• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.608-611
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• 2002

In GMAW(Gas Metal Arc Welding) process, 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, CTWB (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 negro-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.

• Journal of Welding and Joining
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• v.24 no.1
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• pp.88-92
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• 2006

Laser-rotating arc hybrid welding was introduced by combining $CO_2$ laser and rotating gas metal arc welding. While the arc rotation enhances the weld pool motion, it reduces the undercut formation which is one of most critical weld defects in the conventional laser-arc hybrid welding. This research investigated the bead characteristics according to the welding parameters such as frequency of rotation, welding voltage, shielding gas composition and interspacing distance between laser and we. The welding parameters were selected to reduce spatter generation and ensure sound weld beads fur bead welding and butt welding with various joint gaps. Gap bridging ability was improved, such that the sound weld beads were achieved for butt joint with up to 2mm joint sap, with no adjustment of CTWD(Contact tip-to-workpiece distance) and electrode diameter.

• Proceedings of the KWS Conference
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• 1996.10a
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• pp.139-141
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• 1996

Among various welding parameters, the welding current which is inversely proportional to the tip-to-workpiece distance in GMAW is an essential parameter to monitor the GMAW process of horizontal fillet joints. For the case of weld defect such as overlap in horizontal fillet welding, therefore, the signal processing for process monitoring or automatic seam tracking should be modified by considering the weld pool surface geometry including the corresponding weld defect. In other words, the adequate signal processing algorithm is indispensible to improve the performance of the arc sensor. However, arc sensor algorithm already developed usually focus on weld seam tracing but do not considering the weld qualities. In this paper, various experiments were carried out to investigate the tendencies of the weld defects when weaving motion is added, and the experimental method based on 2$^n$ factorial design was proposed for deriving the mathematical model between the leg length and the various welding conditions. Moreover, a signal processing method based on the artificial neural network(Adaptive Resonance Theory) was proposed far discriminating the current signal of sound weld beads from that of weld beads with overlap. Finally, the algorithm for weld seam tracking combined with the mathematical modeling and the signal processing method was carried out to track the weld line in conjunction with the improvement of the weld qualities. The reliability of the proposed algorithms were evaluated through various experiments, which showed that the proposed algorithms could be effectively used for arc welding automation.

• 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.

• Journal of Welding and Joining
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• v.8 no.3
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• pp.70-78
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• 1990

The harsh nature of welding environments makes welding a prime candidate for process automation. Among the variety of welding processes available, gas metal arc welding is one of the most frequently used methods, primarily because it is highly suited to a wide range of applications, and also to automation. Automatic seam tracking method is one of the most demanded techniques for automatic control of arc welding. In this study a seam tracking system has been developed by using the welding arc itself as a sensor. This paper described the principle and experimental result of the arc sensor system, as well as the development and application of the automatic CO_2$ welding for the horizontal fillet welding. A basic problem in horizontal fillet welding is the prevention of hanging bead formation such as undercut at the vertical plate and overlap at the horizontal plate. To produce the symmetric bead shape, the relationship of bead shape to welding parameters(welding velocity, weaving width, weaving speed, tip to workpiece distance) was also investigated.

• Journal of Ocean Engineering and Technology
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• v.27 no.5
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• pp.77-81
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• 2013

The effects of the welding parameters, contact tip-to-workpiece distance (CTWD), current, and voltage on the diffusible hydrogen content in weld metal deposited by self-shielded flux cored arc welding were investigated and rationalized by comparing the amount of heat generated in the extension length of the wire. This showed that as CTWD increased from 15mm to 25mm, the amount of heat generated was increased from 71.1J to 174.8J, and the hydrogen content was decreased from 11.3mL/100g to 5.9mL/100 g. Even if little difference was observed in the amount of heat generated, the hydrogen content was increased with an increase in voltage because of the longer arc length. A regression analysis showed that the regression coefficient of voltage in self-shielded flux cored arc welding is greater than that in $CO_2$ arc welding. This implies that voltage control is more important in self-shielded flux cored arc welding than in $CO_2$ arc welding.

• Journal of Welding and Joining
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• v.13 no.4
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• pp.122-131
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• 1995

An automatic welding equipment for thick plates requires the capability of the seam tracking of the weld line which often includes misalignment of the workpiece and variation of groove width. In this study, an automatic welding equipment and control algorithms based on the arc sensor were proposed for the GMA welding of thick plates which had misalignment and gap variation. The developed system being constituted with 5 axis can be automatically controlled by computer and also automnatically set the welding conditions such as welding current, and voltage. The proposed algorithms for the seam tracking in multi-pass welding of the thick plates were constituted as follows : the detection of weaving-end point for findng the variation of groove width, the control of welding velocity for acquiring a constant thickness deposition of weld metal, and the calculation of groove width and height of an arbitrary pass in the multi-pass weld. As results of the application of the system, it was revealed that the system had a good capability in seam tracking and made an excellent weld quality in V groove butt joint.

• Journal of the Korean Society of Industry Convergence
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• v.5 no.2
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• pp.111-118
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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 FLC(fuzzy logic control), The parameters of input membership functions and those of consequence functions in FLC were tuned through the method of learning by backpropagation algorithm, Bead geometry could he reasoned from welding current, arc voltage, travel speed on FLC using the results learned by neural networks. On the developed inference system of bead geometry using neuo-fuzzy 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 FLC. Therefore the inference system of welding quality expects to be developed through proposed algorithm.