• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1404-1407
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• 1996

A quality monitoring system in butt welding process is proposed to estimate weld pool sizes. The geometrical parameters of the weld pool such as the top bead width and the penetration depth plus half back width are utilized to prove the integrity of the weld quality. The monitoring variables used are the surface temperatures measured at three points on the top surface of the weldment. The temperature profile is assumed that it has a gaussian distribution in vertical direction of torch movement and verify this assumption through temperature analysis. A neural network estimator is designed to estimate weld pool size from temperature informations. The experimental results show that the proposed neural network estimator which used gaussian distribution as temperature information can estimate the weld pool sizes accurately than used three point temperatures as temperature information. Considering the change of gap size in butt welding, the experiment were performed on various gap size.

• Journal of Welding and Joining
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• v.21 no.1
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• pp.80-86
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• 2003

This study develops a system for effective prediction of residual stresses by the backpropagation algorithm using the neural network. To achieve this goal, a series of experiments were carried out to and measured the residual stresses using the sectional method. With the experimental results, the optional control algorithms using a neural network could be developed in order to reduce the effect of the external disturbances during GMA welding processes. Then the results obtained from this study were compared between the measured and calculated results, weld guality might be controlled by the neural network based on backpropagation algorithm.. This system can not only help to understand the interaction between the process parameters and residual stress, but also improve the quantity control for welded structures.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.6
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• pp.860-864
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• 2012

The contact tip is higher the wear of resistance and the longer life are demanded to GMA welding process. In this study, four different contact tips with three different compositions by two wires were evaluated their wear resistance by measuring in every one hour the area of enlarged hole at the exit side during actual wleding. 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. In addition, flux cored wire is excellent abrasion resistance test results showed. Based on these results, the domestic industry, the life of the contact tip to know will be used as basic data.

• Journal of Welding and Joining
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• v.27 no.5
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• pp.62-67
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• 2009

Welding deformation during the assembly process is affected by not only local shrinkage due to rapid heating and cooling, but also root gap and misalignment between parts to be welded. Therefore, the prediction and control of welding deformation have become of critical importance. In this study, it was focused on the development of the 3-axis apparatus for real-time measurement of the welded deformation. To achieve the objective, a D-H algorithm has been carried out to check the behavioral and performance evaluation for the developed robot. The sequence experiments were taken the base materials of $400{\times}200{\times}4.5mm$ plate for butt welding. The real-time experimental measurements are in good agreement with the measured results.

• 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.

• Journal of the Korean Society of Safety
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• v.16 no.4
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• pp.58-64
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• 2001

In this study, it is developed that the system for effective prediction of residual stresses by the back-propagation algorithm using the neural network. To achieve This goal, the series experiment were carried out and measured the residual stresses using the sectional method. Using the experimental results, the optional control algorithms using a neural network should be developed in order to reduce the effect of the external disturbances during GMA welding processes. Then the results obtained from this study were compared between the measured and calculated results, weld guality might be controlled by the neural network based on backpropagation algorithm. This system can no only help to understand the interaction between the process parameters and residual stress, but also improve the quantity control for welded structures.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.04a
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• pp.208-213
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• 2001

Objective of this paper is to develop a new approach involving the use of an Artificial Neural Network(ANN) and multiple regression methods in the prediction of process parameters on bead height for GMA welding process. Using a series of robotic are welding, multi-pass butt welds carried out in order to verify the performance of the neural network estimator and multiple regression methods. To verify the developed system, the design parameters of the neural network estimator are selected from an estimation error analysis. The experimental results show that the proposed models can predict the bead height with reasonable accuracy and guarantee the uniform weld quality.

• Journal of Welding and Joining
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• v.4 no.3
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• pp.58-71
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• 1986

Mechanical and corrosion property of duplex stainless steel weldments made by the GTAW and SMAW process were studied. Fracture toughness, general and local corrosion resistance of GTAW and SMAW weldments were evaluated in terms of Charpy V notch impact test, anodic polarization diagram, pitting corrosion rate, respectively. SMA weld metal showed much lower impact toughness and higher ductile-brittle transition temperature than GTA weld metal. Fractographic and EDX analysis on fracture surface of SMA weld metal demonstrated the existence of (Si, Ti), oxide in large amounts. Potentiodynamic anodic polarization diagram of GMA weld metal showed much lower passive current density than SMA weld metal in 4% $H_2/SO_4$ solution. And pitting corrosion rate test showed the same tendency. Relating the microstructure, chemistry and property, it can be concluded that GTA weld metal gives better toughness due to lower oxygen content, i.e. lower inclusion content, and better corrosion resistance due to higher Pitting Index(PI) than SMA weld metal.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.484-490
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• 2000

The abjective of the study is the development of the system for effective prediction of residual stresses using the backpropagation algorithm from the neural network. To achieve this goal, the series experiment were carried out and measured the residual stresses using the sectional method. Using the experimental results, the optional control algorithms using a neural network should be developed in order to reduce the effect of the external disturbances on during GMA welding processes. Then the results obtained from this study were compared between the measured and calculated results, the neural network based on backpropagation algorithm might be controlled weld quality. This system can not only help to understand the interaction between the process parameters and residual stress, but also improve the quantity control for welded structures.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.5
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• pp.70-75
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• 1999

Adaptive control in the robotic GMA(Gas Metal Arc) welding is employed to monitor the information about weld characteristics and process paramters as well as modification of those parameters to hold weld quality within the acceptable limits. Typical characteristics are the bead geometry composition micrrostructure appearance and process parameters which govern the quality of the final weld. The main objectives of this paper are to realize the mapping characteristicso f penetration through the learning. After learning the neural network can predict the pene-traition desired from 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) were chosen from an error analysis. partial-penetration single-pass bead-on-plate welds were fabricated in 12mm mild steel plates in order to verify the performance of the neural network estimator. The experimental results show that the proposed neural network estimator can predict the penetration with reasonable accuracy and gurarantee the uniform weld quality.