• Proceedings of the KWS Conference
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• 1998.10a
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• pp.129-132
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• 1998

The dynamic resistance monitoring in primary circuit or T/C is one of the important issues in that in-process and real time quality assurance of resistance spot weld is needed to increase the product reliability. It is well known that tile dynamic resistance curve gives us very useful information about nugget growth and weldability. In the present paper, a new dynamic resistance detecting method is presented as a practical manner of weld quality assurance using instantaneous current and voltage measured by primary circuit. Primary dynamic resistance patterns are basically similar to those of the secondary, but there is evident advantage such as no extra devices are needed to obtain the quality assurance index and eventually feedback control will be possible caused by T/C based monitoring system.

• Journal of Welding and Joining
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• v.23 no.4
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• pp.53-58
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• 2005

We designed a weld monitoring system with UV and IR sensors using a embedded DSP controller for implementing a distribution system; running stand alone and communication with outside by industrial standard protocols. Also this system provided a USB port in order to be acquiring data in PC. The user interface program in PC visualized the IR and W data in time, frequency and state space. A correlation of IR and UV signals showed closely related to weld quality. A rapid change of geometry can be found through a moving average filter. And the average value of IR signal at an interval represented a welding width and depth. Through these results, we proposed a monitoring algorithm for a integer type DSP.

• Journal of Welding and Joining
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• v.31 no.6
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• pp.90-95
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• 2013

Pipe inside clad welding is mainly used to the flow pipe of sub-sea or chemical plant. For the inside clad welding to the medium pipe with the diameter of about 12", TIG welding is frequently applied with filler metal. In this case, the clad welding has the very broad weld area over $10m^2$. And, the non-destructive test (NDT) such as ultrasonic test (UT) or radiographic testing (RT) should be conducted on the broad weld area, and it costs very high due to the time-consuming work. Therefore, the present study investigated the variation of arc voltage to develop the in-line quality monitoring system for the pipe inside TIG cladding. The 4 experimental parameters (current, arc length, wire feed position, and shield gas flow rate) varied to observe the change of arc voltage and to establish the model for the monitoring. The arc voltage was decreased when the wire was fed to the backward eccentric position(over 2mm), and the shield gas flow rate was insufficient under 10L/min. In the case of the backward eccentric position over 2mm, the bead appearance was not good and the dilution ratio was increased due to deep penetration. When the shield gas flow rate was lower than 10L/min, the bead surface was oxidized.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.345-348
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• 2008

Recently, in Weld Processing field, unmanned and automatic system construction has experienced the rapid growth, and diverse signal processing has been employed in order to translate the exact weld pattern. It this paper, We will suggest the effective neural network which can deride the weld quality in arc weld and monitoring system in real time. In addition, We will present the pre-processing for selecting the study data, and the method to evaluate the wave of weld more precisely and accurately through known Neural Network.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.2
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• pp.407-412
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• 2009

Recently, in Weld Processing field, unmanned and automatic system construction has experienced the rapid growth, and diverse signal processing has been employed in order to translate the exact weld pattern. In this paper, We will suggest the effective neural network which can decide the weld quality in arc weld and monitoring system in real time. In addition, We will present the pre-processing for selecting the study data, and the method to evaluate the wave of weld more precisely and accurately through known Neural Network.

• Journal of Welding and Joining
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• v.30 no.1
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• pp.27-32
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• 2012

• Proceedings of the KWS Conference
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• 2000.04a
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• pp.307-309
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• 2000

• Journal of Welding and Joining
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• v.18 no.2
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• pp.170-170
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• 2000

• Journal of Welding and Joining
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• v.18 no.2
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• pp.43-48
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• 2000

• Proceedings of the Korean Society of Laser Processing Conference
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• 2000.06a
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• pp.15-19
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• 2000