• 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.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.170-174
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• 2004

Despite the widespread use in the various manufacturing industries, the full automation of the robotic CO$_2$ welding has not yet been achieved partly because the mathematical model for the process parameters of a given welding task is not fully understood and quantified. Several mathematical models to control welding quality, productivity, microstructure and weld properties in arc welding processes have been studied. However, it is not an easy task to apply them to the various practical situations because the relationship between the process parameters and the bead geometry is non-linear and also they are usually dependent on the specific experimental results. Practically, it is difficult, but important to know how to establish a mathematical model that can predict the result of the actual welding process and how to select the optimum welding condition under a certain constraint. In this paper, an attempt has been made to develop an Radial basis function network model to predict the weld top-bead width as a function of key process parameters in the robotic CO$_2$ welding. and to compare the developed model and a simple neural network model using two different training algorithms in order to verify performance. of the developed model.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.66-70
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• 2005

The full automation of welding has not yet been achieved partly because the mathematical model for the process parameters of a given welding task is not fully understood and quantified. Several mathematical models to control welding quality, productivity, microstructure and weld properties in arc welding processes have been studied. However, it is not an easy task to apply them to the various practical situations because the relationship between the process parameters and the bead geometry is non-linear and also they are usually dependent on the specific experimental results. Practically, it is difficult, but important to know how to establish a mathematical model that can predict the result of the actual welding process and how to select the optimum welding condition under a certain constraint. In this paper, an attempt has been made to develop an neural network model to predict the weld top-bead height as a function of key process parameters in the welding. and to compare the developed model and a simple neural network model using two different training algorithms in order to select an optimal neural network model.

• Journal of Welding and Joining
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• v.32 no.1
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• pp.53-60
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• 2014

The automatic arc welding is generally accepted as the preferred joining technique and commonly chosen for assembly of large metal structures such as in areas of automotive, aircraft and shipbuilding due to its joint strength, reliability, and low cost compared to other joint processes. Recently, several mathematical models have been developed and studied for control and monitoring welding quality, productivity, microstructure and weld properties in arc welding processes. This study indicates the prediction of process parameters for the expected welding quality with accordance to the adaptive GTA welding process. Furthermore, the mathematical models is also develop to aid the selection of an optimal welding process as the generation of process controls to predict the bead geometry as a function output parameters in the GTA welding process. The developed models through this study showed comparatively excellent predicted results, and will extend to other welding processes to integrate an optimized system for the robotic welding process.

• Proceedings of the KWS Conference
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• 2009.11a
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• pp.25-25
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• 2009

종래의 위보기 자세에서 용접은 중력이 모재의 표면으로 향하고 있어 용융금속이 중력에 의해 표면방향으로 흘러내리게 되어 용접 실시가 불가능하였다. 이에 Shield Gas Force, Trailing Gas Force 그리고 Ahead Gas Force를 적절히 적용하여 Position Welding에서 중력으로 인해 Molten Metal이 처지는 문제를 극복하여 생산성 향상으로 연결할 수 있음을 선행 실험을 통해 확인하였으나 기존의 C(Convergent)형, CP(Convergent Divergent)형 및 P(Parrallel)형 가스 노즐은 용접조건에 따라 실드 가스의 소모량이 많고, 토출되는 실드가스력이 부족하여 용접시 볼록한 이면 비드 형성을 위한 용융 풀을 효과적으로 제어 할 수 없다. 따라서 본 연구에서는 동일량의 실드 가스 공급시 가스 노즐을 통해 토출되는 실드가스의 소모를 줄이고 실드가스력을 극대화하여 저가의 고생산성을 가진 친 환경 용접기술(Green welding)에 부합하는 CDP(Convergent Divergent Parrallel)형 가스 노즐을 제작하여 기존의 CP형 가스 노즐과 비교 분석하였다. 또한 Overhead Position에서의 비드형상제어와 Flat Position에서 방풍효과를 비교해 보았다. 그 결과 CDP Nozzle은 CP Nozzle보다 동일한 유량에서 풍속은 3.5배, 냉각능력은 1.5배, 가스압력은 6.25배로 우수한 성능을 확인할 수 있었고, Overhead Position에서 가스 유량을 동일하게 하여 용접하였을 때 CP Nozzle의 경우 오목한 이면비드가 나타났지만 CDP Nozzle의 경우 볼록하게 양호한 이면비드 형상이 나타났고, Flat Position에서의 방풍효과 비교실험에서 CDP Nozzle에서는 깊고 균일한 용입을 CP Nozzle에서는 불안정한 용입이 나타났는데 이는 CDP Nozzle의 경우 풍속에 의한 Arc Blow가 적게 발생하여 상대적으로 더 나은 용입을 확인하였다.

