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.
The welding quality in multi-pass welding is mainly dependent on the pre-heating from pervious pass or root-pass welding. In this study, a Mahalanobis Distance and normal distribution method is illustrated and employed to determine whether welding faults have occurred after each pass welding and also to quantify welding quality percentage. To successfully accomplish this objective, sets of multi-pass welding experiment were performed with different welding parameters in each pass; the welded samples of SS400 steel flats adopting the bead-on-plate technique were employed in the experiment. The result of current and voltage for each pass is obtained through the real time mentoring systems. In order to verify the effect of the performance and weld quality of the different weld-pass, Mahalanobis distances for voltage and current values were calculated and used for qualitative and quantitative analysis with comparison to values obtained from the root-pass as reference welds. The results of the experiment and statistical analysis have demonstrated that the weld faults after each weld pass is feasible.
The GMA welding process involves large number of interdependent variables which may affect product quality, productivity and cost effectiveness. The relationships between process parameters for a fillet joint and bead geometry are complex because a number of process parameters are involved. To make the automated GMA welding, a method that predicts bead geometry and accomplishes the desired mechanical properties of the weldment should be developed. The developed method should also cover a wide range of material thicknesses and be applicable for all welding position. For the automatic welding system, the data must be available in the form of mathematical equations. In this study a new intelligent model with genetic algorithm has been proposed to investigate interrelationships between welding parameters and bead geometry for the automated GMA welding process. Through the developed model, the correlation between process parameters and bead geometry obtained from the actual experimental results, predicts that data did not show much of a difference, which means that it is quite suitable for the developed genetic algorithm. Progress to be able to control the process parameters in order to obtain the desired bead shape, as well as the systematic study of the genetic algorithm was developed on the basis of the data obtained through the experiments in this study can be applied. In addition, the developed genetic algorithm has the ability to predict the bead shape of the experimental results with satisfactory accuracy.
Robotic GMA (Gas Metal Arc) welding process is one of widely acceptable metal joining process. The heat and mass inputs are coupled and transferred by the weld arc to the molten weld pool and by the molten metal that is being transferred to the weld pool. The amount and distribution of the input energy are basically controlled by the obvious and careful choices of welding process parameters in order to accomplish the optimal bead geometry and the desired quality of the weldment. To make effective use of automated and robotic GMA welding, it is imperative to predict online faults for bead geometry and welding quality with respect to welding parameters, applicable to all welding positions and covering a wide range of material thickness. MD (Mahalanobis Distance) technique was employed for investigating and modeling the GMA welding process and significance test techniques were applied for the interpretation of the experimental data. To successfully accomplish this objective, two sets of experiment were performed with different welding parameters; the welded samples from SM 490A steel flats. First, a set of weldments without any faults were generated in a number of repeated sessions in order to be used as references. The experimental results of current and voltage waveforms were used to predict the magnitude of bead geometry and welding quality, and to establish the relationships between weld process parameters and online welding faults. Statistical models developed from experimental results which can be used to quantify the welding quality with respect to process parameters in order to achieve the desired bead geometry based on weld quality criteria.
In robotic GMA (Gas Metal Arc) welding process, heat and mass inputs are coupled and transferred by the weld arc and molten base material to the weld pool. The amount and distribution of the input energy are basically controlled by the obvious and careful choices of welding process parameters in order to accomplish the optimal bead geometry and the desired mechanical properties of the quality weldment. To make effective use of automated and robotic GMA welding, it is imperative to predict online faults for bead geometry and welding quality with respect to welding parameters, applicable to all welding positions and covering a wide range of material thickness. To successfully accomplish this objective, two sets of experiment were performed with different welding parameters; the welded samples from SM 490A steel flats adopting the bead-on-plate technique were employed in the experiment. The experimental results of current and voltage waveforms were used to predict the magnitude of bead geometry and welding quality, and to establish the relationships between weld process parameters and online welding faults. MD (Mahalanobis Distance) technique is employed for investigating and modeling of GMA welding process and significance test techniques were applied for the interpretation of the experimental data. Statistical models developed from experimental results which can be used to control the welding process parameters in order to achieve the desired bead geometry based on weld quality criteria.
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