The effect of alternate supply of shielding gases in austenite stainless steel GTA welding

Welding system has now been concentrated on the development of new process to achieve cost savings, higher productivity and better quality in manufacturing industry. Discrete alternate supply of shielding gas is a new technology that alternately supplies the different kinds of shielding gases in weld zone. As the newdevelopedmethods compared to the previous generalwelding with a mixing supply of shielding gas, it cannot only increase thewelding quality, but also reduce the energy by 20% and the emission rate of fume. As a result, under thesamewelding conditions,comparedwith thewelding by supplying pure argon, argon + 67% helium mixture by conventional method and thewelding by supplying alternately pure argon and pure helium by alternate method showed the increased welding speed. Also, the alternate method showed the same welding speed with argon + 67% helium mixture without largely deteriorating of weld penetration. The alternate method with argon and helium compared with the conventional methods of pure argon and argon + 67% helium mixture produced the lowest degree of welding distortion.

Characteristics of alternate supply of shielding gases in aluminum GMA welding

Recently, unlike conventional method in supplying shielding gas, a newly method which alternately supplies different kinds of shielding gases in weld zone is developed and partly commercialized. However, literature related to the present status of the technology in the actual weld field is very scant. To give better understand on this technology, this study was performed. Compared with conventional gas supply method, the variations of weld porosity and weld shape in aluminum welding with alternate supply method of pure argon and pure helium were compared with conventional gas supply method with pure argon and argon + 67%helium mixture, respectively. As a result, compared with the welding by supplying pure argon and argon + 67%helium mixture by conventional method, the welding by supplying alternately pure argon and pure helium, produced lower degree of weld porosity and deeper and broader weld penetration profile.

Modelling of arc welding : the importance of including the arc plasma in the computational domain

We present results of computational simulations of tungsten-inert-gas and metal-inert-gas welding. The arc plasma and the electrodes (including the molten weld pool when necessary) are included self-consistently in the computational domain. It is shown, using three examples, that it would be impossible to accurately estimate the boundary conditions on the weld-pool surface without including the arc plasma in the computational domain. First, we show that the shielding gas composition strongly affects the properties of the arc that influence the weld pool: heat flux density, current density, shear stress and arc pressure at the weld-pool surface. Demixing is found to be important in some cases. Second, the vaporization of the weld-pool metal and the diffusion of the metal vapour into the arc plasma are found to decrease the heat flux density and current density to the weld pool. Finally, we show that the shape of the wire electrode in metal-inert-gas welding has a strong influence on flow velocities in the arc and the pressure and shear stress at the weld-pool surface. In each case, we present evidence that the geometry and depth of the weld pool depend strongly on the properties of the arc.

Understanding welding practices on shipyards : an ethnographic study for designing an interactive robot welder

The aim of this ethnographic study was to understand welding practices in shipyard environments with the purpose of designing an interactive welding robot that can help workers with their daily job. The robot is meant to be deployed for automatic welding on jack-up rig structures. The design of the robot turns out to be a challenging task due to several problematic working conditions on the shipyard, such as dust, irregular floor, high temperature, wind variations, elevated working platforms, narrow spaces, and circular welding paths requiring a robotic arm with more than 6 degrees of freedom. Additionally, the environment is very noisy and the workers – mostly foreigners – have a very basic level of English. These two issues need to be taken into account when designing the interactive user interface for the robot. Ideally, the communication flow between the two parties involved should be as frictionless as possible. The paper presents the results of our field observations and welders’ interviews, as well as our robot design recommendation for the next project stage.

Prediction of GMA welding characteristic parameter by artificial neural network system

Nowadays, demand for automated Gas metal arc welding (GMAW) is growing and consequently need for intelligent systems is increased to ensure the accuracy of the procedure. To date, welding pool geometry has been the most used factor in quality assessment of intelligent welding systems. But, it has recently been found that Mahalanobis Distance (MD) not only can be used for this purpose but also is more efficient. In the present paper, Artificial Neural Networks (ANN) has been used for prediction of MD parameter. However, advantages and disadvantages of other methods have been discussed. The Levenberg–Marquardt algorithm was found to be the most effective algorithm for GMAW process. It is known that the number of neurons plays an important role in optimal network design. In this work, using trial and error method, it has been found that 30 is the optimal number of neurons. The model has been investigated with different number of layers in Multilayer Perceptron (MLP) architecture and has been shown that for the aim of this work the optimal result is obtained when using MLP with one layer. Robustness of the system has been evaluated by adding noise into the input data and studying the effect of the noise in prediction capability of the network. The experiments for this study were conducted in an automated GMAW setup that was integrated with data acquisition system and prepared in a laboratory for welding of steel plate with 12 mm in thickness. The accuracy of the network was evaluated by Root Mean Squared (RMS) error between the measured and the estimated values. The low error value (about 0.008) reflects the good accuracy of the model. Also the comparison of the predicted results by ANN and the test data set showed very good agreement that reveals the predictive power of the model. Therefore, the ANN model offered in here for GMA welding process can be used effectively for prediction goals.