• Journal of Welding and Joining
• /
• v.25 no.6
• /
• pp.64-70
• /
• 2007

Recently, several models to control weld quality, productivity and weld properties in arc welding process have been developed and applied. Also, the applied model to make effective use of the robotic GMA(Gas Metal Arc) welding process should be given a high degree of confidence in predicting the bead dimensions to accomplish the desired mechanical properties of the weldment. In this study, a development of the on-line learning neural network models that investigate interrelationships between welding parameters and bead width as well as apply for the on-line quality control system for the robotic GMA welding process has been carried out. The developed models showed an excellent predicted results comparing with the predicted ability using off-line learning neural network. Also, the system will extend to other welding process and the rule-based expert system which can be incorporated with integration of an optimized system for the robotic welding system.

• Journal of Welding and Joining
• /
• v.15 no.3
• /
• pp.118-127
• /
• 1997

With the trend towards welding automation and robotization, mathematical models for studying the influence of various variables on the weld bead geometry in gas metal arc (GMA) welding process are required. Partial penetration, single-pass bead-on-plate welds using the GMA welding process were fabricated in 12mm mild steel plates employed four different process variables. Experimental results has been designed to investigate the analytical and empirical formulae, and develop mathematical equations for understanding the relationship between process variables and weld bead geometry. The relationships can be usefully employed not only for open loop process control, but also for adaptive control provided that dynamic sensing of process output is performed.

• Journal of Welding and Joining
• /
• v.9 no.3
• /
• pp.34-40
• /
• 1991

An automatic welding system was constructed with a personal computer to capture the welding data in addition to vision seam tracking for the gas metal arc welding process. The monitoring of welding signals is composed of the acquisition of welding voltage and current, obtained by using two differential amplifiers and A/D converters, and processing of the measured data. Using interrupt handing circuit for time sharing, two jobs of seam tracking and monitoring were performed at the required sampling time. Relations between welding signals and various welding circumstances were analyzed from the experimental results.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.22 no.1
• /
• pp.22-28
• /
• 2013

Robotic GMA (Gas Metal Arc) welding process is one of widely acceptable metal joining process. The 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 quality of the 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. MD (Mahalanobis Distance) technique was employed for investigating and modeling the GMA welding process and significance test techniques were applied for the interpretation of the experimental data. To successfully accomplish this objective, two sets of experiment were performed with different welding parameters; the welded samples from SM 490A steel flats. First, a set of weldments without any faults were generated in a number of repeated sessions in order to be used as references. 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. Statistical models developed from experimental results which can be used to quantify the welding quality with respect to process parameters in order to achieve the desired bead geometry based on weld quality criteria.

• Proceedings of the KWS Conference
• /
• 2004.05a
• /
• pp.293-295
• /
• 2004

For the evaluation of corrosion resistance, Al 6061-T6 alloy was welded by Friction Sti. Welding(FSW) and Gas Metal Arc Welding(GMAW) evaluated by Tafel method and immersion test. The Tafel and immersion test results indicated that GMA weld was severely attacked compared with those of friction stir weld. It may be mainly due to the galvanic corrosion mechanism act on the CMA weld.

• Journal of Welding and Joining
• /
• v.20 no.1
• /
• pp.83-90
• /
• 2002

Metal Transfer in gas metal arc (GMA) welding is a complex phenomenon affected by many parameters of the welding conditions and material properties. In this research, the correlation equation between the welding condition and detaching droplet size and detaching velocity in GMA welding was studied via recession analysis on the results of numerical analysis using the volume-of-fluid (VOF) method. Welding parameters and material properties were grouped into three dimensionless numbers and detaching droplet size was expressed as the function of them. Second order and exponential multi-variable correlation forms were assumed, and the coefficients of these equations were calculated for globular and spray modes as well as entire transfer modes. Applying correlation equation into available experimental data, it shows good agreement.

• Proceedings of the KWS Conference
• /
• 1996.10a
• /
• pp.148-152
• /
• 1996

A transient two-dimensional (2D) model was developed for investigating the heat and fluid flow in old pools and determining velocity profile and temperature distribution for the Gas Metal Arc (GMA) welding process. The mathematical formulation deals with the driving farces (electromagnetic, buoyancy, surface tension and plasma drag forces) as well as energy exchange between the molten filler metal droplet and weld pools. A general thermofluid-mechanics computer program, PHOENICS, was employed to numerically solve the governing equation with the associated source terms. The results of computation have shown that the electromagnetic and surface tension farces as will as the molten filler metal droplet have major influence in shaping the weld pool geometry.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2002.05a
• /
• pp.608-611
• /
• 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
• /
• v.30 no.4
• /
• pp.4-9
• /
• 2012

• Journal of the Korean Society for Precision Engineering
• /
• v.16 no.12
• /
• pp.152-160
• /
• 1999

The weld quality of $CO_2$ arc welding is closely related to the arc stability. As the characteristics of the arc are excessively complex and nonlinear, it is not easy to make the arc model as mathematical form and to control the arc state to be stabilized. This paper was aimed to estimate the arc stability and to control for stabilizing the arc state in short circuit metal transfer mode of $CO_2$ arc welding. For these purposes, the behaviors of arc stability was investigated at different welding conditions using Mita's arc stability index, and the fuzzy control algorithm which uses the arc stability index as control imput and the arc voltage as control output was developed. In the control of the arc stability, the experiments of two cases were performed; the case of setting an initial welding voltage arbitrarily, the case of the step change in workpiece shape. Obtained results were as follows; Mita's arc stability index was able to be estimated qualitatively in the case of using the inverter type welding power source and the control performance for stabilizing the arc status was excellent in the case of existing step change disturbance.