Automatic Touch-Up of Welding Paths Using 3D Vision

Njaastad, Eirik B.; Egeland, Olav

doi:10.1016/j.ifacol.2016.12.164

Cited by 10 publications

(3 citation statements)

References 13 publications

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“…The weld joints or seams are then typically located by performing one or more measurements around the nominal starting point of each weld joint before welding. Alternatively, several weld joint positions and orientations can be found simultaneously, but less accurately, by estimating the full 3D pose of the workpiece [9].…”

Section: State Of the Artmentioning

confidence: 99%

Supporting robotic welding of aluminium with a laser line scanner-based trigger definition method

Marco-Rider

Tran

Njaastad

et al. 2022

2022 IEEE 20th International Conference on Industrial Informatics (INDIN)

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Automation and the use of robots for welding operations is an important research topic. Being able to automate and, thus, save time for setting up and using robotic welding for complex, large-scale structures made of reflective materials, such as aluminium, will provide clear economic and competitive advantages. However, challenges coming from the ability to accurately detect and calibrate the robot for a given physical workpiece in addition to noises, such as the reflections, make it hard to develop and demonstrate a feasible automation solution. This paper proposes combining laser line scanning technology with CAD-based analysis of a workpiece geometry to support the identification of relevant elements of the workpiece in the physical world and thus support welding operations. An extendable trigger definition method is proposed to identify features of interest in a workpiece. The method can potentially support the execution of welding sequences, which in our case can be represented as a sequence of triggers that have to be observed and followed at the robot runtime to weld the workpiece together.

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Section: State Of the Artmentioning

confidence: 99%

Supporting robotic welding of aluminium with a laser line scanner-based trigger definition method

Marco-Rider

Tran

Njaastad

et al. 2022

2022 IEEE 20th International Conference on Industrial Informatics (INDIN)

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“…In contrast to ideal objects, which are three-dimensional geometric models built via computer-aided design (CAD), real parts may have some geometric deviations [14], so the laser beam trajectory built on a three-dimensional model can pass by the joint of parts, even in the case of an ideal interface of coordinate systems according to which the robot's control program was created.…”

Section: Introductionmentioning

confidence: 99%

Development of Mechanisms for Automatic Correction of Industrial Complex Tools in the Preprocessing of Laser Welding for Small-Scale and Piece Production Using Computer Vision

2020

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This paper is devoted to the development and improvement of mechanisms for the functioning of an automated system for correcting the points of the planned trajectory of the tool of a laser robotic welding complex in the pre-process. Correction of the points of the planned trajectory is carried out in two stages: in the first stage, the focal laser radiation is corrected; in the second stage, the position of the tool is corrected. Correction of the focal laser radiation is carried out in conjunction with the automated focusing of the camera by moving the tool of the industrial complex along its own axis. The functioning of position correction mechanisms is based on methods for recognizing the edges of the gap line to be welded from the image obtained from the charge-coupled device (CCD) camera. The edges of the gap to be welded in the image are segmented using threshold selection. The boundaries of the threshold selection segment are the extreme values of the pixel distribution of the entire image in brightness. For unambiguous recognition of the desired edges based on the segmentation result, the features defining them as a pair of continuous, conditionally parallel lines are formalized. Relative to the recognized pair of edges, the correct position of the planned trajectory point relative to the current position of the welding head is determined. To transfer the correct position, we formalized the calculation model and chose an arbitrary point in the flat image in the workspace laser robotic welding complex, considering the orientation of the tool and the position of the camera. The results obtained made it possible to develop a correction system and successfully test it in the industrial complex.

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“…To build a flexible manufacturing system, adopting an intelligent robot system is essential to distinguish workpieces within a workspace, perceive a situation and manipulate themselves autonomously [1]. Among the intelligent robot systems, vision-based robot systems have been developed continuously to improve the quality and efficiency of the manufacturing system such as arc welding, materials handling, painting and even assembly [2][3][4]. Especially, picking up an object and assembling it to another subsystem accurately is the most important task in an automated manufacturing system [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A Novel Indirect Calibration Approach for Robot Positioning Error Compensation Based on Neural Network and Hand-Eye Vision

Cao

Lee

2019

Applied Sciences

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It is well known that most of the industrial robots have excellent repeatability in positioning. However, the absolute position errors of industrial robots are relatively poor, and in some cases the error may reach even several millimeters, which make it difficult to apply the robot system to vehicle assembly lines that need small position errors. In this paper, we have studied a method to reduce the absolute position error of robots using machine vision and neural network. The position/orientation of robot tool-end is compensated using a vision-based approach combined with a neural network, where a novel indirect calibration approach is presented in order to gather information for training the neural network. In the simulation, the proposed compensation algorithm was found to reduce the positional error to 98%. On average, the absolute position error was 0.029 mm. The application of the proposed algorithm in the actual robot experiment reduced the error to 50.3%, averaging 1.79 mm.

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Automatic Touch-Up of Welding Paths Using 3D Vision

Cited by 10 publications

References 13 publications

Supporting robotic welding of aluminium with a laser line scanner-based trigger definition method

Supporting robotic welding of aluminium with a laser line scanner-based trigger definition method

Development of Mechanisms for Automatic Correction of Industrial Complex Tools in the Preprocessing of Laser Welding for Small-Scale and Piece Production Using Computer Vision

A Novel Indirect Calibration Approach for Robot Positioning Error Compensation Based on Neural Network and Hand-Eye Vision

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