• Journal of Welding and Joining
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• v.20 no.3
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• pp.60-66
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• 2002

Almost every die fur automobiles must be corrected or remodeled for minor geometrical changes or for better hardness characteristics by arc welding process. Although many other kinds of arc welding processes have been automated with robots, this molten metal deposition process for die remodeling still depend entirely on experienced welders. In this study, the database for bead shapes with respect to welding parameters are constructed by experiments to automate the molten metal deposition by arc welding process. And the changes of welding parameters for inclined base metal are studied to consider the effect of die geometries fur the welding process.

• 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 the Korean Society of Manufacturing Technology Engineers
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• v.7 no.6
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• pp.57-63
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• 1998

An adaptive control in the robotic GMA welding is employed to monitor information about weld characteristics and process parameters as well as to modify those parameters to hold weld quality within acceptable limits. Typical characteristics are the bead geometry, composition, microstructure, appearance, and process parameters which govern the quality of the final weld. The main objectives of this thesis are to realize the mapping characteristics of bead width through learning. After learning, the neural estimation can estimate the bead width desired form the learning mapping characteristic. The design parameters of the neural network estimator(the number of hidden layers and the number of nodes in a layer) are chosen from an estimation error analysis. A series of bead of bead-on-plate GMA welding experiments was carried out in order to verify the performance of the neural network estimator. The experimental results show that the proposed neural network estimator can predict the bead width with reasonable accuracy and guarantee the uniform weld quality.

• 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.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.637-642
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• 2002

Generally, the use of robots in manufacturing industry has been increased during the past decade. GMA(Gas Metal Are) welding is an actively growing area and many new procedures have been developed for use with high strength alloys. One of the basic requirement for welding applications is to study relationships between process parameters and bead geometry. The objective of this paper is to develop a new approach involving the use of neural network and multiple regression methods in the prediction of bead geometry for GMA welding process and to develop an intelligent system that enables the prediction of bead geometry using Rapid Prototyping(RP) in order to employ the robotic GMA welding processes. This system developed using MATLAB/SIMULINK, could be effectively implemented not only for estimating bead geometry, but also employed to monitor and control the bead geometry in real time.

• Journal of Welding and Joining
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• v.34 no.4
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• pp.40-47
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• 2016

The Zn coating on automotive galvanized steel sheets can improve corrosion resistance. However, the boiling temperature of Zn is lower than the melting temperature of steel and it causes well-known spatter and porosity problem. One of most prominent solutions is a pretreatment of Zn coating by an additional welding arc prior to the main welding process. In this research, GTA and GMA are selected as heat sources for pretreatment and main welding processes, respectively. The authors suggested three possible mechanisms to reduce weld defects by the GTA pretreatment: (1) Formation of gap between the sheets; (2) Evaporation of Zn layer; (3) Oxidation of Zn layer. Among them, Zn Oxidation is the most important mechanism to reduce weld defects in the GTA-GMA hybrid process.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.05a
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• pp.369-373
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• 2001

In this paper, we proposes the steps adopted to construct the neural network model for GMAW welds. Conventional, automated process generally involves sophisticated sensing and control techniques applied to various processing parameters. Welding parameters are influenced by numerous factors, such as welding current, arc voltage, torch travel speed, electrode condition and shielding gas type and flow rate etc. In traditional work, the structural mathematical models have been used to represent this relationship. Contrary to the traditional model method, neural network models are based on non-parametric modeling techniques. For the welding process modeling, the non-linearity at well as the coupled input characteristics makes it apparent that the neural network is probably the most suitable candidate for this task. Finally, a suitable proposal to improve the construction of the model has also been presented in the paper.

• Journal of Welding and Joining
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• v.29 no.4
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• pp.41-47
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• 2011

GMA welding process is a production process to improve productivity for the provision of higher welding quality of material. These includes numerous process variables that could affect welding quality, productivity and cost savings. Recently, the welding part of construction equipment had frequent failure of major components in the welding part of each subsidiary material due to shock which is very poor according to the welding part. Therefore, the implementation of sound welding procedure is the most decisive factor for the reliability of construction machinery. The data generated through experiments conducted in this study has validated its effectiveness for the optimization of bead geometry and process variables is presented. The criteria to control the process parameters, to achieve a good bead geometry. This study has developed mathematical models and algorithms to predict or control the bead geometry in GMA fillet welding process.

• Journal of Welding and Joining
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• v.22 no.4
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• pp.35-42
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• 2004

Contact tips are required to have a higher resistance to wear and thus to have an extended life time under the advanced GMAW welding process. Several requirements have been specified and employed by domestic industries for selecting their tips for such a purpose. However no attempt has been made to justify their requirements based on the experimental data of wear resistance or life time of contact tips. In this study, five different contact tips with three different compositions were employed for actual GMA welding up to 4 hours and were evaluated their wear resistance by measuring in every one hour the area of enlarged hole at the exit side. Experimental results clearly showed that the Cr-containing tips strengthened by precipitation hardening have much better resistance to wear than those made by work hardening. It was further noticed that Cr is an excellent alloying element for improving the wear resistance of contact tips only when it is in an properly aged condition. Initial hardness may play some role in the early stage of wear but not in the later stage of welding because the microstructure of tip changes significantly by the prolonged exposure to welding arc heat. Based on these results, critical review has been made on the current requirements employed by domestic industries. Of importance is that a new guideline has been confirmed to be more reasonable.

• Journal of Welding and Joining
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• v.34 no.2
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• pp.59-66
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• 2016

The Gas Metal Arc(GMA) welding, called Metal Inert Gas(MIG) welding, has been an important component in manufacturing industries. A key technology for robotic welding processes is seam tracking system, which is critical to improve the welding quality and welding capacities. The objectives of this study were to develop the intelligent and cost-effective algorithms for image processing in GMA welding which based on the laser vision sensor. Welding images were captured from the CCD camera and then processed by the proposed algorithm to track the weld joint location. The proposed algorithms that commonly used at the present stage were verified and compared to obtain the optimal one for each step in image processing. Finally, validity of the proposed algorithms was examined by using weld seam images obtained with different welding environments for image processing. The results proved that the proposed algorithm was quite excellent in getting rid of the variable noises to extract the feature points and centerline for seam tracking in GMA welding and could be employed for general industrial application.