• International Journal of Advanced Culture Technology
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• v.3 no.1
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• pp.61-67
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• 2015

The application of SS400 carbon steel and AISI430 ferritic stainless steel joint has been increased in industries because of the advantage of both metals was able to increase the service lifetime of the important structures. Therefore, a fusion welding process that could produce a sound weld and good joint properties should be optimized. This research is aimed to weld a butt joint of SS400 carbon steel and AISI430 ferritic stainless steel using Gas Metal Arc Welding (GMAW) welding process and to study the effects of welding parameters on joint properties. The experimental results were concluded as follows. The optimized welding parameter that produced the tensile strength of 448 MPa was the welding current of 110A, the welding speed of 400 mm/min and the mixed gas of $80%Ar+20%CO_2$. Increase of the welding current affected to increase and decrease the tensile strength of the joint, respectively. Lower welding current produced the incomplete bonding of the metals and indicated the low tensile strength. Microstructure investigation of the welded joint showed a columnar grain in the weld metal and a coarse grain in the heat affected zone (HAZ). The unknown hard precipitated phases were also found at the grain boundaries of the weld metal and HAZ. The hardness profile did not show the difference of the hardness on the joint that was welded by various welding currents but the hardness of the weld metal was higher than that of the other location.

• Proceedings of the KWS Conference
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• 1999.05a
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• pp.203-206
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• 1999

• Proceedings of the KWS Conference
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• 2000.10a
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• pp.40-42
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• 2000

This paper presents a new method of segmenting a one-dimensional signal into a set of features of type(line, Vee-groove, Lap-joint and etc.), A set of requirements for the segmentation process result from the application area, which in this case are laser welding, GMAW(Gas Metal Arc Welding), SAW(Submerged Arc Welding) and high speed tack welding. The algorithm is able to detect an exact welding position in the presence of noise and missing data, yet is reasonably economical to implement

• Journal of Welding and Joining
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• v.13 no.1
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• pp.156-166
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• 1995

Experimental models which are able to determine the deviation between weld line and weaving center by measuring the weld current during welding were proposed for the gas metal arc welding process. The models were used for developing a weld seam tracking system which controls the weaving speed of a welding torch. However, it was revealed that the tracking result of the system is affected by the welding conditions. Thus an arc sensor system was developed by using fuzzy control approach for overcoming the difficulty of modelling the nonlinear process. The rule base and parameters of the fuzzy control system were determined on the basis of the results of experiments. This fuzzy control system has shown the successful tracking capability for the wide operating range of welding conditions.

• Journal of the Korean Society of Industry Convergence
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• v.14 no.1
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• pp.15-21
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• 2011

Welding variables and conditions in gas metal arc welding (GMAW) effect on the quality and productivity of the weld, extensive research efforts have been made to analyze the effect of the welding variables and conditions. In this study dynamic behavior of GMAW system is investigated using the characteristics of the power supply, wire and welding arc. Characteristic equation of wire is modified to include the effect of droplets attached at the electrode tip. The dynamic characteristics of arc length, current, voltage with respect to the step, ramp inputs of CTWD was simulated. From results of simulation, some predictions about dynamic characteristics of GMAW and welding process are available. The proposed simulator and results appear to be utilized to determine the proper welding conditions, to be improved by considering power supply dynamic characteristics.

• Journal of Welding and Joining
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• v.23 no.3
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• pp.54-60
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• 2005

Tungsten Inert Gas(TIG) welding is today one of the most popular arc welding process because of its high quality welds and low equipment costs. Even if welding productivity increases with welding speed and current, this strategy is limited by the appearance of defects such as undercut and humping bead due to the depressed molten metal. The purpose of this study investigates the effect of He mixing ratio on the characteristics with high current and speed in TIG welding. The conclusions obtained permit to explain the arc start characteristics quantitatively and the maximum welding speed on stable bead formation with He mixing ratio for high current and speed TIG welding observed in experiments. Also through the relation of the maximum arc pressure and surface depression depth at high current and speed TIG welding, it made clear the mechanism of unstable bead formation.

• 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.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.4
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• pp.132-139
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• 2002

In GMAW(Gas Metal Arc Welding) processes, bead geometry (penetration, bead width and height) is a criterion to estimate welding quality. Bead geometry is affected by welding current, arc voltage and travel speed, shielding gas, CTWD (contact-tip to workpiece distance) and so on. In this paper, welding process variables were selected as welding current, arc voltage and travel speed. And bead geometry was reasoned from the chosen welding process variables using neuro-fuzzy algorithm. Neural networks was applied to design FL(fuzzy logic). The parameters of input membership functions and those of consequence functions in FL were tuned through the method of learning by backpropagation algorithm. Bead geometry could be reasoned from welding current, arc voltage, travel speed on FL using the results learned by neural networks. On the developed inference system of bead geometry using neuro-furzy algorithm, the inference error percent of bead width was within $\pm$4%, that of bead height was within $\pm$3%, and that of penetration was within $\pm$8%. Neural networks came into effect to find the parameters of input membership functions and those of consequence in FL. Therefore the inference system of welding quality expects to be developed through proposed algorithm.

• Proceedings of the KWS Conference
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• 2002.10a
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• pp.289-294
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• 2002

This paper presents first results of an interdisciplinary research project for the development of an "intelligent" welding helmet. Contrary to conventional welding helmets the system allows a detailed observation both of the welding process and the environment. By methods of virtual and augmented reality additional information can be supplied to the welder. The system can be used for welding preparation, welding process observation and quality assurance.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.04a
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• pp.36-41
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• 1997

With the trend towards welding automation and robozation, mathematical models for studying the influence of various parameters on the weld bead geometry in Gas Metal Arc(GMA) welding process are required. The results of bead on plate welds deposited using the GMA welding process has enabled mathematical relationships to be developed that model the weld bead geometry. Experimental results were compared to outputs obtained using existing formulae that correlate process input variables to output parameters and subsequent modelling was performed in order to better predict the output of the GMA welding process. The aim of this work was to explain the relationships between GMA welding variables and weld bead geometry and thus, be able to predict input weld bead size. The relationships can be usefully employed for open loop process control and also for adaptive control provided that dynamic sensing of process output is performed.