Intelligentized Technologies for Welding Manufacturing

2013 ◽  
Vol 773-774 ◽  
pp. 725-731 ◽  
Author(s):  
Shan Ben Chen ◽  
Zhen Ye ◽  
Gu Fang
Keyword(s):  
Intelligent Control ◽  
Arc Welding ◽  
Welding Process ◽  
Dynamical Process ◽  
Control Methods ◽  
Welding Robot ◽  
Robotic Welding ◽  
Artificial Intelligent ◽  
Intelligent Computing ◽  
Robot Systems

This paper presents some newest and potential developments on artificial intelligent technologies for welding manufacturing process in Shanghai Jiao Tong University (SJTU), which contains multi-information acquirement and fusion processing of arc welding dynamical process; Intelligent computing for welding process; Intelligent control methods for welding process and quality control; artificial intelligent technologies for welding robot systems and robotic welding process; and some application in welding engineering. The ideas of intelligentized welding manufacturing technology (IWMT) and intelligentized welding manufacturing engineering (IWME) are presented in this paper for systematization of intending researches and applications on intelligentized technologies for modern welding manufacturing.

Intelligent Control of Arc Welding Dynamics during Robotic Welding Process

2010 ◽  
Vol 638-642 ◽  
pp. 3751-3756 ◽  
Author(s):  
San Ben Chen ◽  
W.Y. Wang ◽  
H.B. Ma
Keyword(s):  
Weld Pool ◽  
Intelligent Control ◽  
Arc Welding ◽  
Visual Information ◽  
Control Strategies ◽  
Welding Process ◽  
Control Methods ◽  
Welding Robot ◽  
Robotic Welding ◽  
Pulsed Gtaw

This paper presents a sequential research works on visual information acquirement and intelligent control of arc weld pool dynamics and seam formation during pulsed GTAW (Gas Tungsten Arc Welding) in robotic welding process. The visual information acquirement methods are focused in computer vision sensing, image processing and characteristic extraction of the weld pool surface from the single-item pool images by particular algorithms for robotic welding process. Based on acquired visual characteristics of weld pool and established neural network and knowledge models for predicting dynamical characteristics of weld pool during robotic welding, corresponding control methods, such adaptive control, self-learning and other composite intelligent control strategies are developed to control welding pool dynamics during pulsed GTAW by welding robot. Some experiments and applications of intelligent control methods in welding robot systems are shown in the paper.

scholarly journals An adaptive approach to compensate seam tracking error in robotic welding process by a moving fixture

2018 ◽  
Vol 15 (6) ◽  
pp. 172988141881620
Author(s):  
Reza Ebrahimpour ◽  
Rasul Fesharakifard ◽  
Seyed Mehdi Rezaei
Keyword(s):  
Weld Pool ◽  
Arc Welding ◽  
Sliding Mode ◽  
Gas Metal Arc Welding ◽  
Tracking Error ◽  
Welding Process ◽  
Seam Tracking ◽  
Ball Screw ◽  
Robotic Welding ◽  
Metal Arc Welding

Welding is one of the most common method of connecting parts. Welding methods and processes are very diverse. Welding can be of fusion or solid state types. Arc welding, which is classified as fusion method, is the most widespread method of welding, and it involves many processes. In gas metal arc welding or metal inert gas–metal active gas, the protection of the molten weld pool is carried out by a shielding gas and the filler metal is in the form of wire which is automatically fed to the molten weld pool. As a semi-metallic arc process, the gas metal arc welding is a very good process for robotic welding. In this article, to conduct the metal active gas welding torch, an auxiliary ball screw servomechanism is proposed to move under a welder robot to track the welded seam. This servomechanism acts as a moving fixture and operates separately from the robot. At last, a decentralized control method based on adaptive sliding mode is designed and implemented on the fixture to provide the desired motion. Experimental results demonstrate an appropriate accuracy of seam tracking and error compensation by the proposed method.

