Multi-pass path planning for thick plate by DSAW based on vision sensor

Sensor Review ◽  
2014 ◽  
Vol 34 (4) ◽  
pp. 416-423 ◽  
Author(s):  
Chengdong Yang ◽  
Zhen Ye ◽  
Yuxi Chen ◽  
Jiyong Zhong ◽  
Shanben Chen
Keyword(s):  
Image Processing ◽  
Path Planning ◽  
Arc Welding ◽  
Thick Plate ◽  
Welding Parameters ◽  
Automatic Welding ◽  
Vision Sensor ◽  
Characteristic Parameters ◽  
Content Type ◽  
Passive Vision Sensor

Purpose – This paper aims to solve the problem that the changing of groove size and assembly gap would affect the precision of the multi-pass path planning and the welding quality and realize the automatic welding of a thick plate. Design/methodology/approach – First, a double-sided double arc welding (DSAW) system with a self-designed passive vision sensor was established, then the image of the groove was captured and the characteristic parameters of groove were extracted by image processing. According to the welding parameters and the extracted geometry size, multi-pass path planning was executed by the DSAW system. Findings – A DSAW system with a self-designed passive vision sensor was established which can realize the welding thick plate by double-sided double arc by two robots. The clear welding image of the groove was acquired, and an available image processing algorithm was proposed to accurately extract the characteristic parameters of the groove. According to the welding parameters and the extracted geometry size, multi-pass path planning can be executed by the DSAW system automatically. Originality/value – Gas metal arc welding is used for root welding and filler passes in DSAW. Multi-pass path planning for thick plate by Double-sided Double Arc Welding (DSAW) based on vision sensor was proposed.

Multi-Pass Route Planning for Thick Plate of Low Alloy High Strength Steel by Double-Sided Double Arc Welding

2012 ◽  
Vol 590 ◽  
pp. 28-34 ◽  
Author(s):  
Cheng Dong Yang ◽  
H.Y. Huang ◽  
H.J. Zhang ◽  
Y.X. Chen ◽  
San Ben Chen
Keyword(s):  
High Strength Steel ◽  
Arc Welding ◽  
Thick Plate ◽  
High Efficiency ◽  
Route Planning ◽  
High Strength ◽  
Welding Parameters ◽  
Welding Torch ◽  
Welding System

Double-sided double arc welding (DSDAW), a high efficiency method for welding thick plate of low alloy high strength steel which does not require back chipping is used in this paper, research on multi-pass route planning for thick plate of low alloy high strength plate by double-sided double arc welding. Firstly, establish a double-sided double arc welding system that can realize thick plate of low alloy high strength steel double-sided double arc welding by double robots. Then, Propose the multi-pass route planning for thick plate of low alloy high strength steel by double-sided double arc welding by means of misplaced welding. According to the welding parameters and the geometry size of groove, plan the layers, the number of beads and the concrete position of the welding torch for each bead. Finally, the welding experiment has been done to verify the effectiveness of multi-pass route planning. The results of welding experiment are approximately agreement with the multi-pass route planning results. The backing weld can get better appearance in the front and guarantee fusion penetration in the back simultaneously. On the basis of the multi-pass welding route planning, good fusion and leveling interface can be obtained after filler passes.

Control of the weld quality using welding parameters in a robotic welding process

2018 ◽  
Vol 1 (87) ◽  
pp. 32-40 ◽  
Author(s):  
M.-H. Park ◽  
B.-J. Jin ◽  
T.-J. Yun ◽  
J.-S. Son ◽  
C.-G. Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Arc Welding ◽  
Gma Welding ◽  
Welding Process ◽  
Welding Parameters ◽  
Automatic Welding ◽  
Welding Quality ◽  
Arc Welding Process ◽  
Intelligent Models ◽  
Welding System

