Weld seam detection using computer vision for robotic Arc Welding

Dinham, Mitchell

doi:10.1109/coase.2012.6386339

Cited by 23 publications

(9 citation statements)

References 10 publications

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“…In this article, with the assumption that plant parameters are specified, a sliding mode controller has been used to deal with nonparametric uncertainties. For this purpose, a sliding surface is first defined in equations ( 6) and (7).…”

Section: Controller Designmentioning

confidence: 99%

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An adaptive approach to compensate seam tracking error in robotic welding process by a moving fixture

Ebrahimpour

Fesharakifard

Rezaei

2018

International Journal of Advanced Robotic Systems

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Welding is one of the most common method of connecting parts. Welding methods and processes are very diverse. Welding can be of fusion or solid state types. Arc welding, which is classified as fusion method, is the most widespread method of welding, and it involves many processes. In gas metal arc welding or metal inert gas–metal active gas, the protection of the molten weld pool is carried out by a shielding gas and the filler metal is in the form of wire which is automatically fed to the molten weld pool. As a semi-metallic arc process, the gas metal arc welding is a very good process for robotic welding. In this article, to conduct the metal active gas welding torch, an auxiliary ball screw servomechanism is proposed to move under a welder robot to track the welded seam. This servomechanism acts as a moving fixture and operates separately from the robot. At last, a decentralized control method based on adaptive sliding mode is designed and implemented on the fixture to provide the desired motion. Experimental results demonstrate an appropriate accuracy of seam tracking and error compensation by the proposed method.

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Section: Controller Designmentioning

confidence: 99%

“…In this method, the accuracy is less than 1 mm in welding seam tracking. 6,7 Laser vision sensor have been successfully employed in robust seam tracking. The feature point was obtained using the traditional morphological method before welding and determine the tracked region.…”

Section: Introductionmentioning

confidence: 99%

An adaptive approach to compensate seam tracking error in robotic welding process by a moving fixture

Ebrahimpour

Fesharakifard

Rezaei

2018

International Journal of Advanced Robotic Systems

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“…However, the adaptation strategy has to be reconfigured for each new part geometry and is limited to simple workpiece geometries. Other approaches focus on computer vision to reconstruct the entire geometry of the part based on 3D sensor data [7], [8]. Geometry reconstruction however needs complex computations and discards important CAD data information, like e.g.…”

Section: Related Workmentioning

confidence: 99%

Deformable shape registration using surface-based free-form deformations in robotic welding

Kuss

Ksensow

Verl

2017

2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)

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In this paper, a novel approach is presented to perform a deformable shape registration of workpiece geometries in robotic welding. Based on the free-form deformation (FFD) method, a surface-based extension FFDS is presented where the initial shape of the control points lattice corresponds to the shape of the surface to be deformed. A point-based registration is performed using the sum of least squares criteria to obtain the non-rigid shape transformation. The deformation is applied to the computer-aided design (CAD) model of the workpiece geometry preserving its topology for subsequent robot path planning to move a welding gun along the deformed part contours. The performance of the presented approach is evaluated using virtual test data and rea measurements of a welding workpiece obtained with a 3D sensor. Moreover, the performance of this approach is compared to state of the art deformable shape registration revealing a significant reduction of computation time and the usability for path planning in robotic welding

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“…Another work proposed using Hough transform to detect the outside boundary of the weldment (Dinham et al, 2012). The algorithms is capable of not only detecting straight line weld seams but can be used for curved, saw tooth and other various shapes of weld seams.…”

Section: Analysis Of Indentify Detect and Tracked Of Weld Seams Pathmentioning

confidence: 99%

A Review Paper on Vision Based Identification, Detection and Tracking of Weld Seams Path in Welding Robot Environment

Shah¹,

Sulaiman²,

Shukor³

et al. 2016

MAS

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<p><span style="font-size: 10.5pt; font-family: 'Times New Roman','serif'; mso-bidi-font-size: 10.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">Welding is an important technology especially for joining between two metals, fabricated and repairing metals products in manufacturing industries such as in automotive industries. To increase the productivity and lower cost, today the welding operation in industries introduces the welding robot. The main challenges to using welding robot is time taken to program robot path for a new job in low to medium volume manufacturing industries. This paper begins with the review of identified, detected and tracked the weld seams path with different type of welding in welding environment. Next, a review of analysis an identified and detect the weld seams path approaches is included with advantages, drawback and limitation. This paper is concluded by a comprehensive summary which discussed the disadvantages and limitation of a robust approach in each stage. The improvement of a new approach in each stage depends on the lack, limitation and the results which are expected from the work.</span></p>

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Weld seam detection using computer vision for robotic Arc Welding

Cited by 23 publications

References 10 publications

An adaptive approach to compensate seam tracking error in robotic welding process by a moving fixture

An adaptive approach to compensate seam tracking error in robotic welding process by a moving fixture

Deformable shape registration using surface-based free-form deformations in robotic welding

A Review Paper on Vision Based Identification, Detection and Tracking of Weld Seams Path in Welding Robot Environment

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