Vision-based control of wire extension in GMA welding

Lertrusdachakul, Intuon; Mathieu, Alexandre; Aubreton, Olivier

doi:10.1007/s00170-014-6290-9

Cited by 13 publications

(6 citation statements)

References 10 publications

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“…Hung et al [25] proposed using machine vision techniques to assist the resharpening process of micro-drills, which make it possible to automatically obtain all required grinding parameters. In addition, many successful applications have been achieved in the welding domain [26][27][28].…”

Section: Machine Vision Applications In Manufacturingmentioning

confidence: 99%

Deterministic removal strategy for machine vision assisted scanning micro electrochemical flow cell

Guo

Qian

Reynaerts

2018

Journal of Manufacturing Processes

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Small cavities on a sliding surface can improve lubrication performance, which has been verified by many researchers. Electrochemical machining (ECM) is an effective way to fabricate this kind of small cavities on a large scale. When the diameter and the removal volume for the cavities are specified, it is still required to efficiently determine the appropriate machining parameters. This paper presents a machine vision based control system for an ECM variant: the scanning micro electrochemical flow cell (SMEFC). The aim of this system is to control the diameter of the cavity in real-time. With the assistance of machine vision, fast acquisition of the machining parameters for the specified diameter and the specified removal volume is possible. The system configuration is first explained in detail, including hardware and software configuration and the image processing algorithms. The latter are based on the Shi-Tomasi corner detector and are used for feedback control, stability and symmetry evaluation of the electrolyte droplet. For convenience, all of these functions have been integrated into a self-developed unified G-code interface. Furthermore, the theoretical explanation for controlling the machining process by vacuum gap (VG) tuning has been investigated through a two-phase flow simulation model, which revealed how the VG influences the shear rate and the pressure difference near the meniscus. Finally, a case study shows how to use the proposed strategy to get suitable machining parameters for a cavity with a diameter of 900 μm and a target removal volume of 0.03 mm 3 . This demonstrates the availability of a deterministic removal strategy.

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Section: Machine Vision Applications In Manufacturingmentioning

confidence: 99%

Deterministic removal strategy for machine vision assisted scanning micro electrochemical flow cell

Guo

Qian

Reynaerts

2018

Journal of Manufacturing Processes

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“…μ L is its mean and σ L is its variance. Combining (1)- (13), the model of the intensity distribution for the image with reflected laser lines is obtained as shown in (14) at the bottom of the page.…”

Section: Image Analysismentioning

confidence: 99%

“…In [12], the metal transfer information was extracted by a laser backlight technique with a camera, a laser beam, and a complex optical system. In [13], the wire extension (the ending position and the starting position of the melt wire) is measured online with a camera and light illumination. In [14], a simplified backlight system was proposed to measure both the metal transfer information and the wire extension.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Recognition and Characterization of the Reflected Laser Lines for Robotic Gas Metal Arc Welding

Wang

2017

IEEE Trans. Ind. Inf.

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Unsupervised recognition of the reflected laser lines from the arc-light-modified background is prerequisite for the subsequent measurement and characterization of the weld pool shape, which is of great importance for the modeling and control of robotic arc welding. To facilitate the unsupervised recognition, the reflected laser lines need to be segmented as accurate as possible, which requires the segmented laser lines to be as continuous as possible to decrease the adverse effect of the noise blobs. In this paper, the intensity distribution caused by the arc light in the captured image is modeled. Based on the model, an efficient and robust approach is proposed, and it comprises six parts: reduction of the uneven image background by a difference operation, spline enhancement to remove the fuzziness, a gradient detection filter to eliminate the uneven background further, segmentation by an effective threshold selection method, removal of the noise blobs adaptively, and clustering based on the online computed slope of the laser line. After the laser line is clustered, a second-order polynomial is fitted to it. Finally, the weld pool is characterized by the parameters of the clustered laser line and its fitted polynomial. Experimental results verified that the proposed approach for unsupervised reflected laser line recognition is significantly superior to the state-of-the-art approach in terms of recognition accuracy.

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“…The SAR system consists of the following components: projectors (NEC NP-PA500UG), cameras, 3 KVM, 4 power rails and network switches (HUG-500SE). Since the projectors were to be mounted overhead and the lens of the projectors was approximately 4.3 m (projector 1-back) and 4.5m (projector 2-front) away from the centre of the panel, the overall weight (80 kg per enclosure) and projection capability had to be considered.…”

Section: Prototype Hardwarementioning

confidence: 99%

“…Unlike the vision-based monitoring system introduced by [4] which was limited to detection and evaluation of spot-welds, the goal of our work is to explore and investigate the use of augmented reality in the production environment. Our solution is to develop a projector based Spatial Augmented Reality (SAR) system that can be integrated at production stations as required and works alongside existing plant infrastructure.…”

Section: Introductionmentioning

confidence: 99%

Use of projector based augmented reality to improve manual spot-welding precision and accuracy for automotive manufacturing

Doshi

Smith

Thomas

et al. 2016

Int J Adv Manuf Technol

117

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This paper presents the use of a projector-based spatial augmented reality system in an industrial quality assurance setting to highlight spot-weld locations on vehicle panels for manual welding operators. The aim of this work is to improve the precision and accuracy of manual spot-weld placements with the aid of visual cues as a proactive step by the automotive manufacturer to enhance product quality. The prototype system was deployed at General Motors (GM) Holden plant in Elizabeth, Australia on the production line building Holden Cruze vehicles. Production trials were conducted and techniques developed to analyse and validate the precision and accuracy of spot-welds both with and without the visual cues. A reduction of 52 % of the standard deviation of manual spot-weld placement was observed when using augmented reality visual cues. The average standard deviation with-AR assistance (19 panels and 114 spot-welds) was calculated at 1.94 mm compared to without-AR (45 panels and 270 spot-welds) at 4.08 mm. All welds were within the required specification and panels evaluated in this study were used as the final product made available to consumers. The visual cues enabled operators to spot-weld at a higher degree of precision and accuracy.

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Vision-based control of wire extension in GMA welding

Cited by 13 publications

References 10 publications

Deterministic removal strategy for machine vision assisted scanning micro electrochemical flow cell

Deterministic removal strategy for machine vision assisted scanning micro electrochemical flow cell

Unsupervised Recognition and Characterization of the Reflected Laser Lines for Robotic Gas Metal Arc Welding

Use of projector based augmented reality to improve manual spot-welding precision and accuracy for automotive manufacturing

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