• Proceedings of the KWS Conference
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• 2001.05a
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• pp.65-68
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• 2001

The application of a feedback control system in robotic arc welding is becoming more and more demanding than ever before. This requirement arises from the fact that robotic arc welding process needs no manual operator to monitor and manipulate the process parameters and hence a means of controlling the quality of the robotic arc welding process becomes apparent. Arc force sensor employed in this research to monitor the bead geometry of the arc welding process, A relationship between the bead dimension and the arc force distributions was established. Experimental configuration for measurement of arc force was used to quantify the changes in the arc force distributions of the plate being welded. Arc force sensor mounted at the end of the robot wrist was employed to measure the arc force applied to the weld. The sensor information was the used to establish a relationship between welding current and arc force. Arc force sensor have shown to be on of the most sophisticated technique to monitor perturbations that occurred during arc welding process.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.75-80
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• 2008

This paper presents a vision based autonomous inspection system for welding quality control of a RV sinking seat. In order to overcome the precision error that arises from a visible inspection by an operator in the manufacturing process of a RV sinking seat, the machine vision based welding quality control system is proposed. It consists of the CMOS camera and the NI vision system. The geometry of the welding bead, which is the welding quality criteria, is measured by using the captured image with a median filter applied on it. The image processing software for the system was developed using the NI LabVIEW software. The proposed welding quality inspection system for RV sinking seat was verified using experimentation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.7
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• pp.1326-1331
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• 2006

Weld root gap is a important fact of a falling-off weld quality in various kind of weld defect. The welding quality can be controlled by monitoring important parameters, such as, the Arc voltage, welding current and welding speed during the welding process. Welding systems use either a vision sensor or an Arc sensor, both of which are unable to control these parameters directly. Therefore, it is difficult to obtain necessary bead geometry without automatically controlling the welding parameters through the sensors. In this paper we propose a novel approach using neural networks for detecting and monitoring of weld root gap and bead shape. Through experiments we demonstrate that the proposed system can be used for real welding processes. The results demonstrate that the system can efficiently estimate the weld bead shape and detect the welding defects.

• Journal of the Korean Society of Mechanical Technology
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• v.13 no.1
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• pp.17-22
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• 2011

As the quality of a weld joint is strongly influenced by process parameters 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. In this study, prepared by ${\Phi}1.6mm$ GMA welding of metal wire nose Advice jowelui 350A 600A grade level inverter welder and DAIHEN SCR's were carried out using welding. Welding conditions were 5.5m/min wire feed rate the welding current is rapidly transmit approximately 260A, welding voltage was about 30V. CTWD a 22mm, shielding gas was Ar 20L/min and the welding speed was a 240mm/min. Using data collected during welding equipment welding current and welding voltage waveform was analyzed by measuring the volume of the transition mode. Addition of $CaCO_3$ as a loss of the spread of the weld bead dilution rate decreased, suggesting that, GMA in the overlay welding bead shape control, dilution control and may be used as a welding flux is considered. Stabilizing effect of the arc by the Ca-containing $CaF_2$, $CaCO_3$, $CaMg(CO_3)_2$, respectively, welding flux 0.1wt.% added GMA welding and weld overlay were evaluated with dilution, $CaF_2$, and $CaMg(CO_3)_2$ added to the dilution of Seemed to increase.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.1553-1558
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• 2007

This paper presents a vision based autonomous inspection system for welding quality control of car sinking seat. In order to overcome the precision error that arises from a visible inspection by operator in the manufacturing process of a car sinking seat, this paper proposes the MVWQC (machine vision based welding quality control) system. This system consists of the CMOS camera and NI machine vision system. The image processing software for the system has been developed using the NI vision builder system. The geometry of welding bead, which is the welding quality criteria, is measured by using the captured image with median filter applied on it. Experiments have been performed to verify the proposed MVWQC of car sinking seat.