• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.362-366
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• 1990

The resistance spot welding process has been extensively used for joining of sheet metals, which are subject to variation of many process variables. Many qualitative analyses of sampled process variables have been successfully attempted to achieve a uniform nugget size. In this paper, the electrode movement signal which is a good indicative of the nugget size was examined by introducing a mathematical model with four parameters. A neural network method was applied for the estimation of the nugget size by four parameters. The prediction by the neural network is in good agreement with the actual nugget size. The results are quite promising in that the qualitative estimation of the invisible nugget size can be achieved without destructive testing of the welds.

• Proceedings of the KWS Conference
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• 1992.10a
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• pp.91-95
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• 1992

The resistance spot welding process has been extensively used for joining of sheet metals, which are subject to variation of many process variables. Many qualitive analyses of sampled process variables have been attempted to predict nugget size. In this paper, dynamic resistance and electrode movement signal which is a good indicative of the nugget size was examined by introducing an artificial neural network estimator. An artificial neural feedforward network with back-propagation of error was applied for the estimation of the nugget size. The prediction by the neural network is in good agreement with the actual nugget size for resistance spot welding of galvanized steel. The results are quite promising in that the quantitative estimation of the invisible nugget size can be achieved without conventional destructive testing of welds.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.2
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• pp.393-406
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• 1993

In resistance spot welding process, size of molten nuggest have been utilized to assess the integrity of the weld quality. However real-time monitoring of the nugget size is an extremely difficult problem. This paper describes the design of an artificial neural networks(ANN) estimator to predict the nugget size for on-line use of weld quality monitoring. The main task of the ANN estimator is to realize the mapping characteristics from the sampled dynamic resistance signal to the actual negget size through training. The structure of the ANN estimator including the number of hidden layers and nodes in a layer is determined by an estimation error analysis. A series of welding experiments are performed to assess the performance of the ANN estimator. The results are quite promissing in that real-time estimation of the invisible nugget size can be achieved by analyzing the dynamic resistance signal without any conventional destructive testing of welds.

• International Journal of Automotive Technology
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• v.5 no.1
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• pp.55-59
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• 2004

Spot welding is a very important and useful technology in fabrication of thin sheet structures such as the parts in an automobile. However, because the fatigue strength of the spot welding point is considerably lower than that of the base metal due to stress concentration at the nugget edge, the nugget size must be estimated to evaluate a reasonable fatigue strength at a spot welded lap joint. So far, many investigators have experimentally studied the estimation of fatigue strengths of various spot weldments by using a destructive method. However, these destructive methods poses problems so testing of weldments by these methods are difficult. Furthermore, these methods cannot be applied to a real product, and are time and cost consuming, as well. Therefore, there has been a strong, continual demand for the development of a nondestructive method for estimating nugget size. In this study, the effective nugget size in spot weldments have been analyzed by using thermoelastic stress analysis adopting infrared thermography. Using the results of the temperature distribution obtained by analysis of the infared stress due to adiabatic heat expansion under sinusoidal wave stresses, the effective nugget size in spot welded specimens were estimated. To examine the evaluated effective nugget size in spot weldments, it was compared with the results of microstructure observation from a 5％ Nital etching test.

• Journal of Welding and Joining
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• v.11 no.2
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• pp.27-41
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• 1993

The estimation of nugget sizes was attempted by utilizing the artificial neural networks method. Artificial neural networks is a highly simplified model of the biological nervous system. Artificial neural networks is composed of a large number of elemental processors connected like biological neurons. Although the elemental processors have only simple computation functions, because they are connected massively, they can describe any complex functional relationship between an input-output pair in an autonomous manner. The electrode head movement signal, which is a good indicator of corresponding nugget size was determined by measuring the each test specimen. The sampled electrode movement data and the corresponding nugget sizes were fed into the artificial neural networks as input-output pairs to train the networks. In the training phase for the networks, the artificial neural networks constructs a fuctional relationship between the input-output pairs autonomusly by adjusting the set of weights. In the production(estimation) phase when new inputs are sampled and presented, the artificial neural networks produces appropriate outputs(the estimates of the nugget size) based upon the transfer characteristics learned during the training mode. Experimental verification of the proposed estimation method using artificial neural networks was done by actual destructive testing of welds. The predicted result by the artifficial neural networks were found to be in a good agreement with the actual nugget size. The results are quite promising in that the real-time estimation of the invisible nugget size can be achieved by analyzing the process variable without any conventional destructive testing of welds.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.301-306
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• 2003

