A k-epsilon model for turbulent weld pool convection in gas metal arc welding process

In this paper, we present a modified k - epsilon model capable of addressing turbulent weld-pool convection in a GMAW process, taking into account the morphology of the phase change interface during a Gas Metal Arc Welding (GMAW) process. A three-dimensional turbulence mathematical model has been developed to study the heat transfer and fluid flow within the weld pool by considering the combined effect of three driving forces, viz., buoyancy, Lorentz force and surface tension (Marangoni convection). Mass and energy transports by the droplets are considered through the thermal analysis of the electrode. The falling droplet's heat addition to the molten pool is considered to be a volumetric heat source distributed in an imaginary cylindrical cavity ("cavity model") within the weld pool. This nature of heat source distribution takes into account the momentum and the thermal, energy of the falling droplets. The numerically predicted weld pool dimensions both from turbulence and laminar models are then compared with the experimental post-weld results sectioned across the weld axis. The above comparison enables us to analyze the overall effects of turbulent convection on the nature of heat and fluid flow and hence on the weld pool shape/size during the arc welding processes.

Determination of the causes of weld-metal cracking in high-strength steels and the development of heat-treatable low-alloy-steel filler wires for use with the inert-gas-shielded arc-welding process

"Materials Central, Contract no. AF 33(616)-5878, Project no. 7351."

Arc voltage behavior in GMAW-P under different drop transfer modes

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.

The effect of metal transfer modes and shielding gas composition on the emission of ultraafine particles in MAG steel welding

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.

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.

Análise microestrutural de revestimentos usados no reparo de turbinas hidráulicas danificadas pela cavitação

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estudo da técnica de enchimento de chanfro do aço naval pelo processo GMAW-CW

Nesse trabalho estudou-se a viabilidade operacional e as características econômicas e geométrica da técnica do processo de soldagem GMAW-CW (alimentação adicional de um arame frio) em comparação Gas Metal Arc Welding (GMAW). O sistema de alimentação de arame frio foi projetado e adaptado a pistola de soldagem MIG/MAG. Utilizou-se uma fonte eletrônica de múltiplos processos ajustada em tensão constante e CC+, a proteção gasosa foi uma mistura 75%Ar+25%CO² e CO² comercialmente puro. O arame utilizado foi o da classe ER70S-6 com diametro de 1,2 mm para o arame eletrodo e 1,0 mm para o arame frio, os dois arames foram alimentados em cabeçotes independentes. As variáveis operacionais de entrada foram: a velocidade de alimentação de arame energizado, em três níveis, 4, 6 e 8 m/min e a velocidade de alimentação do arame frio em 50%, 60% e 70% da velocidade de alimentação do arame energizado. As soldagens foram automatizadas em simples deposição no sentido empurrando e o posicionamento do arame frio, em um único nível, Tandem em chanfro em “U” de chapas de aço ASTM 1020. As variáveis de resposta utilizadas foram: inspeção superficial dos cordões; análise da geometria (largura, penetração, reforço e diluição) da solda e econômicas (taxa de fusão, taxa de deposição, rendimento e custo operacional). Os resultados indicaram que para a análise superficial, com o uso do gás Ar25%CO² a superfície dos cordões mostraram-se mais homogêneas em relação ao CO² e com menor índice de salpicagem, para a análise das características econômicas, o processo GMAW-CW sempre foi superior ao processo convencional, quanto aos custos operacionais o processo convencional mostrou-se menor, porém não houve o preenchimento do chanfro, o que ocorreu com a utilização do processo GMAW-CW.

Unbalanced Three-Phase Induction Motors Starting and Arc Welding Machines Short-Circuit Modeling

Three-Phase Induction Motors (TIM) and Arc Welding Machines (AWM) are loads of special behavior widely used in industrial and commercial installations, and therefore may contribute significantly to the deterioration of the quality of energy supplied by utilities. This paper proposes a modeling in constant power of the unbalanced TIM starting using Genetic Algorithm (GA) and AWM short-circuit based on their statics characteristics curves. The proposed models are compared with the conventional models in the literature. The results showed the good performance of the proposed models, allowing a more precise analysis of the real requests of these loads.