• Journal of Welding and Joining
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• v.32 no.6
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• pp.8-13
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• 2014

One of the main challenges of the automatic arc welding process which has been widely used in various constructions such as steel structures, bridges, autos, motorcycles, construction machinery, ships, offshore structures, pressure vessels, and pipelines is to create specific welding knowledge and techniques with high quality and productivity of the production-based industry. Commercially available automated arc welding systems use simple control techniques that focus on linear system models with a small subset of the larger set of welding parameters, thereby limiting the number of applications that can be automated. However, the correlations of welding parameters and bead geometry as welding quality have mostly been linked by a trial and error method to adjust the welding parameters. In addition, the systematic correlation between these parameters have not been identified yet. To solve such problems, a new or modified models to determine the welding parameters for tandem GMA (Gas Metal Arc) welding process is required. In this study, A new predictive model called STACO model, has been proposed. Based on the experimental results, STACO model was developed with the help of a standard statistical package program, MINITAB software and MATLAB software. Cross-comparative analysis has been applied to verify the reliability of the developed model.

• Journal of Welding and Joining
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• v.26 no.2
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• pp.85-91
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• 2008

In general, the quality of a welding process tends to vary with depending on the work environment or external disturbances. Hence, in order to achieve the desirable quality of welding, we should have the optimal welding condition that is not significantly affected by these changes in the environment or external disturbances. In this study, we used a dual response surface method in consideration of both the mean output variables and the standard deviation in order to optimize the V-groove arc welding process. The input variables for GMA welding process with the dual response surface are welding voltage, welding current and welding speed. The output variables are the welding quality function using the shape factor of bead geometry. First, we performed welding experiment on the interested area according to the central composite design. From the results obtained, we derived the regression model on the mean and standard deviation between the input and output variables of the welding process and then obtained the dual response surface. Finally, using the grid search method, we obtained the input variables that minimize the object function which led to the optimal V-groove arc welding process.

• Journal of Welding and Joining
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• v.21 no.3
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• pp.46-50
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• 2003

The horizontal fillet joint is one of the most important weld joints in the shipbuilding industry. High-speed rotating arc welding, which can increase the leg length, is an effective way to improve the weld productivity and quality for the horizontal fillet welding. Based on the Taguchi method, the effects of welding parameters on bead characteristics - leg length, asymmetry, undercut, overlap - are investigated fur high-speed welding process. As a result, the adequate welding parameters are selected for the required leg length, symmetric bead and no undercut. Besides, considerably consistent leg length is observed for the horizontal fillet welding with gap variation up to 3mm.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.32 no.11
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• pp.978-983
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• 2008

An application of innovative laser+GMA hybrid welding process is presented for reducing bead humping defects in high speed welding and increasing side wall fusion in narrow groove welding without torch or wire oscillation. In this hybrid process, the laser heat input is applied adjacent to the weld pool at a relatively low power density to produce a wider, flatter weld bead. In bead on plate in sheet metal gauges, the hybrid process was able to produce hump-free welds from 70ipm (${\sim}1780mm/min$) to over 150ipm (${\sim}3810mm/min$) of the travel speed compared to the un-assisted GMAW process. A square-butt joint in 15mm A572 Gr50 steel welds was investigated. A square butt joint with a gap of 3.2mm was filled with 6 passes. Liquid Nitrogen calorimetry and innovative $CO_2$ laser reflective optics were also developed to demonstrate the concept of hybrid welding.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.12
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• pp.84-91
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• 2004

A CCD camera with a laser stripe was applied to realize the automatic weld seam tracking in GMAW. It takes relatively long time to process image on-line control using the basic Hough transformation, but it has a tendency of robustness over the noises such as spatter and arc tight. For this reason, it was complemented with adaptive Hough transformation to have an on-line processing ability for scanning specific weld points. The adaptive Hough transformation was used to extract laser stripes and to obtain specific weld points. The 3-dimensional information obtained from the vision system made it possible to generate the weld torch path and to obtain the information such as width and depth of weld line. In this study, a neural network based on the generalized delta rule algorithm was adapted for the process control of GMA, such as welding speed, arc voltage and wire feeding speed.

