Laser cuts time to completion at vosper thornycroft
01 Jan 1996-Welding and metal fabrication (DMG Business Media)-Vol. 64, Iss: 8, pp 11-12
About: This article is published in Welding and metal fabrication.The article was published on 1996-01-01 and is currently open access. It has received 18 citation(s) till now.
TL;DR: In this paper, the authors evaluated the use of activated flux TIG (ATIG) welding for the austenitic stainless steels with fluxes of only one major component and found that even the very simple flux that was used can greatly increase the penetration of the weld bead.
Abstract: Gas tungsten arc welding is fundamental in those applications where it is important to control the weld bead shape and the metallurgical characteristics. This process is, however, of low productivity, particularly in the welding of large components. The activated flux TIG (ATIG) welding process, developed by the Paton Welding Institute in the 1960s, is now considered as a feasible alternative to increase the process productivity. ATIG welding uses a thin layer of an active flux that results in a great increase in weld penetration. This effect is, generally, connected to the capture of electrons in the outer parts of the arc by elements of high electronegativity, which constrict the arc causing an effect similar to that used in plasma arc welding. Generally, the literature does not present the flux formulations for ATIG welding, the few formulations that were found to have a complex nature. The present work evaluates the use of ATIG welding for the austenitic stainless steels with fluxes of only one major component. The changes in weld geometry were compared to variations in the electrical signals from the arc and the arc shape. The effect of the flux on the weld microstructure was also studied. The results indicate that even the very simple flux that was used can greatly increase the penetration of the weld bead.
TL;DR: An integrated approach using the Taguchi method, grey relational analysis and a neural network to optimize the weld bead geometry in a novel gas metal arc (GMA) welding process is presented.
Abstract: The objective of this paper is to present an integrated approach using the Taguchi method (TM), grey relational analysis (GRA) and a neural network (NN) to optimize the weld bead geometry in a novel gas metal arc (GMA) welding process. The TM is first used to construct a database for the NN. The GRA is adopted to solve the problem of multiple performance characteristics in a GMA welding process using activating flux. The grey relational grade obtained from the GRA is used as the output of the back-propagation (BP) NN. Then, a NN with the Levenberg-Marquardt BP (LMBP) algorithm is used to provide the nonlinear relationship between welding parameters and grey relational grade of each weldment. The optimal parameters of the novel GMA welding process were determined by simulating parameters using a well-trained BPNN model. Experimental results illustrate the proposed approach.
TL;DR: The effects of activating fluxes on welding arc were investigated in this article, where a special set of water-cooling system and stainless steel were used as parent material and high-speed camera system and oscillograph were used for capturing instantaneous arc shape and arc voltage respectively.
Abstract: The effects of activating fluxes on welding arc were investigated. A special set of water-cooling system and stainless steel were used as parent material. During welding process, high-speed camera system and oscillograph were used for capturing instantaneous arc shape and arc voltage respectively. The experimental results indicate that the SiO2 flux can increase the arc voltage. while TiO2 has no this effect on arc voltage. Compared with conventional tungsten inert gas welding (C-TIG), it is found that the arc shape of A-TIG welding used with the SiO2 flux has changed obviously.
TL;DR: In this article, a specific activated flux has been developed to enhance the depth of penetration up to 6mm in single pass by TIG welding in 9Cr-1Mo steel.
Abstract: Tungsten inert gas (TIG) welding process is generally used to produce high quality weld joints of 9Cr-1Mo steel. However, there is limitation associated with the depth of penetration achievable in single pass autogenous welding. Specific activated flux has been developed in the present work to enhance the depth of penetration up to 6 mm in single pass by A-TIG welding. 9Cr-1Mo steel A-TIG weld joint using activated flux was made in single pass welding while the multipass TIG weld joint using modified 9Cr-1Mo filler wire was made in seven passes. The enhancement in depth of penetration during A-TIG welding process for this steel was attributed to arc constriction. The strength properties of the A-TIG weld joint was superior to that of the multipass TIG weld joint. The multipass TIG weld joint exhibited slightly improved impact toughness than the A-TIG weld joint in PWHT condition. Therefore, there was no degradation in the microstructure and mechanical properties of the weld joint produced by A-TIG welding...
TL;DR: In this paper, a second-order response surface model was developed for predicting the response for the set of given input variables, and numerical and graphical optimization was performed using RSM to obtain the target depth of penetration (DOP) and heat-affected zone (HAZ) width using desirability approach.
Abstract: Optimization of A-TIG welding process parameters for 9Cr-1Mo steel has been carried out using response surface methodology (RSM) and genetic algorithm (GA). RSM has been used to obtain the design matrix for generating data on the influence of process parameters on the response variables. A second-order response surface model was developed for predicting the response for the set of given input variables. Then, numerical and graphical optimization was performed using RSM to obtain the target depth of penetration (DOP) and heat-affected zone (HAZ) width using desirability approach. Multiple regression models were developed based on the generated data, and then the models were used in GA to determine the optimum process parameters for achieving the target DOP and HAZ width. GA-based models employed two different selection processes. Both the RSM- and GA-based models suggested a number of solutions in terms of process parameters, and the identified solutions were validated by experiments. GA-based model employ...