Purpose: Since the welding automations have widely been required for industries and engineering, the development of the predicted model has become more important for the increased demands for the automatic welding systems where a poor welding quality becomes apparent if the welding parameters are not controlled. The automated welding system must be modelling and controlling the changes in weld characteristics and produced the output that is in some way related to the change being detected as welding quality. To be acceptable a weld quality must be positioned accurately with respect to the joints, have good appearance with sufficient penetration and reduce low porosity and inclusion content. Design/methodology/approach: To achieve the objectives, two intelligent models involving the use of a neural network algorithm in arc welding process with the help of a numerical analysis program MATLAB have been developed. Findings: The results represented that welding quality was fully capable of quantifying and qualifying the welding faults. Research limitations/implications: Welding parameters in the arc welding process should be well established and categorized for development of the automatic welding system. Furthermore, typical characteristics of welding quality are the bead geometry, composition, microstructure and appearance. However, an intelligent algorithm that predicts the optimal bead geometry and accomplishes the desired mechanical properties of the weldment in the robotic GMA (Gas Metal Arc) welding should be required. The developed algorithm should expand a wide range of material thicknesses and be applicable in all welding position for arc welding process. Furthermore, the model must be available in the form of mathematical equations for the automatic welding system. Practical implications: The neural network models which called BP (Back Propagation) and LM (Levenberg-Marquardt) neural networks to predict optimal welding parameters on the required bead reinforcement area in lab joint in the robotic GMA welding process have been developed. Experimental results have been employed to find the optimal algorithm to predict bead reinforcement area by BP and LM neural networks in lab joint in the robotic GMA welding. The developed intelligent models can be estimated the optimal welding parameters on the desired bead reinforcement area and weld criteria, establish guidelines and criteria for the most effective joint design for the robotic arc welding process. Originality/value: In this study, intelligent models, which employed the neural network algorithms, one of AI (Artificial Intelligence) technologies have been developed to study the effects of welding parameters on bead reinforcement area and to predict the optimal bead reinforcement area for lab joint in the robotic GMA welding process. BP (Back Propagation) and LM (Levenberg-Marquardt) neural network algorithm have been used to develop the intelligent model.

scholarly journals REPLACEMENT OF MANUAL GMAW WELDING BY FCAW SEMI-AUTOMATIC WELDING

2021 ◽  
Vol 2021 (2) ◽  
pp. 4342-4347
Author(s):  
MARIAN SIGMUND ◽  
◽  
TADEAS CICHA
Keyword(s):  
Carbon Steel ◽  
Arc Welding ◽  
Gas Metal Arc Welding ◽  
Welding Parameters ◽  
Automatic Welding ◽  
Cored Wire ◽  
Metal Arc Welding ◽  
Flux Cored Arc Welding ◽  
Welding Procedure ◽  
Welding Processes

The article describes a replacement and benefits between manual gas metal arc welding (GMAW) with solid wire and semi-automatic flux-cored arc welding (FCAW) with metal flux-cored wire for a specific application of a welded steel compensator used for connecting piping systems to form larger units. For the replacement of the technologies and improvement of the welding efficiency and productivity a specific type of carbon steel mounting insert, DN300 PN16, was selected. Since these pressure parts are subject to the directive 2014/68/EU, both the welding processes have to meet the same welding quality requirements. In particular, they are the welding procedure qualification report (WPQR) and the welder’s or welding operator’s qualification in accordance with valid European standards. Based on this requirement, a sample was selected so that it would cover the widest possible range of carbon steel mounting inserts produced. This article describes the whole experiment including the selection of the right equipment and filler material, finding the ideal welding parameters, and the subsequent qualification of the welding procedure and the operator with emphasis on the largest possible increase in the welding speed and productivity for these specific weldments.

Correlation between Welding Parameter and Bead Geometry of Flux Cored Arc Welding (FCAW) in Horizontal Position (2F)

2014 ◽  
Vol 564 ◽  
pp. 549-554
Author(s):  
Nik Mohd Baihaki Abd Rahman ◽  
Abdul Ghalib Tham ◽  
Sunhaji Kiyai Abas ◽  
Razali Hassan ◽  
Yupiter H.P. Manurung ◽  
...  
Keyword(s):  
Heat Input ◽  
Arc Welding ◽  
Weld Bead ◽  
Welding Parameter ◽  
Welding Parameters ◽  
Bead Geometry ◽  
Automatic Welding ◽  
Trend Line ◽  
Flux Cored Arc Welding ◽  
Good Prediction Accuracy

A robotic system can convert the semi-automatic Flux Cored Arc Welding (FCAW) to an automatic welding system. The critical requirement in automated welding process is that the optimal welding parameter has to be set before welding start. These input welding parameters cannot be easily guessed unless one has the knowledge. Only very specific range of heat input that produces quality weld deposition. The correlation between the heat input and fillet weld bead can be displayed in a unique trend-line graph. Mathematical formulas that match the trend-line profile can be used to create a prediction calculator that displays the digital values of weld bead geometry when welded at a specific range of heat input. Small Mean Absolute Deviation between predicted and measured geometry means good prediction accuracy. With this correlation chart, the welding parameter for quality weld bead can be selected and the geometry of FCAW weld deposition in 2F position can be predicted accurately without trial and error.