• Journal of Welding and Joining
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• v.15 no.6
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• pp.116-123
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• 1997

In GMA welding processes, bead contour and penetration patterns are criterion to estimate weld quality. Bead geometry is commonly defined with width, height and depth. When weaving is taken into account, selection of welding conditions is known to be difficult. Thus, empirical or trial-and-error method are usually introduced. This study examined the correlation of welding process variables including weaving parameters with bead geometry using srtificial neural networks(ANN). The main task of the Ann estimator is to realize the mapping characteristics from the sampled welding process variables to the actual bead geometry through training. After the neural network model is constructed, welding process variables for desired bead geometry is selected by inverse model. Experimental varification of the inverse model is conducted through actual welding.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.25 no.1
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• pp.30-35
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• 2016

Tandem arc welding is a guarantor for high efficiency and cost saving since the quantity of wire which is deposited in the welding is approximated 30% greater that in conventional welding. The welding process is now being successfully applied in many industries. However, in the case of tandem arc welding, good quality and high productivity should depend on the welding parameters. Therefore, an intelligent algorithms for the automatic tandem arc welding process has been necessarily required. In this study, a predictive model based on the neural network by using the data acquired during tandem gas metal arc (GMA) welding process has been developed. To verify the reliability of the developed predictive model, a mutual comparison with the surface of the top-bead width obtained from actual experiments has been analyzed.

• Journal of Welding and Joining
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• v.32 no.2
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• pp.22-28
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• 2014

To enhance productivity and provide high quality production material in a GMA welding process, weld quality, productivity and cost reduction affects the number of process variables. In addition, a reliable welding process and conditions must be implemented to reduce weld structure failure. In various industries the welding process mathematical model is not fully formulated for the process parameter and on the welding conditions, therefore only partial variables can be predicted. The research investigates the interaction between the welding parameters (welding speed, distance between electrodes, and flow rate of shielding gas) and bead geometry for predicting the weld bead geometry (bead width, bead height). Taguchi techniques are applied to bead shape to develope curve equation for predicting the optimized process parameters and quality characteristics by analyzing the S/N ratio. The experimental results and measured error is within the range of 10% presenting satisfactory accuracy. The curve equation was developed in such a way that you can predict the bead geometry of constructed machinery that can be used for making tandem welding process.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.6
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• pp.49-55
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• 2008

Arc welding process is one of the most important technologies to join metal plates. Robotic welding offers the reduced manufacturing cost sought, but its widespread use demands a means of sensing and correcting for inaccuracies in the part, the fixturing and the robot. A number of problems that need to be addressed in robotic arc welding processes include sensing, joint tracking, and lack of adequate models for process parameter prediction and quality control. Problems with parameter settings and quality control occur frequently in the GMA(Gas Metal Arc) welding process due to the large number of interactive process parameters that must be set and accurately controlled. The objectives of this paper are to realize the mapping characteristics of bead width using a sensitivity analysis and develop the neural network and multiple regression method, and finally select the most accurate model in order to control the weld quality(bead width) for fillet welding. The experimental results show that the proposed neural network estimator can predict bead width with reasonable accuracy, and guarantee the uniform weld quality.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1404-1407
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• 1996

A quality monitoring system in butt welding process is proposed to estimate weld pool sizes. The geometrical parameters of the weld pool such as the top bead width and the penetration depth plus half back width are utilized to prove the integrity of the weld quality. The monitoring variables used are the surface temperatures measured at three points on the top surface of the weldment. The temperature profile is assumed that it has a gaussian distribution in vertical direction of torch movement and verify this assumption through temperature analysis. A neural network estimator is designed to estimate weld pool size from temperature informations. The experimental results show that the proposed neural network estimator which used gaussian distribution as temperature information can estimate the weld pool sizes accurately than used three point temperatures as temperature information. Considering the change of gap size in butt welding, the experiment were performed on various gap size.