scholarly journals Stable 3D seam tracking for thick plate GMAW manufacturing with T-joints using Kalman filter and machine learning

2021 ◽  
Author(s):  
Yinshui He ◽  
Zhuohua Yu ◽  
Ziyi Xiao ◽  
Jian Le
Keyword(s):  
Machine Learning ◽  
Kalman Filter ◽  
Clustering Algorithm ◽  
Weld Seam ◽  
Welding Process ◽  
Fault Detection And Diagnosis ◽  
Seam Tracking ◽  
Reference Image ◽  
Tracking Accuracy ◽  
The Impact

Abstract In this paper, a robust stable three-dimensional (3D) seam tracking method is investigate based on the Kalman filter (KF) and machine learning during the multipass gas metal arc welding process with a T-joint of 60 mm thickness. The laser vision sensor is used to profile the weld seam, and with the reference image captured before arcing a scheme is proposed to extract the variable weld seam profiles (WSPs) using scale-invariant feature transform and the clustering algorithm. An effective slope mutation detection method is presented to identify the feature points of the extracted WSP, namely the candidate welding positions. In order to lower the impact of fake welding positions on seam tracking, a Bayesian Network model is first built to implement fault detection and diagnosis for the visual feature measurement process using the involved process parameters and the trigger rule. A KF, as an estimator, is then established to further stabilize the tracking process combing with a self determination algorithm of the measurement result. With the visual calibration technology, 3D seam tracking is realized. Seam tracking results show that the proposed method overcomes the tremor of the tracking position and multiple fake candidate welding positions on tracking accuracy, and the tracking accuracy is 0.6 mm. This method provides potential industrial application value for industrial manufacturing with large-scale components.

Laser vision seam tracking system based on proximal policy optimization

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yanbiao Zou ◽  
Hengchang Zhou
Keyword(s):  
Weld Seam ◽  
Tracking System ◽  
Tracking Error ◽  
Welding Process ◽  
Seam Tracking ◽  
Reference Image ◽  
Image Block ◽  
Content Type ◽  
Weld Seam Tracking ◽  
Policy Optimization

Purpose This paper aims to propose a weld seam tracking method based on proximal policy optimization (PPO). Design/methodology/approach By constructing a neural network based on PPO and using the reference image block and the image block to be detected as the dual-channel input of the network, the method predicts the translation relation between the two images and corrects the location of feature points in the weld image. The localization accuracy estimation network (LAE-Net) is built to update the reference image block during the welding process, which is helpful to reduce the tracking error. Findings Off-line simulation results show that the proposed algorithm has strong robustness and performs well on the test set of curved seam images with strong noise. In the welding experiment, the movement of welding torch is stable, the molten material is uniform and smooth and the welding error is small, which can meet the requirements of industrial production. Originality/value The idea of image registration is applied to weld seam tracking, and the weld seam tracking network is built on the basis of PPO. In order to further improve the tracking accuracy, the LAE-Net is constructed and the reference images can be updated.

scholarly journals Second order Kalman filtering channel estimation and machine learning methods for spectrum sensing in cognitive radio networks

2021 ◽  
Author(s):  
Olusegun Peter Awe ◽  
Daniel Adebowale Babatunde ◽  
Sangarapillai Lambotharan ◽  
Basil AsSadhan
Keyword(s):  
Machine Learning ◽  
Kalman Filter ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Clustering Algorithm ◽  
Polynomial Regression ◽  
Primary User ◽  
Radio Networks ◽  
Second Order

AbstractWe address the problem of spectrum sensing in decentralized cognitive radio networks using a parametric machine learning method. In particular, to mitigate sensing performance degradation due to the mobility of the secondary users (SUs) in the presence of scatterers, we propose and investigate a classifier that uses a pilot based second order Kalman filter tracker for estimating the slowly varying channel gain between the primary user (PU) transmitter and the mobile SUs. Using the energy measurements at SU terminals as feature vectors, the algorithm is initialized by a K-means clustering algorithm with two centroids corresponding to the active and inactive status of PU transmitter. Under mobility, the centroid corresponding to the active PU status is adapted according to the estimates of the channels given by the Kalman filter and an adaptive K-means clustering technique is used to make classification decisions on the PU activity. Furthermore, to address the possibility that the SU receiver might experience location dependent co-channel interference, we have proposed a quadratic polynomial regression algorithm for estimating the noise plus interference power in the presence of mobility which can be used for adapting the centroid corresponding to inactive PU status. Simulation results demonstrate the efficacy of the proposed algorithm.

scholarly journals Research on the Deviation Sensing of V-groove Weld Seam based on a Novel Two Channel Acoustic Sensor

