Extraction of Weld Seam in 3D Point Clouds for Real Time Welding Using 5 DOF Robotic Arm

Patil, Vrushali; Patil, Indraneel; Kalaichelvi, V.; Karthikeyan, R.

doi:10.1109/iccar.2019.8813703

Cited by 18 publications

(7 citation statements)

References 16 publications

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“…The first one is the execution time t ex (wall-clock time is used). The second one is the average relative distance d e,i of the points in S i to the found segment i, where S i is a set of points with the shortest distance (4) to segment i compared to the other segments (7). Both the execution time and the fit error were calculated for 10 consequent runs of the algorithm to simulate scanning multiple sections along the weld groove.…”

Section: Resultsmentioning

confidence: 99%

“…The first iteration starts. The points are divided into point sets using (7), which is shown in three different colors. Then, the translation (8) (for i = 1) and the rotation to the line fit L 1 is applied for segment 1.…”

Section: Initial Finalmentioning

confidence: 99%

“…Computer vision sensors are often used for weld groove (or weld seam) tracking, feature extraction, or groove geometry parametrization. Although, in some applications, analysis of global 3-dimensional (3D) point clouds [ 7 , 8 , 9 ] or passive light systems [ 10 ] can be used for global weld seam detection, most of the research in weld seam tracking and groove feature extraction is done using local structured light sensors. That is, sensors projecting structured light into the region of interest (ROI).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Laser Scanning and Parametrization of Weld Grooves with Reflective Surfaces

Cibicik

Tingelstad

Egeland

2021

Sensors

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This paper presents a novel weld groove parametrization algorithm, which is developed specifically for weld grooves in typical stub and butt joints between large tubular elements. The procedure is based on random sample consensus (RANSAC) with additionally proposed correction steps, including a corner correction step for grooves with narrow root weld, and an iterative error elimination step for improving the initially obtained data fit. The problem of curved groove sides (due to the pipe geometry) is attributed and solved. In addition, the procedure detects and eliminates several types of data noise due to laser line reflections. The performance of the procedure is studied experimentally using small-scale test objects, which have been ground using typical industrial power tools to achieve a realistic level of reflections. The execution times and data fit errors of the proposed procedure are compared to a procedure based on a more conventional RANSAC approach for line segment detection.

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Section: Resultsmentioning

confidence: 99%

Section: Initial Finalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Laser Scanning and Parametrization of Weld Grooves with Reflective Surfaces

Cibicik

Tingelstad

Egeland

2021

Sensors

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“…Compared to the algorithm used for acute edge detection, this algorithm relied more heavily on the point cloud data provided by the stereo camera being used, as it is the primary sensor. Other works have also used a similar offline technique [30], allowing accurate, real-time information related to the structure at the time of operation.…”

Section: Obtuse Edge Detectionmentioning

confidence: 99%

Implementation of a Flexible and Lightweight Depth-Based Visual Servoing Solution for Feature Detection and Tracing of Large, Spatially-Varying Manufacturing Workpieces

et al. 2022

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This work proposes a novel solution for detecting and tracing spatially varying edges of large manufacturing workpieces, using a consumer grade RGB depth camera, with only a partial view of the workpiece and without prior knowledge. The proposed system can visually detect and trace various edges, with a wide array of degrees, to an accuracy of 15 mm or less, without the need for any previous information, setup or planning. A combination of physical experiments on the setup and more complex simulated experiments were conducted. The effectiveness of the system is demonstrated via simulated and physical experiments carried out on both acute and obtuse edges, as well as typical aerospace structures, made from a variety of materials, with dimensions ranging from 400 mm to 600 mm. Simulated results show that, with artificial noise added, the solution presented can detect aerospace structures to an accuracy of 40 mm or less, depending on the amount of noise present, while physical aerospace inspired structures can be traced with a consistent accuracy of 5 mm regardless of the cardinal direction. Compared to current industrial solutions, the lack of required planning and robustness of edge detection means it should be able to complete tasks more quickly and easily than the current standard, with a lower financial and computational cost than the current techniques being used within.

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“…As for the data for 3D welding seams, several effective extraction algorithms have been proposed. Patil et al [ 18 ] suggested a 3D point cloud welding seam clustering extraction algorithm independent of the shapes of workpieces. With the point cloud data processing algorithm determined based on the point cloud library (PCL) point cloud database, Patil et al extracted 3D welding seams, estimated the pose of the welding seams, and achieved the real-time seam tracking of butt joints using a five degrees of freedom (5-DOF) robotic arm.…”

Section: Introductionmentioning

confidence: 99%

Online Extraction of Pose Information of 3D Zigzag-Line Welding Seams for Welding Seam Tracking

Hong

Jia

Hong

et al. 2021

Sensors

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Three-dimensional (3D) zigzag-line welding seams are found extensively in the manufacturing of marine engineering equipment, heavy lifting equipment, and logistics transportation equipment. Currently, due to the large amount of calculation and poor real-time performance of 3D welding seam detection algorithms, real-time tracking of 3D zigzag-line welding seams is still a challenge especially in high-speed welding. For the abovementioned problems, we proposed a method for the extraction of the pose information of 3D zigzag-line welding seams based on laser displacement sensing and density-based clustering point cloud segmentation during robotic welding. after thee point cloud data of the 3D zigzag-line welding seams was obtained online by the laser displacement sensor, it was segmented using theρ-Approximate DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm. In the experiment, high-speed welding was performed on typical low-carbon steel 3D zigzag-line welding seams using gas metal arc welding. The results showed that when the welding velocity was 1000 mm/min, the proposed method obtained a welding seam position detection error of less than 0.35 mm, a welding seam attitude estimation error of less than two degrees, and the running time of the main algorithm was within 120 ms. Thus, the online extraction of the pose information of 3D zigzag-line welding seams was achieved and the requirements of welding seam tracking were met.

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Extraction of Weld Seam in 3D Point Clouds for Real Time Welding Using 5 DOF Robotic Arm

Cited by 18 publications

References 16 publications

Laser Scanning and Parametrization of Weld Grooves with Reflective Surfaces

Laser Scanning and Parametrization of Weld Grooves with Reflective Surfaces

Implementation of a Flexible and Lightweight Depth-Based Visual Servoing Solution for Feature Detection and Tracing of Large, Spatially-Varying Manufacturing Workpieces

Online Extraction of Pose Information of 3D Zigzag-Line Welding Seams for Welding Seam Tracking

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