Fig 1 - uploaded by Hashem Ghariblu
Content may be subject to copyright.
Source publication
The V-groove joint of thick wall intersecting pipes must be filled by multi-layer weld. The welding path of intersecting pipes is complicated, and hence multi-layer welds increase the complexity of the problem. This paper proposes a methodology for path planning of multi-layer weld of thick wall intersecting pipes. The methodology is based on measu...
Contexts in source publication
Context 1
... V-groove weld is a common joint in thick-walled welding. Figure 1(a) is a metallography picture that shows the V-groove joint characteristics including nine passes at six layers. The first pass, called root pass, is very important in welding. ...
Context 2
... example, the bevel angle in GTAW (Gas Tungsten Arc Welding) has to be chosen larger than what is selected for GMAW (Gas Metal Arc Welding) since the size of the welding torch in GTAW is bigger than the torch size of GMAW. Figure 1(b) shows the simplified state of Fig. 1(a). Although here each pass is either diamond (3, 5, and 7 passes) or Trapezius (1, 2, 4, 6, 8, and 9 passes), their geometry will change during intersecting pipe welding. ...
Context 3
... grooves, is selected so that the welding torch can manoeuvre along groove. For example, the bevel angle in GTAW (Gas Tungsten Arc Welding) has to be chosen larger than what is selected for GMAW (Gas Metal Arc Welding) since the size of the welding torch in GTAW is bigger than the torch size of GMAW. Figure 1(b) shows the simplified state of Fig. 1(a). Although here each pass is either diamond (3, 5, and 7 passes) or Trapezius (1, 2, 4, 6, 8, and 9 passes), their geometry will change during intersecting pipe welding. The torch orientation for GMAW welding of each pass is shown in Fig. 1(b), too. Figure 2 shows the weld electrode movement with weaving motion for welding the diamond ...
Context 4
... welding torch in GTAW is bigger than the torch size of GMAW. Figure 1(b) shows the simplified state of Fig. 1(a). Although here each pass is either diamond (3, 5, and 7 passes) or Trapezius (1, 2, 4, 6, 8, and 9 passes), their geometry will change during intersecting pipe welding. The torch orientation for GMAW welding of each pass is shown in Fig. 1(b), too. Figure 2 shows the weld electrode movement with weaving motion for welding the diamond passes and trapezius ...
Context 5
... , θ ab1 , Y ab values for each pass are determined. Table III shows these values for the case study. By inserting the values of Table III in Eq. (9), Z ab (Φ), θ ab (Φ) are determined as a function of Φ. Figure 9 shows the weld passes at the intersection of the pipes. In this figure, only the inner wall of the branch pipe is displayed. Meanwhile, Fig. 10 shows the welding passes in the side and front views, where the outer wall of the branch pipe is displayed. The algorithm succeeds to generate the weld path of each pass in the right ...
Context 6
... an example, Fig. 11 shows how the second pass of the final pass welding (path-32) varies. This figure illustrates the variations in position and orientation of angles with respect to the Φ angle around the branch pipe. The orientation angles are actually the first three components of the third column of Fig. 10. Welding passes in the side and front views. ...
Context 7
... an example, Fig. 11 shows how the second pass of the final pass welding (path-32) varies. This figure illustrates the variations in position and orientation of angles with respect to the Φ angle around the branch pipe. The orientation angles are actually the first three components of the third column of Fig. 10. Welding passes in the side and front views. Table III and is alternately repeated in the next three ...
Context 8
... order to complete the multi-layer welding path planning described above, the risk of electrode torch and arm collision to the pipes should be taken into account. Figure 13 shows how robot is mounted in the roof as gantry to decrease the collision probability, between welding torch and welded pipes. In this section, a trajectory planning methodology for application of intersecting pipes welding is presented considering collision avoidance between the welding torch and intersecting pipes as shown in Fig. 14. ...
Context 9
... Figure 13 shows how robot is mounted in the roof as gantry to decrease the collision probability, between welding torch and welded pipes. In this section, a trajectory planning methodology for application of intersecting pipes welding is presented considering collision avoidance between the welding torch and intersecting pipes as shown in Fig. 14. In this section, a trajectory planning methodology for application of intersecting pipes welding is presented, considering collision avoidance between the welding torch and intersecting ...