2021 ◽  
Author(s):  
Yanfeng Gao ◽  
Jianhua Xiao ◽  
Genliang Xiong ◽  
Hua Zhang
Keyword(s):  
Weld Seam ◽  
Welding Process ◽  
Simulation Method ◽  
Seam Tracking ◽  
Acoustic Sensor ◽  
Robotic Welding ◽  
Weld Seam Tracking ◽  
Novel Method ◽  
Linear Property ◽  
Sensing Performance

Abstract It is essential to sense the deviation of weld seam real-timely in robotic welding process. However, welding process always accompanied with high temperature, strong arc light and background noises, which significantly affects the application of sensors. In this study, a novel acoustic sensor was developed. This sensor consists of two microphones. Based on the sound signals collected by these two microphones, the deviation of weld seam was detected. The frequency response of the developed acoustic sensor was studied through simulation method firstly, and then the sensing performance of it was analyzed with experiments. The experimental results show that the developed acoustic sensor has a linear property for the deviation detection of V-groove weld seam. This research provides a novel method for weld seam tracking.

scholarly journals Convolutional Neural Network Using Kalman Filter for Human Detection and Tracking on RGB-D Video

2018 ◽  
Vol 12 (2) ◽  
pp. 105 ◽  
Author(s):  
Jovin Angelico ◽  
Ken Ratri Retno Wardani
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Convolutional Neural Network ◽  
Human Detection ◽  
Detection Accuracy ◽  
Tracking Accuracy ◽  
Lighting Condition ◽  
Detection And Tracking ◽  
Computation Efficiency ◽  
The Impact

The computer ability to detect human being by computer vision is still being improved both in accuracy or computation time. In low-lighting condition, the detection accuracy is usually low. This research uses additional information, besides RGB channels, namely a depth map that shows objects’ distance relative to the camera. This research integrates Cascade Classifier (CC) to localize the potential object, the Convolutional Neural Network (CNN) technique to identify the human and nonhuman image, and the Kalman filter technique to track human movement. For training and testing purposes, there are two kinds of RGB-D datasets used with different points of view and lighting conditions. Both datasets have been selected to remove images which contain a lot of noises and occlusions so that during the training process it will be more directed. Using these integrated techniques, detection and tracking accuracy reach 77.7%. The impact of using Kalman filter increases computation efficiency by 41%.

Classification of Weld Defects Based on Convolution Neural Network

2021 ◽  
pp. 23-36
Author(s):  
A.I. Gavrilov ◽  
M.Tr. Do
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Weld Seam ◽  
Control Process ◽  
Welding Process ◽  
Optimal Solution ◽  
Automatic Welding ◽  
Welding Seam ◽  
Noise Factors

Automatic welding technology has been widely applied in many industrial fields. It is a complex process with many nonlinear parameters and noise factors affecting weld quality. Therefore, it is necessary to inspect and evaluate the quality of the weld seam during welding process. However, in practice there are many types of welding seam defects, causes and the method of corrections are also different. Therefore, welding seam defects need to be classified to determine the optimal solution for the control process with the best quality. Previously, the welder used his experience to classify visually, or some studies proposed visual classification with image processing algorithms and machine learning. However, it requires a lot of time and accuracy is not high. The paper proposes a convolutional neural network structure to classify images of welding seam defects from automatic welding machines on pipes. Based on comparison with the classification results of some deep machine learning networks such as VGG16, Alexnet, Resnet-50, it shows that the classification accuracy is 99.46 %. Experimental results show that the structure of convolutional neural network is proposed to classify images of weld seam defects have availability and applicability

A study on an automatic seam tracking system by using an electromagnetic sensor for sheet metal arc welding of butt joints

2002 ◽  
Vol 216 (6) ◽  
pp. 911-920 ◽  
Author(s):  
B-H You ◽  
J-W Kim
Keyword(s):  
Sheet Metal ◽  
Arc Welding ◽  
Tracking System ◽  
Weld Line ◽  
Welding Process ◽  
Seam Tracking ◽  
Displacement Sensor ◽  
Tracking Accuracy ◽  
Metal Arc Welding ◽  
Electromagnetic Sensor

Many sensors, such as the vision sensor and the laser displacement sensor, have been developed to automate the arc welding process. However, these sensors have some problems due to the effects of arc light, fumes and spatter. An electromagnetic sensor, which utilizes the generation of an eddy current, was developed for detecting the weld line of a butt joint in which the root gap size was zero. An automatic seam tracking system designed for sheet metal arc welding was constructed with a sensor. Through experiments, it was revealed that the system had an excellent seam tracking accuracy of the order of ±0.2mm.

