Modeling and Analysis of Underwater Wet Weld Process Based on Regression Method

2013 ◽  
Vol 690-693 ◽  
pp. 2621-2624
Author(s):  
Bo Chen ◽  
Ji Cai Feng
Keyword(s):  
Weld Seam ◽  
Welding Process ◽  
Regression Method ◽  
Modeling And Analysis ◽  
Underwater Wet Welding ◽  
Underwater Environment ◽  
Arc Sensor ◽  
Weld Parameters ◽  
Automation Technology ◽  
Weld Automation

Underwater weld technology is urgently needed for the widely development of marine recourses, and weld automation technology is the inevitable choice because of the underwater environment. Because of the influence of the rigorous environment, the weld seam forming of underwater wet welding is very poor. To control the weld seam forming automatically, the model between the weld parameters and the weld seam shape must be built. This paper used arc sensor to monitor the electrical information of underwater wet welding process, and regression method was used to model the process, and the factors that influence the weld seam forming mostly were analyzed.

A Study on Modeling of CO2 Weld Seam Forming Quality

2014 ◽  
Vol 488-489 ◽  
pp. 111-114
Author(s):  
Bo Chen ◽  
Ji Cai Feng
Keyword(s):  
Weld Seam ◽  
Processing Method ◽  
Weld Quality ◽  
Welding Technology ◽  
Signal Processing Method ◽  
Signal Features ◽  
Network Method ◽  
Automation Technology ◽  
Weld Automation ◽  
Pool Image

CO2welding technology is widely used nowadays, because the work environment is very bad, weld automation technology is urgently needed. To control the weld quality automatically, weld sensors should be first used to obtain information that could reflect the weld quality. This paper used arc and visual sensors to obtain the electrical and weld pool image of CO2weld process, and signal processing method was used to obtain the signal features of the information. Then neural network method was used to model the process, experiment results showed that the method could effectively predict the weld seam forming.

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.

Modeling of underwater wet welding process based on visual and arc sensor

2014 ◽  
Vol 41 (3) ◽  
pp. 311-317 ◽  
Author(s):  
Bo Chen ◽  
Jicai Feng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Weld Seam ◽  
Back Propagation ◽  
Welding Process ◽  
Welding Quality ◽  
Welding Seam ◽  
Content Type ◽  
Underwater Welding ◽  
Underwater Wet Welding

Purpose – The purpose of this paper was to use visual and arc sensors to simultaneously obtain the underwater wet welding information, and a weld seam-forming model was made to predict the weld seam's geometric parameters. It is difficult to obtain a fine welding quality in underwater welding because of the intense disturbances of the water environment. To automatically control the welding quality, the weld seam-forming model should first be established. Thus, the foundation was laid for automatically controlling the underwater welding seam-forming quality. Design/methodology/approach – Visual and arc sensors were used simultaneously to obtain the weld seam image, current and voltage information; then signal algorithms were used to process the information, and the back propagation (BP) neural network was used to model the process. Findings – Experiment results showed that the BP neural network model could precisely predict the weld seam-forming parameters of underwater wet welding. Originality/value – A weld seam-forming model of underwater wet welding process was made; this laid the foundation for establishing a controller for controlling the underwater wet welding process automatically.

Analysis of Underwater Wet Welding Seam Forming Based on Arc Sensor Information

2013 ◽  
Vol 300-301 ◽  
pp. 500-503
Author(s):  
Bo Chen ◽  
Hong Tao Zhang ◽  
Ji Cai Feng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Weld Seam ◽  
Water Environment ◽  
Welding Process ◽  
Welding Quality ◽  
Welding Seam ◽  
Underwater Welding ◽  
Arc Sensor ◽  
Sensor Information

It is difficult to obtain fine weld seam of underwater welding because of the water environment, this paper used arc sensor to obtain electrical information of the under water wet welding process, and BP neural network was used to model the process, experiment results showed the model could predict the process precisely, this laid the foundation for further controlling the welding quality automatically.

Active Visual Sensor Based Weld Seam Tracking for Underwater Wet Welding

2013 ◽  
Vol 717 ◽  
pp. 588-591
Author(s):  
Bo Chen ◽  
Chuan Bao Jia ◽  
Ji Cai Feng
Keyword(s):  
Weld Seam ◽  
Development Trend ◽  
Seam Tracking ◽  
Visual Sensor ◽  
Underwater Welding ◽  
Underwater Wet Welding ◽  
Weld Seam Tracking ◽  
Image Processing Algorithms ◽  
The Development Trend ◽  
Weld Automation

Weld automation is the development trend of underwater welding, and underwater weld seam tracking is one of the key technologies in weld automation. This paper used active visual sensor to automatically monitor the weld seam in underwater wet weld process, and image processing algorithms were developed to automatically obtain the weld torch deviation, then the weld torch was adjusted automatically according to the deviation obtained by the image, experiment results showed that this method could be used in underwater wet welding.

scholarly journals Process Stability, Microstructure and Mechanical Properties of Underwater Submerged-Arc Welded Steel

Metals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1249
Author(s):  
Maofu Zhang ◽  
Yanfei Han ◽  
Chuanbao Jia ◽  
Shengfa Dong ◽  
Sergii Maksimov ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Epoxy Resin ◽  
Water Glass ◽  
Welding Process ◽  
Process Stability ◽  
Surrounding Water ◽  
Welded Steel ◽  
Underwater Wet Welding ◽  
Marine Engineering ◽  
High Level