Hand–eye calibration of arc welding robot and laser vision sensor through semidefinite programming

2018 ◽  
Vol 45 (5) ◽  
pp. 597-610 ◽  
Author(s):  
Yanbiao Zou ◽  
Xiangzhi Chen
Keyword(s):  
Semidefinite Programming ◽  
Coordinate System ◽  
Arc Welding ◽  
Rotation Matrix ◽  
Feasibility Problem ◽  
Vision Sensor ◽  
Welding Robot ◽  
Content Type ◽  
Laser Vision Sensor ◽  
Laser Vision

PurposeThis paper aims to propose a hand–eye calibration method of arc welding robot and laser vision sensor by using semidefinite programming (SDP).Design/methodology/approachThe conversion relationship between the pixel coordinate system and laser plane coordinate system is established on the basis of the mathematical model of three-dimensional measurement of laser vision sensor. In addition, the conversion relationship between the arc welding robot coordinate system and the laser vision sensor measurement coordinate system is also established on the basis of the hand–eye calibration model. The ordinary least square (OLS) is used to calculate the rotation matrix, and the SDP is used to identify the direction vectors of the rotation matrix to ensure their orthogonality.FindingsThe feasibility identification can reduce the calibration error, and ensure the orthogonality of the calibration results. More accurate calibration results can be obtained by combining OLS + SDP.Originality/valueA set of advanced calibration methods is systematically established, which includes parameters calibration of laser vision sensor and hand–eye calibration of robots and sensors. For the hand–eye calibration, the physics feasibility problem of rotating matrix is creatively put forward, and is solved through SDP algorithm. High-precision calibration results provide a good foundation for future research on seam tracking.

Offline Programming for an Arc Welding Robot with Redundant DOF

2012 ◽  
Vol 184-185 ◽  
pp. 1623-1627 ◽  
Author(s):  
Huan Ming Chen ◽  
Zhou Ping Liu
Keyword(s):  
Arc Welding ◽  
Degrees Of Freedom ◽  
Motion Path ◽  
Welding Parameters ◽  
Programming System ◽  
Robot Controller ◽  
Offline Programming ◽  
Kinematics Simulation ◽  
Communication Module ◽  
Saddle Shape

To raise the programming efficiency of arc welding robots, the offline programming system was developed for a Motoman-UP20 robot with redundant degrees of freedom in VC++ integration environment. The system consists of kinematics analysis, motion simulation, welding trajectory plan, welding parameters plan and job file generating module. It can plan the motion path and posture of welding gun for saddle-shape seams, and display the workpiece on the interface synchronically. Job instructions can be made step by step, or generated automatically. Kinematics simulation module and communication module are integrated together, and job files can be exchanged between PC and robot controller via Ethernet to realize remote control.

RNN-based multispectral satellite image processing for remote sensing applications

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Venkata Dasu Marri ◽  
Veera Narayana Reddy P. ◽  
Chandra Mohan Reddy S.
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Deep Learning ◽  
Image Classification ◽  
Satellite Image ◽  
Multispectral Image ◽  
Content Type ◽  
Colliding Bodies Optimization ◽  
Sensing Applications ◽  
Remote Sensing Applications

Purpose Image classification is a fundamental form of digital image processing in which pixels are labeled into one of the object classes present in the image. Multispectral image classification is a challenging task due to complexities associated with the images captured by satellites. Accurate image classification is highly essential in remote sensing applications. However, existing machine learning and deep learning–based classification methods could not provide desired accuracy. The purpose of this paper is to classify the objects in the satellite image with greater accuracy. Design/methodology/approach This paper proposes a deep learning-based automated method for classifying multispectral images. The central issue of this work is that data sets collected from public databases are first divided into a number of patches and their features are extracted. The features extracted from patches are then concatenated before a classification method is used to classify the objects in the image. Findings The performance of proposed modified velocity-based colliding bodies optimization method is compared with existing methods in terms of type-1 measures such as sensitivity, specificity, accuracy, net present value, F1 Score and Matthews correlation coefficient and type 2 measures such as false discovery rate and false positive rate. The statistical results obtained from the proposed method show better performance than existing methods. Originality/value In this work, multispectral image classification accuracy is improved with an optimization algorithm called modified velocity-based colliding bodies optimization.

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