Optimization of Flux Cored Arc Welding Process Parameter Using Genetic and Memetic Algorithms

2013 ◽  
Vol 13 (4) ◽  
pp. 239-250 ◽  
Author(s):  
T. Kannan ◽  
N. Murugan ◽  
B. N. Sreeharan
Keyword(s):  
Arc Welding ◽  
Welding Process ◽  
Memetic Algorithms ◽  
Optimization Techniques ◽  
Low Carbon ◽  
Robotic Welding ◽  
Bead Width ◽  
Flux Cored Arc Welding ◽  
Arc Welding Process ◽  
Welding Processes

AbstractMost of the manufacturing enterprises indulge in the bonding of metals during the production process. This makes welding one of the most important processes in industries. Subsequently, due to the high usage of welding process, industrial engineers desire to optimize the parameters concerned to achieve the desired weld bead characteristics. This paper focuses on optimization of flux cored arc welding process parameters, which are used for deposition of duplex stainless steel on low carbon structural steel plates. Experiments were conducted based on central composite rotatable design and mathematical models were developed using multiple regression method. Further, optimization with objectives as minimizing percentage dilution, maximizing height of reinforcement and bead width was carried out using genetic algorithm and memetic algorithm. This problem was formulated as a multi objective, multivariable and non-linear programming problem. The algorithms were implemented using basic functions of C language making it highly reliable, adoptable, very user friendly and extendable to other welding processes such as GMAW, GTAW, robotic welding, etc. The adopted optimization techniques were further compared based on various computational factors.

scholarly journals A Mobile Robotic Welding Robot will be Verified both Theoretically and Empirically using AWS and ASTM Standards

2020 ◽  
Vol 8 (5) ◽  
pp. 5246-5251
Keyword(s):  
Weld Joint ◽  
Vertical Section ◽  
Welding Process ◽  
Movement Control ◽  
Cutting Edge ◽  
Welding Robot ◽  
Robotic Welding ◽  
Weld Joints ◽  
Welding Procedure ◽  
Welding System

Customary automated welding, basic in ventures, for example, car creation, gets unfeasible in enterprises that utilization unstructured assembling systems, for example, shipbuilding. This is expected to some extent to the size of the made frameworks and the size and areas of the weld. In these unstructured assembling conditions, the cutting edge for automated welding has generally comprised of a fixed-track framework with a mechanical welding carriage that works along the track. In any case, elective automated welding approaches that utilize advancements from the field of versatile mechanical autonomy are being sought after. One such model is the semiautonomous Versatile Robotic Welding System (MRWS). The MRWS is a lightweight versatile controller comprising of a two-degrees-of-opportunity portable stage and a threedegrees-of-opportunity burn controller. The MRWS is equipped for climbing ferrous surfaces by the utilization of changeless magnet tracks and situating the welding light along a weld joint. This framework is intended to automate the welding procedure for an assortment of weld joints with insignificant arrangement time. Arrangement comprises of putting the MRWS superficially to be welded and heading to the expected weld joint. So as to be used in a producing condition, such a framework must be confirmed for the welding procedure it is performing. This paper exhibits and confirms the MRWS as a legitimate other option for automated welding in unstructured situations. The confirmation procedure comprises of two parts: plan approval dependent on hypothetical investigation of the MRWS framework models to demonstrate the weld procedure necessities can be met, trailed by an exact confirmation dependent on AWS weld test particulars for a particular, normally utilized welding process. The plan approval centers around the two essential contrasts between the MRWS and demonstrated fixed-track motorized welding frameworks, burn movement control on a portable stage, and effect of the MRWS attractive feet on the weld process. The observational confirmation was performed on a vertical section weld on gentle steel with tough movement, 3G-PF

A Service Reliability Assessment Method for the Rigidity-Flexibility Coupling Open Arc Welding Robot

2013 ◽  
Vol 365-366 ◽  
pp. 780-783 ◽  
Author(s):  
Xing Han ◽  
Chang Li ◽  
Xiao Gang Ma
Keyword(s):  
Arc Welding ◽  
Reliability Assessment ◽  
Welding Process ◽  
Mathematic Model ◽  
Monte Carlo Sampling ◽  
Assessment Method ◽  
Service Reliability ◽  
Virtual Model ◽  
Welding Robot ◽  
Stochastic Errors

The open arc welding robots have been used widely nowadays; however, the elastic deformations of every inner link inevitably affect the welding quality. In this paper, it proposes an effective method of service reliability assessment for the rigidity-flexibility coupling open arc welding robot. Firstly, a parametric virtual model of the robot is built in ADAMS/View-AutoFlex module. Secondly, the stochastic errors generated in the working process are simulated by a program created from pseudo-random numbers which have been written by multiplicative congruence method. Lastly, Monte Carlo sampling calculation is implemented for the virtual model, and large sample data of the dynamic characteristics in welding process are obtained and used to calculate the service reliability of the welding robot. This method includes simplicity (no need to solve complex mathematic model) and high accuracy which make it highly promising for application in mechanism design.

