Recent trends of welding system have been focused on the development of new process in order to achieve better quality, higher productivity and friendly to the environmentally in welding process. The Magnetic Pulse Welding(MPW) process is based on the principle of a tremendous amount of energy compressed and discharged for an extremely short period of time. The purpose of this paper is to investigate welding characteristics according to the distributions of an electromagnetic force on the weldment using a finite element method and to find the optimal process parameters such as input current and frequency. To successfully accomplish this objective, a 2-dimensional axisymmetric electromagnetic numerical model has firstly been developed. The equation was solved using a general electromagnetic mechanics computer program, ANSYS code. The comparison between the calculated and measured results has been carried out to verify the developed system.
There must selection an optimal of welding parameter and condition that reduces the risk of mechanical failures on weld structures.The residual stress and welding deformation have the large impact on the failure of welded structures.To achieve the required precision for welded structures, it is required to predict the welding distortions at the early stages.Therefore, this study uses 2DFinite Element Method (FEM) to predict residual stress and strain on thick SS400 steel metal plate. A birth and death technique is employed to control the each weld pass welding. Gas Metal Arc (GMA) welding experiment is also performed with similar welding condition to validate the FE results.The simulated and experiment results provide good evidence that heat input is main dependent on the welding parameter and residual stress and distortions are mainly affected by amount on heat input during each weld-pass.
Generally welding is one of the most important processes to have a strong influence on the quality and productivity from a manufacture-based industry such as shipbuilding, automotive and machinery. The GMA(Gas Metal Arc) welding process involves large number of interdependent welding parameters which may affect product quality, productivity and cost effectiveness. To solve such problems, mathematical models are required to select the welding parameters for GMA welding process. In this study, the GMA welding process was studied using the information generated during the welding. The statistical analysis of a generalized regression approach was conducted by the following three methods: Firstly using the mathematical model (linear regression, 2nd regression); Secondly GA(Genetic Algorithm) with intelligent models; And finally using response surface analysis of models to develop the relationships between welding parameters and bead width as welding quality.
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