Spot welding is frequently used for industrial purpose, such as automobile and aerospace industries and household appliances due to its high performance. In these day, robotization and systemization of welding process made it possible to produce more precise or smaller electric parts. And when it comes to welding of steel sheet, the size of nugget must be getting smaller. Therefore, welding conditions are limited to avoid defects, such as deformation, damage, weakening of joining area. In this research, the measurement of the nugget size by the nondestructive inspection has been conducted. As a result, the right estimation of the nugget size and void defects, the detection of corona bond near joining area, the selection of the optimum ultrasonic mode, and set up for ultrasonic inspection are studied. From the trustworthy solutions of nugget size estimated by results of measurement, the optimum inspection conditions depending upon the width of welding parts are determined as well.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.49-53
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• 2004

Thin spot welding is frequently used for industrial purpose, such as automobile and aerospace industries and household appliances due to its high performance. In these days, the systemization of welding process made it possible to produce more precise or smaller electric parts. And when it comes to welding of steel sheets, the size of nugget must be getting smaller. Therefore, welding conditions are limited to avoid defects, such as deformation, damage and weakening of joining area. In this research, thin spot welding establishes the welding conditions, and then the measurement of the nugget size by the nondestructive inspection has been conducted. As a result, the right estimation of the nugget size, the detection of corona bond near joining area, the selection of the optimum ultrasonic mode, and setup for ultrasonic inspection are studied. From the trustworthy solutions of the nugget size estimated by results of measurement, the optimum inspection conditions depending upon the width of welding parts are determined as well.

• Journal of the Korean Society for Nondestructive Testing
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• v.25 no.3
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• pp.209-214
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• 2005

Spot welding is the most widely used in automotive and aerospace industries. The quality of weld depends upon the size of nugget between the overlapped steel plates. Recently, the thickness of the steel plates is much thinner and hence, it introduces the smaller size of nugget. Therefore, it is necessary not only to develop the criterion to evaluate the quality of weld but also to obtain the optimal welding conditions for the better performance. In this paper, the steel plates, 0.5 mm through 1.5 mm thickness, have been spot welded at different welding conditions and the nugget sizes are examined by ultrasonic technique (C-scan type). The relationships between the nugget sizes and the weldability have been investigated. The result of ultrasonic technique shows the good agreement with that of the tensile test.

• Proceedings of the KWS Conference
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• 1993.05a
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• pp.115-119
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• 1993

Many qualitive analyses of sampled process variables have been attempted to predict nugget size in resistance spot welding process. In this paper, dynamic resistance and electrode movement signal which is a good indicative of the nugget size was examined by introducing an artificial neural network estimator. An artificial neural feedforward network with back-propagation of error was applied for the estimation of the nugget size. To assess the advantage of this method. results have been compared with conventional regression method.

• Steel and Composite Structures
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• v.18 no.2
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• pp.345-355
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• 2015

In this study, the effects of weld parameters on nugget size and tensile-shear strength of welding joint in electrical resistance spot welding of galvanized DP 600 steel sheets having 1.2 mm were investigated. Taguchi design method has been employed to examine the effects of five parameters of welding current, electrode pressure, welding time, clamping time and holding time by using the $L_{27}(5^3)$ orthogonal array. Results showed that the most effective parameters on tensile shear strength and the nugget size ratio (hn/dn) were found as welding current and welding time, whereas electrode pressure, clamping time and holding time were less effective factors. Max. 545 MPa strength was obtained through proposed optimum conditions by Taguchi technique.