Combining Welding Expert Systems with Welding Databases to Improve Shipbuilding Production

1990 ◽  
Vol 6 (04) ◽  
pp. 232-240
Author(s):  
Hans H. Vanderverldt ◽  
Sterling Johnston ◽  
Jerald E. Jones ◽  
Dawn White ◽  
B. Cleveland
Keyword(s):  
Expert Systems ◽  
Vision System ◽  
Computer Software ◽  
Welding Process ◽  
Cost Savings ◽  
Welding Robot ◽  
Robotic Welding ◽  
Software Environment ◽  
Robot Welding ◽  
Welding Processes

Construction of a large ship requires many thousands of feet of welding. Whenever the welding process can be streamlined or automated, tremendous cost savings can be obtained. The WELDEXCELL system is a WELDing EXpert manufacturing CELL that provides computerized technical support information, off-line weld planning, and an integrated welding robot/welding system/vision system controller. The first of two subsystems, the Welding Job Planner (WJP) accomplishes off-line intelligent weld planning for both automated and manual welding processes. The second subsystem, the Welding Job Controller (WJC) provides a fully integrated hardware control environment with associated software for combined control of a welding robot, welding equipment, and a robotic vision system. In the WELDEXCELL system, a series of expert systems and databases have been combined in a new type of computer software environment called a blackboard. There are as many as 19 separate components of the Welding Job Planner subsystem of WELDEXCELL which fall into five interrelated functional groups. WELDEXCELL will be used by design engineers, welding engineers, mechanical engineers, and nondestructive testing (NDT) engineers for both manual welding and to interface to automated and robotic welding systems and vision systems. WELDEXCELL also includes the control system hardware and software to provide off-line intelligent adaptive control of the welding process itself. The development of WELDEXCELL is a multi-year effort involving a partnership of government, industry, university research, and technology transfer. The project has already generated new concepts with potential for future spin-off benefits. The ultimate payback in productivity will be large for the American welding, fabrication, manufacturing, and construction industries.

Rigidity-Flexibility Coupling Motion Simulation of the Open Arc Welding Robot in ADAMS

2013 ◽  
Vol 694-697 ◽  
pp. 1675-1678
Author(s):  
Chang Li ◽  
Xiao Gang Ma ◽  
Guang Bing Zhao
Keyword(s):  
Arc Welding ◽  
Welding Process ◽  
Mathematic Model ◽  
Robot Kinematics ◽  
Motion Simulation ◽  
Welding Robot ◽  
Design Quality ◽  
Elastic Deformations ◽  
Real Value ◽  
Coupling Motion

Considering the effects of mechanisms elastic deformations on the welding quality, this paper builds a rigidity-flexibility coupling parameterized virtual model of an open arc welding robot in ADAMS. Using this method, we can obtain the robot dynamic characters in welding process that approach the real value at utmost. The results show it is an effective way for improving the robot kinematics accuracy and design quality, including simplicity (no need to solve complex mathematic model) and high accuracy which make it highly promising for application.

On-Line Identification and Control of Part-Preparation and Fixturing Errors in Arc Welding

1993 ◽  
Vol 115 (4) ◽  
pp. 385-389 ◽  
Author(s):  
S. Nagarajan ◽  
H. C. Wikle ◽  
B. A. Chin
Keyword(s):  
Real Time ◽  
Thermal Imaging ◽  
Arc Welding ◽  
Imaging Techniques ◽  
Welding Process ◽  
Robotic Welding ◽  
Infrared Thermal Imaging ◽  
Joint Preparation ◽  
On Line ◽  
And Control

Sensing elements need to be incorporated in robotic welding systems to enable the robot to perceive and adapt to on-line variations occurring in the welding process. In this work, infrared thermal imaging techniques have been used to track variations produced by inadequate control during the joint preparation and fixturing stages. Variations in two joint parameters, gap and position, were studied. Changes in these parameters were found to have peculiar effects on the surface temperature distributions. The observed effects were used to develop quantitative error signals. These error signals were then used to measure the joint gaps and joint-torch offsets in real-time. The joint torch offset error signal was successfully used to control an initial error in joint position during real-time welding.

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