• Journal of Welding and Joining
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• v.17 no.2
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• pp.97-103
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• 1999

The dynamic resistance monitoring in primary circuit is one of the important issues. Because in-process and real time quality assurance of resistance spot weld is needed to increase the product reliability. In this study, new dynamic resistance detecting method is proposed as a practical manner of weld quality assurance using instantaneous current and voltage measured at the primary circuit. and also, various patterns of primary dynamic resistance curve are characterized with the macro photograph and the weldability lobe curve. It is found that the 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 information and eventually real time feedback control will be possible.

• 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.

• Proceedings of the KWS Conference
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• 1999.10a
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• pp.142-145
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• 1999

The dynamic resistance monitoring 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. Secondary dynamic resistance patterns, as a real manner, are hard to adapt those factors in real time and in-plant system. In the present study, a new dynamic resistance detecting method is presented as a practical manner of weld quality assurance at the primary circuit. By the correlation analysis, it is found that the primary dynamic resistance patterns are basically similar to those of the secondary. Various dynamic resistance indices are characterized with the primary curve. And quality of the weld, like the tensile shear strength, is estimated using adaptive neuro-fuzzy estimation system which is consisted of the Sugeno fuzzy algorithm. Through the fuzzy clustering and parameter optimization, real time weld quality assurance system with less efforts is proposed.

• Journal of Welding and Joining
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• v.18 no.3
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• pp.114-121
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• 2000

Since resistance spot welding has become one of the most popular sheet metal fabrication processes, a strong emphasis is being put on the quality of the welds. Throughout the years many quality estimation systems have been developed by many researchers to ensure weld quality. In this study, the process variables, which were monitored in the primary circuit of the welding machine, are used to estimate the weld quality with Hopfield neural network. The primary dynamic resistance is vectorized and stored as five patterns in the network. As the welding is done, the dynamic resistance patterns are recognized and the quality is estimated with the proposed method. Due to the primary process variables, it is possible to utilize this algorithms as an in-process real time quality monitoring system.

• Proceedings of the KWS Conference
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• 2000.04a
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• pp.303-306
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• 2000

Quality estimation of the weld has been one of the important issues in RSW which is a main process of the sheep metal fabrication in auto-body industry, It was well known that among the various welding process variables, dynamic resistance has a close relation with nugget formation. With this variable, it is possible to estimate the weld quality in real time. In this study, a new quality estimation algorithm is developed with the primary dynamic resistance measured at welding machine timer. For this, feature recognition method of Hopfield neural network is used. Primary resistance patterns are vectorized and classified with five patterns. The network trained by these patterns recognizes the dynamic resistance pattern and estimates the weld quality Because the process variable monitored at the primary circuit is used, it is possible to apply this system to real time application without any consideration of electrode wear or shunt effect.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.10 s.181
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• pp.2581-2588
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• 2000

In previous studies, the dynamic resistance, which was calculated by the process variables measured at the electrode of the welding machine, and the electrode displacement were used for quality exa mination. However, in-process usage of such systems is not effective in systems that include a welding gun attached to a robot. In order to overcome such problems, we obtained and used the process variables from the welding machine timer. This would allow us to estimate real time in -process weld quality. For quality estimation, the features were extracted as factors from the primary dynamic resistance patterns, which were measured in t he welding machine timer. The relationship between the indexes and nugget size of the welds was observed through the regression analysis. Using the analyzed factors, a regression model that could estimate nugget diameter was developed. Two regression equations of the model were suggested depending on the factors, and it was showed that the model developed by stepwise method was effective one for weld quality estimation. The developed estimation model was in good linearity with the nugget diameter obtained through the experimentation.

• Proceedings of the KWS Conference
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• 2002.10a
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• pp.330-335
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• 2002

Resistance spot welding is one of the most widely used processes in sheet metal fabrication. Quality assurance of welding has been important to increase the productivity. In this study, weld quality estimation using primary circuit dynamic resistance applied to the in-process real-time systems. For quality estimation, factors relating to quality were extracted from the dynamic resistance, measured in the timer. The relationship between these factors and weld quality was determined through a artificial neural network model. This method has the advantage over the conventional one, such as obtaining the quality information without the use of extra devices.

• Journal of Welding and Joining
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• v.20 no.6
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• pp.45-45
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• 2002

This study is a middle report on the development of intelligent spot welding monitoring technology applicable to the production line. An intelligent algorithm has been developed to predict the quality of welding in real time. We examined whether it is effective or not through the In-Line and the Off-Line tests. The purpose of the present study is to provide a reliable solution which can prevent welding defects in production site. In this study, the process variables, which were monitored in the primary circuit of the welding, are used to estimate the weld quality by Hidden Markov Model(HMM). The primary dynamic resistance patterns are recognized and the quality is estimated in probability method during the welding. We expect that the algorithm proposed in the present study is feasible to the applied in the production sites for the purpose of in-process real time quality monitoring of spot welding.

• Journal of Welding and Joining
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• v.20 no.6
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• pp.769-775
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• 2002

This study is a middle report on the development of intelligent spot welding monitoring technology applicable to the production line. An intelligent algorithm has been developed to predict the quality of welding in real time. We examined whether it is effective or not through the In-Line and the Off-Line tests. The purpose of the present study is to provide a reliable solution which can prevent welding defects in production site. In this study, the process variables, which were monitored in the primary circuit of the welding, are used to estimate the weld quality by Hidden Markov Model(HMM). The primary dynamic resistance patterns are recognized and the quality is estimated in probability method during the welding. We expect that the algorithm proposed in the present study is feasible to the applied in the production sites for the purpose of in-process real time quality monitoring of spot welding.

• Proceedings of the KWS Conference
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• 2001.10a
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• pp.184-186
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• 2001

Quality estimation of the weld has been one of the important issues in RSW which is a main process of the sheet metal fabrication in auto-body industry. It was well known that among the various welding process variables, dynamic resistance has a close relation with nugget formation. In this study, a new quality estimation algorithm is developed with the primary dynamic resistance measured at welding machine timer. For this, Back propagation algorithm of neural network is used.