scholarly journals All-position welding control system based on machine vision and nonlinear regression

2021 ◽  
Vol 13 (10) ◽  
pp. 168781402110527
Author(s):  
Yu Tang ◽  
Zhongren Wang ◽  
Liying Jin ◽  
Xilin Ke ◽  
Haisheng Liu
Keyword(s):  
Control System ◽  
Machine Vision ◽  
Process Parameters ◽  
Nonlinear Regression ◽  
Clustering Algorithm ◽  
Control Method ◽  
Welding Process ◽  
Practical Significance ◽  
Automatic Welding ◽  
Welding Quality

Aiming at the problems of poor welding quality and low degree of automatic welding on the engineering site, a welding process parameter control method based on machine vision and nonlinear regression technology is proposed. Firstly, a vision unit and a peripheral sensor unit are designed to obtain the information of each influencing factor of the welding process parameters. Secondly, a clustering algorithm is used to improve the extraction accuracy of feature point coordinates of weld images. Thirdly, a nonlinear regression fitting method is proposed to determine the mathematical relationship between welding quality at different welding positions and corresponding process parameters. Experimental results show that the control system is easy to operate, and the flexible control of welding process parameters in the whole process is realized. The weld cumulative height and width deviations are less than 0.5 and 0.3 mm, respectively. The welding surface is stable and meets welding requirements. Therefore, this method is of great practical significance in engineering field welding.

Quality Measure and Control System for the Whole Process of Arc Welding

2008 ◽  
Vol 575-578 ◽  
pp. 722-727
Author(s):  
Zhi Yong Li ◽  
Bao Wang ◽  
Jing Bin Ding
Keyword(s):  
Quality Control ◽  
Control System ◽  
Welding Process ◽  
Quality Measure ◽  
Welding Quality ◽  
Welding Material ◽  
Whole Process ◽  
Metallurgical Structure ◽  
Measure And Control System ◽  
And Control

Welding quality control is critical for welding manufacturing. However, the factors that affect welding quality exist in the whole welding process. Whole process welding quality control is a technology control process that can control the welding quality from choice of the welding material, stability of the welding process and quality assurance after welding. In this paper, a quality measure and control system is developed. The system is consisted of three modules: Technology property evaluation and welding material choice module is based on Analysator Hannover. This module can evaluate three type of welding material: electrode, solid welding wire and flux-cored wire. The welding process stability evaluation module can collect electrical and light spectral signal for disturbance factors diagnosis during the welding. The metallurgical structure and property forecasting module call the CCT diagram data base for predicting he metallurgical structure and mechanical property of the weld affected zone and weld metal. For stainless steel, a predicting method based on Schaeffer is also provided in this module.

Neural-Fuzzy Variable Gap Control Method for GMAW Pipe-Line Welding with CCD Camera

2011 ◽  
Vol 130-134 ◽  
pp. 2358-2363 ◽  
Author(s):  
Hai Lin Hu ◽  
Jing Li ◽  
Fang Li ◽  
Wei Zhu ◽  
He Qiang Pang
Keyword(s):  
Fuzzy Inference ◽  
Control Method ◽  
Gas Metal Arc Welding ◽  
Fuzzy Variable ◽  
Welding Process ◽  
Ccd Camera ◽  
Network Control ◽  
Welding Quality ◽  
Metal Arc Welding ◽  
Pipe Line

He sensing of the weld pool and controlling of torch at the center of the groove are important problems in back welding of GMAW (Gas Metal Arc Welding) for pipeline, furthermore, the gap of the groove perhaps is varied, which needs an intelligent control strategy to obtain the high welding quality. Fuzzy neural network control method based on BP algorithm is proposed in this paper, from the module of image processing, the corresponding gap location and width can be obtained. Then determine corresponding swing width and speed when weld gap is varied by the network fuzzy inference and calculating Euclidean distance for GMAW variable gap backing welding process. Experiment results show that the designed control method can improve the welding quality compared with traditional fixed swing and the traditional auto swing.

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.

Girth Automatic Welding System

2014 ◽  
Vol 556-562 ◽  
pp. 2528-2531
Author(s):  
Xiao Long Xu ◽  
Ke Li Xing ◽  
Jia Yu Dai
Keyword(s):  
Control System ◽  
Automatic Control ◽  
Automatic Control System ◽  
Welding Process ◽  
Touch Screen ◽  
Automatic Welding ◽  
Stepping Motor ◽  
Welding Torch ◽  
Welding System

Designed a positioning, welding and reset automatic control system based on stepping motor, PLC, touch screen. Welding torch is positioned through the photoelectric switch. PLC controls stepping motor running and welding torch starting arc and ending arc. Communication between the touch screen and PLC improves the efficiency of the system and achieves the girth automation welding process.

Automatic Welding Track Detection for Welding Process by Machine Vision

2012 ◽  
Vol 462 ◽  
pp. 886-890
Author(s):  
G. Kiswanto ◽  
A. Rianto
Keyword(s):  
Image Processing ◽  
Machine Vision ◽  
Direct Contact ◽  
Welding Process ◽  
Line Detection ◽  
Automatic Welding ◽  
Median Filtering ◽  
Hough Transforms ◽  
Image Processing Algorithms ◽  
Processing Algorithms

The use of machine vision technology in the welding process has been developed along with the need for more consistent and more qualified welding results. The application of machine vision to perform object analysis without direct contact to the object itself making the whole processes fast while maintaining the accuracy. In this study, some image processing algorithms are combined which include : image enhacement (grayscaling and median filtering), edge detection, binarization, and Hough Transform for line detection. The candidate of welding tracks are produced through this standard algorithm. However, afterward, some modifications to the Hough transforms are carried to enable finding the actual welding tracks. The implemented method is capable of detecting the actual welding tracks and their appropriate welding path with accuracy not more than 0.9 mm.

