A study on welding quality of robotic arc welding process using Mahalanobis distance method

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

Numerical and experimental study of residual stress and strain in multi - pass GMA welding

Purpose: Recently, manufacturing industries have been concentrated on selection an optimal of welding parameter and condition that reduces the risk of mechanical failures on weld structures should be required in manufactory industry. In robotic GMA (Gas Metal Arc) welding process, 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 mechanical properties of the quality weldment. The residual stress and welding deformation have the large impact on the failure of welded structures. Design/methodology/approach: To achieve the required precision for welded structures, it is required to predict the welding distortions at the early stages. Therefore, this study represented 2D Finite Element Method (FEM) to predict residual stress and strain on thick SS400 steel metal plate. Findings: The experiment for Gas Metal Arc (GMA) welding process 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. Practical implications: This present study on based on the numerical analysis using ansys software, for a thick multi-pass GMA welding. A birth and death technique is employed to control the each weld pass welding. Originality/value: The developed 2D multi-pass model employs Goldak’s heat distribution, to simulate welding on SS400 steel butt-weld joint with a thickness of 16mm. moreover the numerical results are validated with experiment results

A study on simulation model and kinematic model of welding robot

Purpose: This study tries to develop a simulation model of six degree freedom for Faraman AM1 welding robot using CATIA V5 and compares with the computed kinematic model for robotic welding. The error varification of simulated model and kinematics of the robot is also being carried out. Design/methodology/approach: CATIA (Computer Aided Three dimensional Interactive Application) is a multi-platform PLM/CAD/CAM/CAE commercial software suite to use to develop six degree freedom for Faraman AM1 welding robot. The forward kinematic and inverse kinematic equations are also used to verify the developed model. Findings: The results obtained from the six degree freedom for Faraman AM1 simulated model has a good agreement with computed kinematic models equations. The catia V5 a very powerful tool which could used in develope a simulation for robotic welding system. The the angle error between simulated model and computed inverse kinmenatic equation obtained too very small. Research limitations/implications: The developed simulated in Catia is mainly aimed to be used in GMA welding process. D-H (Denavit-Hartenberg) convection is used to determine the orthonormal coordinate frames at different joints of a robotic manipulator and determining four kinematic parameters. Originality/value: The six degree freedom for Faraman AM1 welding robot is model to analysed and compared with forward and inverse knimatic

Prediction welding quality in multi - pass welding process using Mahalanobis distance method

The welding quality in multi-pass welding is mainly dependent on the pre-heating from previous 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

Numerical and experiment study of residual stress and strain in multi - pass GMA welding

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

Numerical studies on residual stress and strain distribution in Thick-Welded Plate

In this study, a numerical method is used to investigate the effect of welding parameter and cooling time on residual stress and strain in the multi-pass butt Gas Metal Arc (GMA) welding process, while plate is restrained in transverse direction of welding direction. Two dimension finite element simulations were implemented to predict the welding temperature distributions, residual stress and strain on 16mm thick SM490 steel plate for different input welding parameter at each weld pass using Ansys software. Not only the temperature dependent thermal properties were considered, but also birth and death technique is employed to control the process of weld filling. The simulated results provide good evidence that residual stress and strain is mainly dependent on heat load, welding parameter and restraints on material. The large amount of residual stress and strain is being developed around the Heat Affected Zone, Fusion zone and welding regions. The elastic FE model can be used to predict precisely the welding deformation and residual stress in a thick multi pass butt welding. Furthermore, the extensive experiment effort of component testing could be reduced adequate and deliberate application of welding FE simulation

A smart system to determine and control for the process parameters in pipeline welding

Determination of the optimal welding parameters to achieve specific weldments on a new material is usually an expensive and time consuming. To determine the welding parameters using Artificial Intelligence (AI) technologies, one must consider many factors including productivity, thermal input, defect formation, and process robustness. Determination of the welding parameters for pipeline welding is based on a skilled welder’s long-term experience rather than on a theoretical and analytical technique. In this paper, a smart system develops which determines welding parameters and position for each weld pass in pipeline welding based on one database and FEM model, two BP neural network models and a C-NN model. The preliminary test of the system has indicated that the system could determine the welding parameters for pipeline welding quickly, from which good weldments can be produced without experienced welding personnel. Experiments using the predicted welding parameters from the developed system proved the feasibility of interface standards and intelligent control technology to increase productivity, improve quality, and reduce the cost of system integration