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

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.

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

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

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

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

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

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.

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

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.