A study of a dual-electromagnetic sensor system for weld seam tracking of I-butt joints

2003 ◽  
Vol 217 (9) ◽  
pp. 1305-1313 ◽  
Author(s):  
J-W Kim ◽  
J-H Shin
Keyword(s):  
Sheet Metal ◽  
Arc Welding ◽  
Weld Seam ◽  
Welding Process ◽  
Seam Tracking ◽  
Sensor System ◽  
Displacement Sensor ◽  
Butt Joints ◽  
Current Variation ◽  
Electromagnetic Sensor

Seam tracking systems for the arc welding process use various kinds of sensor including the arc sensor, vision sensor and laser displacement sensor. Among the various sensors available, the electromagnetic sensor is one of the most useful methods, especially in sheet metal butt-joint arc welding, primarily because it is hardly affected by the intense arc light and fumes generated during the welding process, or by the surface conditions of the weldment such as paint marks and scratches. In this study, a dual-electromagnetic sensor, which utilizes the induced current variation in the sensing coil due to the eddy current variation in the metal near the sensor, was developed for the arc welding of sheet metal I-butt joints. The dual-electromagnetic sensor thus detects the offset displacement of the weld line from the centre of the sensor head, even when there is no gap in the joint. A set of design variables for the sensor was examined to determine the maximum sensing capability through repeated experiments. Seam tracking was performed by correcting the position of the sensor to the amount of offset displacement determined during each sampling period. From the experimental results, the developed sensor system showed an excellent capability for weld seam detection and tracking when the sensor-to-workpiece distance was less than 5mm.

Underwater Wet Weld Seam Tracking Based on Passive Visual Sensor

2013 ◽  
Vol 683 ◽  
pp. 725-728
Author(s):  
Bo Chen ◽  
Chuan Bao Jia ◽  
Ji Cai Feng
Keyword(s):  
Weld Seam ◽  
Tracking System ◽  
Water Environment ◽  
Welding Process ◽  
Seam Tracking ◽  
Visual Sensor ◽  
Weld Seam Tracking ◽  
Influence Of Water ◽  
Image Processing Algorithms ◽  
Weld Automation

Weld seam tracking system is urgently needed in weld automation process, but it has not been well studied in underwater weld applications. This paper used visual sensor to automatically monitor the weld seam in underwater wet weld process, and image processing algorithms were developed to remove the influence of water environment on the captured image and automatically obtain the weld torch deviation, and the weld torch was adjusted automatically according to the deviation obtained by the image, experiment results showed that the system could meet the requirements of underwater wet welding process.

A Survey of Underwater Wet Weld Seam Tracking Based on Ultrasonic Sensor

2013 ◽  
Vol 433-435 ◽  
pp. 2227-2230
Author(s):  
Bo Chen ◽  
Ji Cai Feng
Keyword(s):  
Weld Seam ◽  
Welding Process ◽  
Development Trend ◽  
Ultrasonic Sensor ◽  
Seam Tracking ◽  
Signal Process ◽  
Underwater Welding ◽  
Weld Seam Tracking ◽  
Automation Control ◽  
Tracking Technology

With the exploration of marine sources becoming more and more important, underwater welding is widely needed. Because of the special working condition, underwater weld seam tracking technology is urgently needed, for the automation control of the underwater welding process is the inevitable development trend because of the rigorous environment. This paper used ultrasonic sensor to monitor the weld seam position in underwater wet welding process, and signal process algorithm was developed to obtain the weld seam information, experiment results showed that this method could detect the weld seam shape correctly, this load the foundation for further automatically controlling the welding process.

A Study on Modified Hough Algorithm for Image Processing in Weld Seam Tracking System

2015 ◽  
Vol 1088 ◽  
pp. 824-828 ◽  
Author(s):  
Jong Pyo Lee ◽  
Qian Qian Wu ◽  
Min Ho Park ◽  
Cheol Kyun Park ◽  
Ill Soo Kim
Keyword(s):  
Image Processing ◽  
Weld Seam ◽  
Gma Welding ◽  
Vision System ◽  
Tracking System ◽  
Computing Time ◽  
Weld Line ◽  
Welding Process ◽  
Seam Tracking ◽  
Weld Seam Tracking

In modern market, achieving mechanical and automatic arc welding process is the key issue to be solved in welding industries. Because of the high complexity of the welding environment, manual detection of the weld line information is hard to be successful and time consuming. Therefore, this study aim at developing a new image processing algorithm for seam tracking system in Gas Metal Arc (GMA) welding by modified Hough algorithm based on the laser vision system. Firstly, noises in the captured weld seam images by CCD camera were effectively removed by noise filtering algorithm and then weld joint position were detected by the modified Hough algorithm to realize the automatic weld seam tracking. To verify the efficiency of the developed image processing model, a common image processing method was employed and the processed results were compared with the proposed algorithm. Statistical results proved that the modified Hough algorithm was able to acquire the weld information precisely with less computing time and memory cost, which also capable for industrial application.

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