Context 10
... planning the trajectory, it is required to state the robot movement in its workspace with respect to the robot base. Therefore, Eq. (10) Torch T adds some welding characteristics to the path before the robot moves along the path (Fig. 10). These characteristics include weaving motion to widen the weld width, the electrode extension to control the melting pool, and the electrode angle relative to the work piece, defined in the welding by work and motion ...
Context 11
... solution to collision avoidance is realized by adding a kinematic constraint in the Cartesian space. In Fig. 16, the welding torch position at the second layer of our multi-layer weld case study is drawn ...
Context 12
... w angle (Fig. 15) has a constraint value in this case, so the robot wrist stands at the highest position with respect to the top surface of the main pipe. Figure 16(c) shows the B-spline curve of the robot wrist, which is drawn using four mentioned points. Although it seems that the collision is avoided in Fig. 16, it is not so useful. The reason is ...
Context 13
... w angle (Fig. 15) has a constraint value in this case, so the robot wrist stands at the highest position with respect to the top surface of the main pipe. Figure 16(c) shows the B-spline curve of the robot wrist, which is drawn using four mentioned points. Although it seems that the collision is avoided in Fig. 16, it is not so useful. ...
Context 14
... w angle (Fig. 15) has a constraint value in this case, so the robot wrist stands at the highest position with respect to the top surface of the main pipe. Figure 16(c) shows the B-spline curve of the robot wrist, which is drawn using four mentioned points. Although it seems that the collision is avoided in Fig. 16, it is not so useful. The reason is that if the robot tends to move exactly along the B-spline path, the end effector cannot follow the path, defined by Eq. (11). This is because B-spline is an approximate solution while Eq. (11) is an exact solution. Instead, to solve this problem, by employing the functional redundancy, the robot ...
Context 15
... cannot follow the path, defined by Eq. (11). This is because B-spline is an approximate solution while Eq. (11) is an exact solution. Instead, to solve this problem, by employing the functional redundancy, the robot wrist is not constrained to move along the B-spline curve, but it is allowed to move inside a volumetric ring named as safety ring (Fig. 17). The main features of the safety ring concept are presented in ref. [32]. The search into the null space to find the solution is done by adding a projection matrix to Eq. ...
Context 16
... simplify the problem, an ellipse instead of B-spline is used, which is difficult to formulate. Figure 18 shows the safety ring, generated by this ellipse. Eq. (13) tries to find ˙ q p that minimized D min . ...
Context 17
... this section, the results of implementing a safety ring to avoid the collision for welding layer 3 of our multi-layer weld case study are discussed. Figure 19 shows the robot wrist path, which is inside the safety ring and guarantees no collision. Figure 20 shows the history of joint angles of planned trajectory versus time. ...
Similar publications
This paper proposes an efficient algorithm for robust sensor placement with the purpose of recovering a source signal from noisy measurements. To model uncertainty on the spatially-variant sensors gain and on the spatially correlated noise, we assume that both are realizations of Gaussian processes. Since the signal to noise ratio (SNR) is also unc...
In this paper, a methodology for path distance and time synthetic optimal trajectory planning is described in order to improve the work efficiency of a robotic chainsaw when dealing with cutting complex timber joints. To demonstrate this approach one specific complicated timber joint is used as an example. The trajectory is interpolated in the join...
Robotic machining has obtained growing attention recently because of the low cost, high flexibility and large workspace of industrial robots (IRs). Multiple degrees of freedom of IRs improve the dexterity of machining while causing the problem of redundancy. Meanwhile, the performance of IRs, such as their stiffness and dexterity, is affected by th...
In order to improve the motion stability of a pointing mechanism, the trajectory planning and trajectory optimization were conducted. To obtain better motion stability, the jerk is required continuous. Therefore, higher order polynomial or B-spline are needed and the calculation amount increases in trajectory planning. A novel hybrid interpolation...
