• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.2
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• pp.310-316
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• 2003

One of the significant subject in the automatic arc welding is to establish control system of the welding parameters for controlling bead geometry as a criterion to evaluate the quality of arc welding. This paper proposes an inference algorithm for bead geometry in CMA Welding using Neuro-Fuzzy algorithm. The characteristic welding parameters are measured by the circuit composed of hall sensor, voltage divider tachometer, etc. and then the bead geometry of each weld pool is calculated and detected by an image processing with CCD camera and a measuring with microscope. The relationships between the characteristic welding parameters and the bead geometry have been arranged empirically. From the result of experiments, membership functions and fuzzy rules are tuned and determined by the learning of neural network, and then the relationship between actual bead geometry and inferred bead geometry are concluded by fuzzy logic controller. In the applied inference system of bead geometry using Neuro-Fuzzy algorithm, the inference error percent is within -5%∼+4% in case of bead width, -10%∼+10% in bead height, -5%∼+6% in bead area, -10%∼+10% in penetration. Use of the Neuro-Fuzzy algorithm allows the CMA Welding system to evaluate the quality in bead geometry in real time as the welding parameters change.

• Journal of Welding and Joining
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• v.17 no.4
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• pp.39-45
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• 1999

The procedure of robotic Gas metal Arc (GMA) welding in order to achieve the optimized bead geometry needs the selection of suitable process parameters such as arc current, welding voltage, welding speed. It is required the relationships between process parameters and bead geometry. The objective of this paper is to develop the algorithm that enables the determination of process parameters from the optimized bead geometry for robotic GMA welding. It depends on the inversion of empirical equations derived from multiple regression analysis of the relationships between the process parameters and the bead dimensions using the least square method. The method not only directly determines those parameters which will give the desired set of bead geometry, but also avoids the need to iterate with a succession of guesses employed Finite Element Method(FEM). These results suggest that process parameter from experimental equation for robotic GMA welding may be employed to monitor and control the bead geometry in real time.

• 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 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.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.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.5
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• pp.84-89
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• 2007

This research was done on the basis of assumption that there is a relationship between welding parameters and geometry of the back-bead being a gap in arc welding. Multiple regression analysis was used as method for predicting the geometry of the back-bead. The analysis data and the verification data were used for the formation of multiple regression analysis. The method was used to perform the prediction of the back-bead.

• 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.

• Proceedings of the KWS Conference
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• 2003.11a
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• pp.169-171
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• 2003

In this paper, the prediction for the optimized bead geometry such as bead width, height, penetration and bead area in the Gas Metal Arc (GMA) welding with Taguchi method is presented. An orthogonal array, and the Signal-to-Noise (S/N) ratio employed to investigate the welding quality characteristics together in the selection of process parameters in the GMA welding process, to analyze the effect of each process parameter on the bead geometry and to finally determine the process parameters with the optimal bead geometry. Experimental results fi-om this research show that the Taguchi method provides an effective tool to enhance the accuracy of the optimized bead geometry.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.5
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• pp.30-35
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• 2008

As the quality of a weld feint is strongly influenced by process parameters during the welding process, an intelligent algorithms that can predict the bead geometry and shape to accomplish the desired mechanical properties of the weldment should be developed. This paper focuses on the development of mathematical models fur the selection of process parameters and the prediction of bead geometry(bead width, bead height and penetration) in robotic GMA(Gas Metal Arc) welding. Factorial design can be employed as a guide for optimization of process parameters. Three factors were incorporated into the factorial model: arc current, welding voltage and welding speed. A sensitivity analysis has been conducted and compared the relative impact of three process parameters on bead geometry in order to verify the measurement errors on the values of the uncertainty in estimated parameters. The results obtained show that developed mathematical models can be applied to estimate the effectiveness of process parameters for a given bead geometry, and a change of process parameters affects the bead width and bead height more strongly than penetration relatively.

• 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.