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.

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.

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.

Microstructural characterization of electron beam evaporated tungsten oxide films for gas sensing applications

Tungsten trioxide is one of the potential semiconducting materials used for sensing NH3, CO, CH4 and acetaldehyde gases. The current research aims at development, microstructural characterization and gas sensing properties of thin films of Tungsten trioxide (WO3). In this paper, we intend to present the microstructural characterization of these films as a function of post annealing heat treatment. Microstructural and elemental analysis of electron beam evaporated WO3 thin films and iron doped WO3 films (WO3:Fe) have been carried out using analytical techniques such as Transmission electron microscopy, Rutherford Backscattered Spectroscopy and XPS analysis. TEM analysis revealed that annealing at 300oC for 1 hour improves cyrstallinity of WO3 film. Both WO3 and WO3:Fe films had uniform thickness and the values corresponded to those measured during deposition. RBS results show a fairly high concentration of oxygen at the film surface as well as in the bulk for both films, which might be due to adsorption of oxygen from atmosphere or lattice oxygen vacancy inherent in WO3 structure. XPS results indicate that tungsten exists in 4d electronic state on the surface but at a depth of 10 nm, both 4d and 4f electronic states were observed. Atomic force microscopy reveals nanosize particles and porous structure of the film. This study shows e-beam evaporation technique produces nanoaparticles and porous WO3 films suitable for gas sensing applications and doping with iron decreases the porosity and particle size which can help improve the gas selectivity.

Electron beam evaporation of tungsten oxide films for gas sensors

Pure and Iron incorporated nanostructured Tungsten Oxide (WO3) thin films were investigated for gas sensing applications using noise spectroscopy. The WO3 sensor was able to detect lower concentrations (1 ppm-10 ppm) of NH3, CO, CH4 and Acetaldehyde gases at higher operating temperatures between 100oC to 250oC. The response of the WO3 sensor to NH3, CH4 and Acetaldehyde at lower temperatures (50oC-100oC) was significant when the sensor was photo-activated using blue-light emitting diode (Blue-LED). The WO3 with Fe (WO3:Fe) was found to show some response to Acetaldehyde gas only at relatively higher operating temperature (250oC) and gas concentration of 10 ppm.

Thin film deposition and characterization of pure and iron-doped electron-beam evaporated tungsten oxide for gas sensors

Pure Tungsten Oxide (WO3) and Iron-doped (10 at%) Tungsten Oxide (WO3:Fe) nanostructured thin films were prepared using a dual crucible Electron Beam Evaporation techniques. The films were deposited at room temperature in high vacuum condition on glass substrate and post-heat treated at 300 oC for 1 hour. From the study of X-ray diffraction and Raman the characteristics of the as-deposited WO3 and WO3:Fe films indicated non-crystalline nature. The surface roughness of all the films showed in the order of 2.5 nm as observed using Atomic Force Microscopy (AFM). X-Ray Photoelectron Spectroscopy (XPS) analysis revealed tungsten oxide films with stoichiometry close to WO3. The addition of Fe to WO3 produced a smaller particle size and lower porosity as observed using Transmission Electron Microscopy (TEM). A slight difference in optical band gap energies of 3.22 eV and 3.12 eV were found between the as-deposited WO3 and WO3:Fe films, respectively. However, the difference in the band gap energies of the annealed films were significantly higher having values of 3.12 eV and 2.61 eV for the WO3 and WO3:Fe films, respectively. The heat treated samples were investigated for gas sensing applications using noise spectroscopy and doping of Fe to WO3 reduced the sensitivity to certain gasses. Detailed study of the WO3 and WO3:Fe films gas sensing properties is the subject of another paper.

Electron beam evaporation of tungsten oxide films for gas sensors

Pure and Iron incorporated nanostructured Tungsten Oxide (WO3) thin films were investigated for gas sensing applications using noise spectroscopy. The WO3 sensor was able to detect lower concentrations (1 ppm-10 ppm) of NH3, CO, CH4 and Acetaldehyde gases at operating temperatures between 100 degrees celcius to 250 degrees celcius. The iron doped Tungsten Oxide sensor (WO3:Fe) showed some response to Acetaldehyde gas at relatively higher operating temperature (250 degrees celcius) and gas concentration of 10 ppm. The sensitivity of the WO3 sensor towards NH3, CH4 and Acetaldehyde at lower operating temperatures (50 degrees celcius - 100 degrees celcius) was significant when the sensor was photo-activated using blue-light emitting diode (Blue-LED). From the results, photo-activated WO3 thin film that operates at room temperature appeared to be a promising gas sensor. The overall results indicated that the WO3 sensor exhibited reproducibility for the detection of various gases and the WO3:Fe indicated some response towards Acetaldehyde gas.

Gas sensing characteristics of Fe-doped tungsten oxide thin films

This study reports on the gas sensing characteristics of Fe-doped (10 at.%) tungsten oxide thin films of various thicknesses (100–500 nm) prepared by electron beam evaporation. The performance of these films in sensing four gases (H2, NH3, NO2 and N2O) in the concentration range 2–10,000 ppm at operating temperatures of 150–280 °C has been investigated. The results are compared with the sensing performance of a pure WO3 film of thickness 300 nm produced by the same method. Doping of the tungsten oxide film with 10 at.% Fe significantly increases the base conductance of the pure film but decreases the gas sensing response. The maximum response measured in this experiment, represented by the relative change in resistance when exposed to a gas, was ΔR/R = 375. This was the response amplitude measured in the presence of 5 ppm NO2 at an operating temperature of 250 °C using a 400 nm thick WO3:Fe film. This value is slightly lower than the corresponding result obtained using the pure WO3 film (ΔR/R = 450). However it was noted that the WO3:Fe sensor is highly selective to NO2, exhibiting a much higher response to NO2 compared to the other gases. The high performance of the sensors to NO2 was attributed to the small grain size and high porosity of the films, which was obtained through e-beam evaporation and post-deposition heat treatment of the films at 300 °C for 1 h in air.