Application of laser profilo metry in problems of welding equipment geometric adaptation

2021 ◽  
Author(s):  
Keyword(s):  
Neural Network ◽  
Welded Joint ◽  
Piecewise Linear ◽  
Welding Process ◽  
Piecewise Linear Approximation ◽  
Vision Sensor ◽  
Welding Equipment ◽  
The Neural Network ◽  
Laser Vision Sensor ◽  
Laser Vision

Adaptive adjustment of the relationship between the welding process parameters and the butt geometry permits to reduce the likelihood of welding defects appearance and improve the quality of the welded joint in automatic welding of large-diameter pipes. To obtain data on the configuration of the welded joint, the RF627 laser vision sensor is used. To reduce the influence of restrictions arising during the welding process, a median algorithm for filtering impulse noise is proposed. To calculate the geometric parameters of the welded joint, a model based on pixel data obtained from a laser sensor is proposed. The restoration of the welded butt parameters is carried out according to the algorithm of piecewise-linear approximation, which involves the determination of six characteristic points of the butt. The adaptive adjuster uses an inverse neural network model for adjustment the parameters of the welding process: welding current, voltage, wire feed speed. To train the neural network, the characteristic parameters of the welded butt are used: gap, skewing (warping of the edges) and bluntness (for the root weld), the current width of the butt groove in each layer (for other types of welds). The weights of the neural network layers are restored online using a gradient descent algorithm. The important role of the laser vision sensor in solving the problem of adaptation of welding equipment and the effectiveness of the proposed algorithms are confirmed experimentally. Keywords laser vision sensor; robotic welding; multilayer/multi-pass welding; piecewise linear approximation; adaptive control with a reverse neural network model

Improving the tracking quality of the weld seam butt with V-form grooving by using Kalman filter and neural network

2021 ◽  
Author(s):  
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Colored Noise ◽  
Weld Seam ◽  
Vision Sensor ◽  
Laser Vision Sensor ◽  
Characteristic Points ◽  
Laser Vision ◽  
Weld Seams

An algorithm for tracking of the welded seams grooving by using a Kalman filter based on six characteristic points of the profile obtained using the RF627 laser vision sensor is proposed. In order to reduce the error in weld seams control, a multilayer neural network with a backpropagation algorithm is created to compensate for errors caused by colored noise when using the Kalman filter. Experimental results show that when the algorithm is applied, the error in tracking the trajectory of weld seams is reduced. Keywords tracking of weld seams; multilayer/multi-pass welding; Kalman filter; multilayer perceptron

scholarly journals Real-Time Weld Quality Prediction Using a Laser Vision Sensor in a Lap Fillet Joint during Gas Metal Arc Welding

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1625
Author(s):  
Kidong Lee ◽  
Insung Hwang ◽  
Young-Min Kim ◽  
Huijun Lee ◽  
Munjin Kang ◽  
...  
Keyword(s):  
Real Time ◽  
Prediction Models ◽  
Gma Welding ◽  
Gas Metal Arc Welding ◽  
Welding Process ◽  
Quality Prediction ◽  
Vision Sensor ◽  
Image Processing Algorithm ◽  
Laser Vision Sensor ◽  
Laser Vision

Nondestructive test (NDT) technology is required in the gas metal arc (GMA) welding process to secure weld robustness and to monitor the welding quality in real-time. In this study, a laser vision sensor (LVS) is designed and fabricated, and an image processing algorithm is developed and implemented to extract precise laser lines on tested welds. A camera calibration method based on a gyro sensor is used to cope with the complex motion of the welding robot. Data are obtained based on GMA welding experiments at various welding conditions for the estimation of quality prediction models. Deep neural network (DNN) models are developed based on external bead shapes and welding conditions to predict the internal bead shapes and the tensile strengths of welded joints.