Based on Nash Optimization Decoupling Control System for Special Shape Pipe Automatic Welding Machine

2011 ◽  
Vol 383-390 ◽  
pp. 2832-2837
Author(s):  
Wen Bin Ma ◽  
Kai Zhang ◽  
Jian Hua Liu ◽  
Yan Nian Rui
Keyword(s):  
Control System ◽  
Control Strategy ◽  
Control Structure ◽  
Welding Process ◽  
Decoupling Control ◽  
Automatic Welding ◽  
Special Shape ◽  
Pipe Welding ◽  
The Right ◽  
Welding Processes

A new decoupling control structure is presented for complex special shape pipe welding process, which consists of model construction by reorganization method. Using this structure, an decoupling control system is established which realizes an adaptive decoupling control strategy for the welding processes with integrated complexities by using structure of decoupling control system and based on multivariable predictive function and nash optimization to making sure the right coefficient. Such system can be easily implemented on the welding peocess and simulation show this approach is effective.

The Development of Control System for Lowcost Automatic Welding Equipment of Aluminum Radiator Head

2012 ◽  
Vol 485 ◽  
pp. 12-15
Author(s):  
Ju Lian Ma ◽  
Hao Bin Zhou ◽  
Xiang Qian Xu ◽  
Xiao Wei Wang
Keyword(s):  
Control System ◽  
Low Cost ◽  
Welding Process ◽  
Welding Parameters ◽  
Automatic Welding ◽  
Control Board ◽  
Manual Operation ◽  
Welding Equipment ◽  
Production Site

Considering the characteristic of welding process for all-aluminum radiator and meeting the needs of Chinese production site, low-cost automatic welding equipment for aluminum radiator head has been designed, and this paper focuses on its electrical controls. The microkernel of main control board is C8051F020. Based on this board the subsystems for the entire piece of this equipment has been established, which include LCD，automatic welding, manual operation , welding parameters setting ,etc. Through trial runs of the machine, it indicates that the design of the electrical controls for the equipment is rational, full-featured, and works reliably and efficiently. And the quality of welding is well.

scholarly journals Comparison between PCA analysis and Grey based Taguchi analysis of TIG welding process parameters on Duplex stainless steel

2021 ◽  
pp. 67-74
Author(s):  
Sandip Mondal ◽  
Goutam Nandi ◽  
Pradip Kumar Pal
Keyword(s):  
Stainless Steel ◽  
Duplex Stainless Steel ◽  
Process Parameters ◽  
Gas Flow ◽  
Welding Process ◽  
Principal Component ◽  
Welding Current ◽  
Tig Welding ◽  
Welding Parameters ◽  
Welding Quality

Tungsten inert gas (TIG) welding on Duplex stainless steel (DSS) is more easy, comfortable and useful, if the process is precisely understood and controlled through development of the science & technology. TIG welding on DSS has been performed with the help of specific controlled welding process parameters. Welding quality has been strongly depended on these process parameters. In this study, some valuable welding parameters are chosen. These are welding current, shielding gas flow rate and speed of welding. These process parameters of TIG welding for ASTM/UNS 2205 DSS welds are optimized by using Principal Component Analysis (PCA) method and Grey based Taguchi’s L9 Orthogonal array (OA) experimental plan with the conception of signal to noise ratio (N/S). After that, compression results of above mentioned two analyses of TIG welding process parameters have been calculated. The quality of the TIG welding on DSS has been evaluated in term of ultimate tensile strength, yield strength and percentage of elongation. Compression results of both analyses indicate application feasibility for continuous improvement of welding quality on DSS in different components of chemical, oil and gas industries.

A Study on Welding Quality of Robotic Arc Welding Process Using Mahalanobis Distance Method

2013 ◽  
Vol 773-774 ◽  
pp. 759-765 ◽  
Author(s):  
Reenal Ritesh Chand ◽  
Ill Soo Kim ◽  
Ji Hye Lee ◽  
Jong Pyo Lee ◽  
Ji Yeon Shim ◽  
...  
Keyword(s):  
Process Parameters ◽  
Mahalanobis Distance ◽  
Arc Welding ◽  
Gma Welding ◽  
Welding Process ◽  
Experimental Results ◽  
Welding Parameters ◽  
Bead Geometry ◽  
Welding Quality ◽  
Arc Welding Process

In robotic GMA (Gas Metal Arc) welding process, heat and mass inputs are coupled and transferred by the weld arc and molten base material to the weld pool. The amount and distribution of the input energy are basically controlled by the obvious and careful choices of welding process parameters in order to accomplish the optimal bead geometry and the desired mechanical properties of the quality weldment. To make effective use of automated and robotic GMA welding, it is imperative to predict online faults for bead geometry and welding quality with respect to welding parameters, applicable to all welding positions and covering a wide range of material thickness. To successfully accomplish this objective, two sets of experiment were performed with different welding parameters; the welded samples from SM 490A steel flats adopting the bead-on-plate technique were employed in the experiment. The experimental results of current and voltage waveforms were used to predict the magnitude of bead geometry and welding quality, and to establish the relationships between weld process parameters and online welding faults. MD (Mahalanobis Distance) technique is employed for investigating and modeling of GMA welding process and significance test techniques were applied for the interpretation of the experimental data. Statistical models developed from experimental results which can be used to control the welding process parameters in order to achieve the desired bead geometry based on weld quality criteria.

Sign in / Sign up

Export Citation Format

Share Document