Citations
... By providing necessary data and parameters for establishing the subsequent groove model, a profound analysis of the spatial geometric interplay between the branch pipe and the main pipe allows for a more accurate understanding of pivotal factors in the welding process, laying a solid foundation for the construction of the actual groove model. Utilizing the multi-layer multi-pass continuous welding method [15] , a mathematical model for cutting the end of the jacket pipe was formulated through the form of intersecting test specimens. This framework encompasses the mathematical depiction of the spatial curve of the jacket pipe end diameter and the groove angle on the curve [12,16] . ...
The welding of intersecting structures requires multi-layer multi-pass welding. Manual welding of intersecting curves is being replaced by robotic welding due to its low efficiency and poor consistency. To enhance weld accuracy in intersecting structures and optimize multi-layer multi-pass planning in robot welding of large components, a realistic groove model is devised to address discrepancies between theoretical and actual groove surfaces. This include determining the size of the actual error angle, and the modeling of the actual groove in the intersecting structure. A method for groove error correction is then proposed based on this model. Firstly, the total and the error areas of the actual groove are calculated. Subsequently, the errors for each layer of the groove are computed from the total error area of the groove. Error correction for the groove layout is implemented layer by layer, resulting in an algorithm for multi-layer multi-pass groove layout correction. Additionally, a fitting function that closely approximates the groove profile is derived. This procedure finalizes the groove error correction, and through MATLAB simulation, the corrected intersecting structure groove is modeled, with results aligning with the error correction algorithm. Finally, further simulation and experimentation validate the feasibility, accuracy and effectiveness of the error correction algorithm for multi-layer multi-pass weld planning the actual intersecting structure groove, offering a theoretical foundation for the intricate weld planning required in robotic welding of intersecting components.
... It is also recognised as a promising field for intelligent trajectory correction and/or welding parameter modification [25]. A methodology based on measuring the electrode pose in multi-pass pipe welding is presented in [26] whereby the measured values are used to interpolate the path of each pass between two views to compensate for the path deviation around the pipe circumference. Digital twins of robotic cells have been accurately constructed as copies of the physical ones by exploiting kinematics linkages [27] or automatically reconstructed based on JSON files [28]. ...
This paper examines the issues pertaining to the design and development of robotic welding stations for large intricate products, by example of a fabric dying machine, combining large sheet metal parts into a shell and smaller parts in the form of pipes, ducts and flanges. The main relevant issues concern: (a) layout of the workstation considering the size and shape constraints of parts and the working volume of the robot, (b) design of the work-holding jigs and fixtures including checks for strength and allowable deformation, (c) definition of the poses of the robot along the welding path to comply with good welding practice and avoid collisions within joint position and speed capabilities and (d) automatic off-line programming of the robot. These issues are solved by exploiting constraint-based digital models of parts and, kinematic simulation and finite element analysis in interactive mode, which is demonstrably most efficient in the presence of variable welding seam cross-section, complex seam 3D-geometry and constrained workspace. Furthermore, the challenge of substantial absolute deviation between actual and nominal shape of large sheet metal parts is dealt with by applying photogrammetry in order to superimpose the actual welding seam shape on the digital model. Appropriate data interfaces between digital models are used in order to automate the work flow up to the automatic generation of robot programs from the computed coordinates of the robot joints.
... In many applications, like spot welding or multi position welding in a large workspace it is necessary that a robotic manipulator find a collision free path to move from earlier working point to the new working point [1,2]. The goal of motion planning in these cases is to find a collision free path. ...
The main objective of a motion planning algorithm is to find a collision-free path in the workspace of a robotic
manipulator in a point-to-point motion. Among the various motion planning methods available, sample-based
motion planning algorithms are easy to use, quick and powerful in redundant robotic systems applications. In this
study, different sampling-based motion planning algorithms are employed to select the most appropriate method for
efficient collision-free motion planning. As a case study, finding a collision-free robotic displacement for welding
a main pipe with other intersecting pipes and joints is considered. The robotic manipulator employed in this study
has seven degrees of freedom, where six degrees are related to the manipulator joints and one degree is related to
its base linear movement suspended from ceiling. Five criteria, time, path length, path time, path smoothness and
process time are used to evaluate the efficiency of different sample-based motion planning algorithms. Finally, a
smaller set of more efficient algorithms are introduced based on the defined efficiency evaluation criteria.