Development of an auto-welding system for CRD nozzle repair welds using a 3D laser vision sensor

2007 ◽  
Vol 21 (10) ◽  
pp. 1720-1725 ◽  
Author(s):  
K. Park ◽  
Y. Kim ◽  
J. Byeon ◽  
K. Sung ◽  
C. Yeom ◽  
...  
Keyword(s):  
Vision Sensor ◽  
Laser Vision Sensor ◽  
Laser Vision ◽  
Welding System

GTAW Procedure Expert System Based on Neural Network

2013 ◽  
Vol 455 ◽  
pp. 425-430 ◽  
Author(s):  
Xue Wu Wang ◽  
Shang Yong Yang
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Expert Knowledge ◽  
Welding Process ◽  
Learning Ability ◽  
Welding Parameters ◽  
Neural Network Learning ◽  
The Neural Network ◽  
Function Design ◽  
Welding Procedure

Intelligent procedure expert system was developed to select appropriate GTAW procedure in this paper. First, the function design and implementation methods of the welding procedure expert system were introduced. The expert system can present the welding procedure card, multimedia display of welding process, and output function to makes the data sharing more convenient. Then, the database design of the welding procedure expert system based on C/S mode was presented where the expert knowledge was stored. At last, the neural network model was established to realize procedure selection based on the neural network learning ability and the welding case from the database. With the BPNN model, the welding parameters can be obtained based on the input welding conditions.

A study on the welding seam tracking by using Laser Vision Sensor

ICCAS 2010 ◽  
2010 ◽  
Author(s):  
Taewook Kim ◽  
Seungbeom Lee ◽  
Seunghwan Baek ◽  
Kwangsuck Boo
Keyword(s):  
Seam Tracking ◽  
Vision Sensor ◽  
Welding Seam ◽  
Laser Vision Sensor ◽  
Laser Vision

Welding seam recognition and tracking for a novel mobile welding robot based on multi-layer sensing strategy

2021 ◽  
Author(s):  
Chao Liu ◽  
Hui Wang ◽  
Yu Huang ◽  
Youmin Rong ◽  
Jie Meng ◽  
...  
Keyword(s):  
Seam Tracking ◽  
Sensor System ◽  
Vision Sensor ◽  
Welding Robot ◽  
Welding Seam ◽  
Localization And Mapping ◽  
Laser Vision Sensor ◽  
Tracking Ability ◽  
Mobile Welding Robot ◽  
Laser Vision

Abstract Mobile welding robot with adaptive seam tracking ability can greatly improve the welding efficiency and quality, which has been extensively studied. To further improve the automation in multiple station welding, a novel intelligent mobile welding robot consists of a four-wheeled mobile platform and a collaborative manipulator is developed. Under the support of simultaneous localization and mapping (SLAM) technology, the robot is capable of automatically navigating to different stations to perform welding operation. To automatically detect the welding seam, a composite sensor system including an RGB-D camera and a laser vision sensor is creatively applied. Based on the sensor system, the multi-layer sensing strategy is performed to ensure the welding seam can be detected and tracked with high precision. By applying hybrid filter to the RGB-D camera measurement, the initial welding seam could be effectively extracted. Then a novel welding start point detection method is proposed. Meanwhile, to guarantee the tracking quality, a robust welding seam tracking algorithm based on laser vision sensor is presented to eliminate the tracking discrepancy caused by the platform parking error, through which the tracking trajectory can be corrected in real-time. The experimental results show that the robot can autonomously detect and track the welding seam effectively in different station. Also, the multiple station welding efficiency can be improved and quality can also be guaranteed.

Measurement of Weld Bead Defect for Shock Absorber Using Laser Vision Sensor

2004 ◽  
Vol 270-273 ◽  
pp. 2332-2337 ◽  
Author(s):  
H. Lee ◽  
K. Sung ◽  
H. Park ◽  
Se Hun Rhee
Keyword(s):  
Shock Absorber ◽  
Weld Bead ◽  
Vision Sensor ◽  
Laser Vision Sensor ◽  
Laser Vision
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