• Journal of Welding and Joining
• /
• v.17 no.5
• /
• pp.98-106
• /
• 1999

In GMAW, the spatters are generated according to the variation of the arc. Of the arc is stable, Few spatters are generated. But if unstable, too many spatters are generated. So, this means the spatters are dependent on the arc state. The aim of this study is to accurately estimate the arc state. To do this, the generated spatters were captured under the some welding conditions, and the waveforms of the arc voltage and welding current were collected. From the collected signals, the waveform factors and their standard deviations were extracted. Using these factors as input parameters of multi-layer artificial neural network, the learning for the weight of the generated spatters is performed and the estimation results to the real spatter are assessed. Obtained results are as follow: the linear correlation coefficient between the estimated result and the real spatters was 0.9986. And although the average convergence error was set 0.002, the estimated error to the real spatter was within 0.1 gr/min at each welding condition. In the estimation for the weight generated spatters, the result with multi-layer neural network was far better than with multiple regression analysis. Especially, even though under the welding condition which the arc state is unstable (the spatter is generated much more), very excellent estimation performance was shown.

• Journal of Welding and Joining
• /
• v.16 no.3
• /
• pp.95-101
• /
• 1998

Waveforms of $CO_2$ gas shielded arc welding in short circuit transfer mode was studied with the waveform analysis program, which can calculate various waveform factors such as number of short circuit event, mean and standard deviation of short circuit time and arc time. The calculated values of these factors were correlated independently or in combination with the spatter generation rate to figure out the most reliable index for evaluating spatter generation and further for arc stability. As a result this study, it was confirmed that the spatter generation tends to decrease with the increase of short circuit frequency. Further to this, it was also found that as the short circuit frequency increases the short circuit event becomes more uniform resulting in the decrease of standard deviations ($\sigma$values) of short circuit time and arc time. This result demonstrated that these factors are strongly correlated with each other and thus any one of these factors can be used for the evaluation index. In the discussion, however, short circuit frequency was proposed for the most practical index in evaluating the arc stability of short circuit transfer mode since it is the one which could be monitored in-process condition without any complex caculation process.

• Journal of Welding and Joining
• /
• v.17 no.4
• /
• pp.85-92
• /
• 1999

In GMAW, the spatter is generated because of the variation of the arc state. If the arc state is quantitatively assessed, the control method to make the spatter be reduced is able to develop. This study was attempted to develop the optimal model that could estimate the arc state quantitatively. To do this, the generated spatters was captured under the limited welding conditions, and the waveforms of the arc voltage and of the welding current were collected. From the collected waveforms, the waveform factors and their standard deviations were produced, and the linear and non-linear regression models constituted using the factors and their standard deviations are proposed to estimate the arc state. the performance test to the proposed models was practiced. Obtained results are as follow. From the results of correlation analysis between the factors and the amount of the generated spatters, the standard deviations of the waveform factors have more the multiple regression coefficients than the waveform factors. Because the correlation coefficient between T and {TEX}$T_{a}${/TEX}, and s[T] and s[{TEX}$T_{a}${/TEX}] was nearly one, it was found that these factors have the same effect to the spatter generation. In the regression models to estimate the arc state, it was fond that the linear and the non linear models were also consisted of similar factors. In addition, the linear regression model was assessed the optimal model for estimating the arc state because the variance of data was narrow and multiple regression coefficient was highest among the models. But in the welding conditions which the amount of the generated spatters were small, it was found that the non linear regression model had better the estimation performance for the spatter generation than the linear.

• Proceedings of the KWS Conference
• /
• 2000.10a
• /
• pp.238-239
• /
• 2000

• Journal of Welding and Joining
• /
• v.17 no.4
• /
• pp.16-21
• /
• 1999

• Proceedings of the KWS Conference
• /
• 1993.10a
• /
• pp.64-65
• /
• 1993

• Journal of Welding and Joining
• /
• v.16 no.3
• /
• pp.18-28
• /
• 1998

• Journal of Welding and Joining
• /
• v.21 no.6
• /
• pp.26-32
• /
• 2003

Arc welding process has indicated that it suffers from many flaws. It's because requirement of products is diverse and factors which affects the quality is also various. Therefore, in order to stabilize the welding process, it is important to choose a proper welding machine for the each process, and to evaluate the welding process capability of each machine. In this study, rational and simple index to evaluate the welding machine was set the coefficient of resistance variation through the arc stability examination such as spatter generation weight and bead configuration uniformity etc. And the method to evaluate the process capability index was developed by application of 6$\sigma$.

• Proceedings of the KWS Conference
• /
• 1999.10a
• /
• pp.74-77
• /
• 1999

• Journal of Welding and Joining
• /
• v.17 no.3
• /
• pp.36-43
• /
• 1999

The effect of Si content in welding wires on spattering characteristics and droplet transfer phenomena was studied. In MAG welding using 80% Ar-20% $CO_2$ shielding gas, spattering characteristics and droplet transfer phenomena were varied with Si content of wire. With increasing Si content, the spattering ratio and the ratio of large size spatter $(d\geq1.0mm)$ were increased. The increase of Si content in molten metal made surface tension increase due to reduction of oxygen content, which resulted from deoxidizing action of silicon. The increase of surface tension resulted in unstable transfer phenomena and arc instability in both short circuit and spray region. With changing Si content of wire, spattering characteristics and droplet transfer phenomena was directly influenced by the variation of surface tension, compared with the effect of arc stability.