... After obtaining the C-Space, the rest is related to path planning algorithms which can be A* [34,35], many variants of rapidly exploring random tree (RRT) [36], probabilistic roadmap (PRM) [37], particle swarm optimization (PSO) [38], and so forth. Apart from these, some papers published recently have also provided guidance [39,40,41,42,43]. In this study, A* path planning algorithm will be used. ...
Instead of using the tedious process of manual positioning, an off-line path planning algorithm has been developed
for military turrets to improve their accuracy and efficiency. In the scope of this research, an algorithm is proposed
to search a path in three different types of configuration spaces which are rectangular-, circular-, and torus-shaped
by providing three converging options named as fast, medium, and optimum depending on the application. With
the help of the proposed algorithm, 4-dimensional (D) path planning problem was realized as 2-D + 2-D by using
six sequences and their options. The results obtained were simulated and no collision was observed between any
bodies in these three options.
A collision-free path planning method is proposed based on learning from demonstration (LfD) to address the challenges of cumbersome manual teaching operations caused by complex action of yarn storage, variable mechanism positions, and limited workspace in preform weaving. First, by utilizing extreme learning machines (ELM) to autonomously learn the teaching data of yarn storage, the mapping relationship between the starting and ending points and the teaching path points is constructed to obtain the imitation path with similar storage actions under the starting and ending points of the new task. Second, an improved rapidly expanding random trees (IRRT) method with adaptive direction and step size is proposed to expand path points with high quality. Finally, taking the spatical guidance point of imitation path as the target direction of IRRT, the expansion direction is biased toward the imitation path to obtain a collision-free path that meets the action yarn storage. The results of different yarn storage examples show that the ELM-IRRT method can plan the yarn storage path within 2s–5s when the position of the mechanism changes in narrow spaces, avoiding tedious manual operations that program the robot movements, which is feasible and effective.
The welding process for medium and thick plates typically involves multi-layering and multi-channeling, but its quality and reliability require further improvement. Therefore, this study introduces the convolution neural network algorithm to establish a deep learning model for weld seam recognition. Additionally, the structured light imaging method is used to accurately position the V-shaped weld groove. Meanwhile, a machine vision based multi-layer and multi-pass dynamic routing planning algorithm was also studied and designed, and a contour feature point recognition algorithm for the filling layer was developed. Thus, dynamic routing planning is achieved. It is demonstrated that the difference between the coordinates acquired by the deep learning model and the ideal region decreases steadily and reaches a minimum of (200,80). The confidence level of weld seam detection gradually increases with the adjustment of the welding robot, reaching a maximum of 98%. The confidence level of the detected feature points reaches 100%. In the meantime, the remaining height of the fusion after welding is 2.5mm. There are no negative phenomena present on the surface of the weld seam, meeting the necessary process requirements. Such discrepancies as undercut, incomplete penetration, slag inclusion, and porosity are absent. It shows that the welding technology based on machine vision has strong feasibility, effectively improves the automation level and efficiency of welding technology, and provides reliable technical support for the development of modern machine vision welding technology.
In practical engineering, overlaying on curved surfaces with constantly changing gravity components usually leads to various defects. In this study, Inconel 625 was downward overlaid on curved surface by cold metal transfer (CMT) process with varied heat input, and the dynamic behavior of the molten pool was observed by high speed camera. At high heat input (0.422 kJ/mm and 0.372 kJ/mm), the volume of the molten pool continued to expand due to the downward flow of the liquid, ultimately leading to overflow or instability defects. In contrast, low heat input (0.230 kJ/mm) will make the molten pool shrink during overlaying, which was attributed to the continuously weakened gravity, the relative position between the heat source and the molten pool was then changed. A well-formed and defect-free weld overlay was obtained at optimized heat input 0.230–0.280 kJ/mm. Compared with gas tungsten arc welding (GTAW) process, the weld overlay obtained employing CMT process has the largest thickness and the most uniform fusion line, while the content of Fe element was less than 0.5%. It was confirmed that CMT technology has great potential for overlaying on curved surface.
