• Journal of Welding and Joining
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• v.16 no.5
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• pp.119-133
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• 1998

To implement full automation in pipe welding, it si most important to develop special sensors and their related systems which act like human operator when detecting irregular groove conditions. In this study, an automatic pipe Gas Metal Arc Welding (GMAW) system was proposed to full control pipe welding procedure with intelligent sensor systems. A five-axes manipulator was proposed for welding torch to automatically access to exact welding position when pipe size and welding angle were given. Pool status and torch position were measured by using a weld-pool image monitoring and processing technique in root-pass welding for weld seam tracking and weld pool control. To overcome the intensive arc light, pool image was captured at the instance of short circuit of welding power loop. Captured image was processed to determine weld pool shape. For weld seam tracking, the relative distance of a torch position from the pool center was calculated in the extracted pool shape to move torch just onto the groove center. To control penetration of root pas, gap was calculated in the extracted pool image, and then weld conditions were controlled for obtaining appropriate penetration. welding speed was determined with a fuzzy logic, and welding current and voltage were determined from a data base to correspond to the gap. For automatic fill-pass welding, the function of human operator of real time weld seam control can be substituted by a sensor system. In this study, an arc sensor system was proposed based on a fuzzy control logic. Using the proposed automatic system, root-pass welding of pipe which had gap variation was assured to be appropriately controlled in welding conditions and in torch position by showing sound welding result and good seam tracking capability. Fill-pass welding by the proposed system also showed very successful result by tracking along the offset welding line without any control of human operator.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.1028-1033
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• 1991

This paper describes the design of a neural network estimator to estimate weld pool sizes for on-line use of quality monitoring and control in GMA welding processes. The estimator utilizes surface temperatures measured at various points on the top surface of the weldment as its input. The main task of the neural net is to realize the mapping characteristics from the point temperatures to the weld pool sizes through training, A series of bead-on plate welding experiments were performed to assess the performance of the neural estimator.

• Laser Solutions
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• v.2 no.3
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• pp.19-31
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• 1999

This paper introduces the monitoring scheme of laser welding quality using neural network. The developed monitoring scheme detects light signal emitting from plasma formed above the weld pool with optic sensor and DSP-based signal processor, and analyzes to give a guidance about the weld quality. It can automatically detect defects of laser weld and further give an information about what kind of defects it is, specially partial penetration and porosity among the interior defects. Those could be detected only by naked eyes or X-ray after welding, which needs more processes and costs in mass production. The monitoring scheme extracts four feature vectors from signal processing results of optical measuring data. In order to classify pattern for extracted feature vectors and to decide defects, it uses single-layer neural network with perceptron learning. The monitoring result using only the first feature vector shows confidence rate in recognition of 90%($\pm$5) and decides whether normal status or defects status in real time.

• Proceedings of the KWS Conference
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• 2002.05a
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• pp.62-64
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• 2002

• Proceedings of the KWS Conference
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• 2001.05a
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• pp.303-304
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• 2001

• Proceedings of the KWS Conference
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• 1996.10a
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• pp.142-144
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• 1996

This paper discusses the application of infra-red thermography in monitoring the robotic arc welding process, and it's potential for weld bead dimension and seam tracking control. Thermal images illustrating weld pool formation dynamics and heat distribution phenomena are digitized and their characteristics are measured. At each sampling point the maximum depth of penetration is recorded together with additional information regarding weld bead placement in relation to the seam location. Deficiencies such as incomplete penetration and lack of side wall fusion are readily identified and can be remained during the process. The technique can help an increase in productivity and weld quality by minimizing the amount of post process rework and inspection efforts needed otherwise.

• Proceedings of the KWS Conference
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• 1994.10a
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• pp.111-114
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• 1994

This paper describes a method to measure a weldment surface temperature for estimating variations of the weld pool size in the gas metal arc(GMA) welding processes. An Infrared sensing system is designed to measure the radiation emitted from the top surface of the weldment, The interference effect of the electric arc to the measurement is rejected by detecting the low peaks of the noisy signal. An optimizing criterion, in which the correlation between the weld quality and the measured temperature is maximized, is also proposed to determine the optimal measurement location.

• 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 Welding and Joining
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• v.20 no.1
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• pp.62-68
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• 2002

This study investigated the feasibility of penetration depth measurement using infrared temperature sensing on the weld surface. The detection point was optimized by FEM analysis in the laser keyhole welding. The profile of the weld surface temperature was measured using infrared detector array. Surface temperature behind the weld pool is proportional or exponentially proportional to penetration depth and bead width. From the results, the monitoring device of surface temperature using infrared detector array was applicable fur real time penetration depth control.

• Journal of the Optical Society of Korea
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• v.7 no.3
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• pp.193-196
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• 2003

We have developed a laser welding monitoring system to monitor laser welding process conditions such as sample feed rate, laser focal position, and laser power. A 2 ㎾ Nd:YAG CW laser beam has been applied to the welding of a stainless steel plate (SUS306) to investigate the welding monitoring. Theradiation signal from the weld pool was guided back through the focusing optics and the laser delivery fiber, and measured by a photo detector. By changing the focus of the laser beam along the z-direction, the penetration depth of the welding material has been measured. That shows the penetration depth depends on the frequency fluctuations of the plume signals which can be used in welding quality control.