In underwater wet welding, the unstable welding process caused by the generation and rupture of bubbles and the chilling effect of water on the welding area result in low quality of welded joints, which makes it difficult to meet the practical application of marine engineering. To improve the process stability and joining quality, a mixture of welding flux with a water glass or epoxy resin was placed on the welding zone before underwater welding. In this paper, welds’ appearance, geometry statistics of welds’ formation, welding process stability, slag structure, microstructure, pores and mechanical properties were investigated. It was found that with the addition of water glass in the mixture, the penetration of weld was effectively increased, and the frequency of arc extinction was reduced. Though the porosity rose to a relatively high level, the joints’ comprehensive mechanical properties were not significantly improved. Notably, the applied epoxy resin completely isolated the surrounding water from the welding area, which greatly improved process stability. Furthermore, it benefited from the microstructure filled with massive acicular ferrite, the average elongation and room temperature impact toughness increased by 178.4%, and 69.1% compared with underwater wet welding, respectively, and the bending angle of the joint reaches to 180°.

Correlation between wire feed speed and external mechanical constraint for enhanced process stability in underwater wet flux-cored arc welding

2018 ◽  
Vol 233 (10) ◽  
pp. 2061-2073 ◽  
Author(s):  
Jianfeng Wang ◽  
Qingjie Sun ◽  
Jiangkun Ma ◽  
Peng Jin ◽  
Tianzhu Sun ◽  
...  
Keyword(s):  
Welding Process ◽  
Electrical Signal ◽  
Feed Speed ◽  
Process Stability ◽  
Underwater Wet Welding ◽  
Extinction Process ◽  
Flux Cored Arc Welding ◽  
Wire Feed Speed ◽  
Wire Feed ◽  
Mechanical Constraint

It is a great challenge to improve the process stability in conventional underwater wet welding due to the formation of unstable bubble. In this study, mechanical constraint method was employed to interfere the bubble generated by underwater wet welding, and the new method was named as mechanical constraint assisted underwater wet welding. The aim of the study was to quantify the combined effect of wire feed speed and condition of mechanical constraint on the process stability in mechanical constraint assisted underwater wet welding. Experimental results demonstrated that the introduction of mechanical constraint not only suppressed the bubble without floating but also stabilized the arc burning process. The degree of influence of mechanical constraint, which changed with wire feed speed, played an important role during the mechanical constraint assisted underwater wet welding process. For all wire feed speeds, the fluctuations of welding electrical signal were decreased through introduction of mechanical constraint. The difference in the proportion of arc extinction process between underwater wet welding and mechanical constraint assisted underwater wet welding became less with increasing wire feed speed. At wire feed speed lower than 7.5 m/min, the improvement of process stability was very significant by mechanical constraint. However, the further improvement produced limited effect when the wire feed speed was greater than 7.5 m/min. The observation results showed that a better weld appearance was afforded at a large wire feed speed, corresponding to a lower variation coefficient.

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.

Recent Development of HFW Linepipe With a High-Quality Weld Seam Suitable for Sour Service Environments

2014 ◽  
Author(s):  
Shunsuke Toyoda ◽  
Sota Goto ◽  
Yasushi Kato ◽  
Satoru Yabumoto ◽  
Akio Sato
Keyword(s):  
Crack Propagation ◽  
Chemical Reactions ◽  
Weld Seam ◽  
Welding Process ◽  
Crack Propagation Rate ◽  
Propagation Rate ◽  
Oxide Content ◽  
Electric Resistance ◽  
Diffusible Hydrogen ◽  
High Quality

Based on the appreciable progress being made in quality control and assurance technology for the electric resistance welding process, the number of applications for high-frequency electric resistance welded (HFW) linepipe in highly demanding, severe environments, such as offshore and sour environments, has gradually increased. Resistance to hydrogen-induced cracking (HIC) is the most important property for a linepipe to possess for use in sour environments. However, resistance to HIC, especially along the longitudinal weld seam, has not yet been fully related to metallurgical factors. In this study, to clarify the effects of inclusions on the sour resistance properties of X60- to X70-grade steels, their resistances to HIC were numerically simulated. For the simulation, the steels were assumed to have a yield strength of 562 MPa and a tensile strength of 644 MPa. To estimate the effect of nonmetallic inclusions, a virtual inclusion was situated at the center of a 10-mm-thick HIC test specimen. Tests were performed using NACE test solution A. The crack propagation rate was calculated as a function of the content of diffusible hydrogen, the diameter of the inclusion, and the fracture toughness of the matrix after hydrogen absorption. In the propagation calculation, the resistance to chemical reactions at the interface of the inclusion matrix was also considered to be a delaying factor. By assuming a resistance to chemical reactions at the interface, the crack propagation rate could be fitted to the actual HIC propagation rate. Based on the numerical simulation results, HFW linepipe with a high-quality weld seam was developed. Controlling the morphologies and distributions of oxides generated during the welding process is the key factor for improving the resistance to HIC. Using a combination of optimized chemical composition, microstructure and oxide content, the weld seam of the developed X70-grade HFW steel pipe showed excellent resistance to HIC.

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

Sign in / Sign up

Export Citation Format

Share Document