzed. The 17-DOF welding workstation is decoupled into a single-arm 9-DOF system, a kinematic model is established, kinematic analysis is carried out. The Matlab robot toolbox is used for simulation verification. Then the discretization of weld types and poses in sub-assemblies and unit-assemblies workpieces is analyzed. Finally, the welding workstation and workpiece model are imported into Simscape Multibody of Matlab to simulate the trajectory planning of different types of welds. The simulation results prove the effectiveness of the strategy and have certain reference significance for on-site welding in shipbuilding.

Didactic prototype of a robotic manufacturing cell to program welding trajectories in a frame

This project shows the integration of a didactic prototype of a robotic manufacturing cell for the programming of welding trajectories in a chassis. The purpose of this project is to integrate the robotic cell through the interaction of the controller of an industrial robot, a programmable logic controller (PLC) and a power transmission system for the positioning of a chassis for didactic purposes for students studying engineering in the area of Industrial Automation. To carry out the project, a scale chassis for a tractor and its support base was first manufactured, then the control of a stepper motor was carried out through a SIMATIC S7-1200 CPU 1214C PLC and a microstep driver module. The programming software COSIMIR from FESTO was also used to program the welding trajectories in a Mitsubishi RV-2AJ robot with 5 degrees of freedom that interacts with the PLC through input and output digital modules. Finally, it was possible to obtain a functional prototype of a robotic manufacturing cell that can be used for teaching robot trajectory programming and that is closely related to industrial machines.