985 resultados para Gas tungsten arc welding.


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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Pós-graduação em Engenharia Mecânica - FEG

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Pós-graduação em Engenharia Mecânica - FEIS

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"July 1997."

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A study has been made of the effects of welding and material variables on the occurrence of porosity in tungsten inert gas arc welding of copper. The experiments were based on a statistical design and variables included, welding current, welding speed, arc atmosphere composition, inert gas flow rate, weld preparation, and base material. The extent of weld metal porosity was assessed by density measurement and its morphology by X-ray radiography and metallography. In conjunction with this the copper-steam reaction has been investigated under conditions of controlled atmosphere arc melting. The welding experiments have shown that the extent of steam porosity is increased by increased water vapour content of the arc atmosphere, increased oxygen content of the base material and decreased welding speed. The arc melting experiments have shown that the steam reaction occurs in the body of the weld pool and proceeds to an apparent equi1ibrium state appropriate to to its temperature, the hydrogen and oxygen being supplied by the dissociation of water vapour in the arc atmosphere. It has been shown conclusively that nitrogen porosity can occur in the tungsten inert gas arc welding of copper and that this porosity can be eliminated by using filler wires containing small amounts of aluminum and titanium. Since it has been shown to be much more difficult to produce sound butt welds than melt runs it has been concluded that the porosity associated with joint fit up is due to nitrogen entrained into tho arc atmosphere. Clearly atmospheric entrainment would also, to a much lesser extent, involve water vapour. From a practical welding point of view it has thus been postulated that use of a filler wire containing small amounts of aluminum and/or titanium would eliminate both forms of porosity since these elements are both strongJy deoxidising and denitriding.

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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.

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Bibliography: p. 82.

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During the past few years the attention of architects, engineers and others encased in or connected with the building industry has been attracted to the possibilities of the application of welding processes to the joining of structural members. As oxy-acetylene welding was developed before electric arc welding became perfected it was only natural that the gas torch should be first considered. It developed however on examination of the two processes that while acetylene welding gave better results in most cases it was only in the hands of experts that it could consistently outscore the arc as a welding medium. Arc-welding has the advantage over the acetylene process, where each individual operator must use his own judgement as to the proper flame, in that a squad of arc-welders can work under the direction of a single expert supervisor who accepts the responsibility of fixing the current value and of determining the proper size of welding rod to be used on any given type of work.

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The present study aims to characterize ultrafine particles emitted during gas metal arc welding of mild steel and stainless steel, using different shielding gas mixtures, and to evaluate the effect of metal transfer modes, controlled by both processing parameters and shielding gas composition, on the quantity and morphology of the ultrafine particles. It was found that the amount of emitted ultrafine particles (measured by particle number and alveolar deposited surface area) are clearly dependent from the main welding parameters, namely the current intensity and the heat input of the Welding process. The emission of airborne ultrafine particles increases with the current intensity as fume formation rate does. When comparing the shielding gas mixtures, higher emissions were observed for more oxidizing mixtures, that is, with higher CO2 content, which means that these mixtures originate higher concentrations of ultrafine particles (as measured by number of particles. by cubic centimeter of air) and higher values of alveolar deposited surface area of particles, thus resulting in a more hazardous condition regarding welders exposure.

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Purpose: Experimental measurements have been made to investigate meaning of the change in voltage for the pulse gas metal arc welding (GMAW-P) process operating under different drop transfer modes. Design/methodology/approach: Welding experiments with different values of pulsing parameter and simultaneous recording of high speed camera pictures and welding signals (such as current and voltage) were used to identify different drop transfer modes in GMAW-P. The investigation is based on the synchronization of welding signals and high speed camera to study the behaviour of voltage signal under different drop transfer modes. Findings: The results reveal that the welding arc is significantly affected by the molten droplet detachment. In fact, results indicate that sudden increase and drop in voltage just before and after the drop detachment can be used to characterize the voltage behaviour of different drop transfer mode in GMAW-P. Research limitations/implications: The results show that voltage signal carry rich information about different drop transfer occurring in GMAW-P. Hence it’s possible to detect different drop transfer modes. Future work should concentrate on development of filters for detection of different drop transfer modes. Originality/value: Determination of drop transfer mode with GMAW-P is crucial for the appropriate selection of pulse welding parameters. As change in drop transfer mode results in poor weld quality in GMAW-P, so in order to estimate the working parameters and ensure stable GMAW-P understanding the voltage behaviour of different drop transfer modes in GMAW-P will be useful. However, in case of GMAW-P hardly any attempt is made to analyse the behaviour of voltage signal for different drop transfer modes. This paper analyses the voltage signal behaviour of different drop transfer modes for GMAW-P.

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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.

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