• Journal of Welding and Joining
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• v.15 no.3
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• pp.118-127
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• 1997

With the trend towards welding automation and robotization, mathematical models for studying the influence of various variables on the weld bead geometry in gas metal arc (GMA) welding process are required. Partial penetration, single-pass bead-on-plate welds using the GMA welding process were fabricated in 12mm mild steel plates employed four different process variables. Experimental results has been designed to investigate the analytical and empirical formulae, and develop mathematical equations for understanding the relationship between process variables and weld bead geometry. The relationships can be usefully employed not only for open loop process control, but also for adaptive control provided that dynamic sensing of process output is performed.

• Journal of Welding and Joining
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• v.15 no.6
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• pp.116-123
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• 1997

In GMA welding processes, bead contour and penetration patterns are criterion to estimate weld quality. Bead geometry is commonly defined with width, height and depth. When weaving is taken into account, selection of welding conditions is known to be difficult. Thus, empirical or trial-and-error method are usually introduced. This study examined the correlation of welding process variables including weaving parameters with bead geometry using srtificial neural networks(ANN). The main task of the Ann estimator is to realize the mapping characteristics from the sampled welding process variables to the actual bead geometry through training. After the neural network model is constructed, welding process variables for desired bead geometry is selected by inverse model. Experimental varification of the inverse model is conducted through actual welding.

• International Journal of Korean Welding Society
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• v.3 no.2
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• pp.15-23
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• 2003

A genetic algorithm was applied to an arc welding process to determine near optimal settings of welding process parameters which produce good weld quality. This method searches for optimal settings of welding parameters through systematic experiments without a model between input and output variables. It has an advantage of being able to find optimal conditions with a fewer number of experiments than conventional full factorial design. A genetic algorithm was applied to optimization of weld bead geometry. In the optimization problem, the input variables were wire feed rate, welding voltage, and welding speed, root opening and the output variables were bead height, bead width, penetration and back bead width. The number of level for each input variable is 8, 16, 8 and 3, respectively. Therefore, according to the conventional full factorial design, in order to find the optimal welding conditions, 3,072 experiments must be performed. The genetic algorithm, however, found the near optimal welding conditions from less than 48 experiments.

• Journal of Welding and Joining
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• v.18 no.5
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• pp.63-69
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• 2000

A genetic algorithm was applied to an arc welding process to determine near optimal settings of welding process parameters which produce good weld quality. This method searches for optimal settings of welding parameters through systematic experiments without a model between input and output variables. It has an advantage of being able to find optimal conditions with a fewer number of experiments than conventional full factorial design. A genetic algorithm was applied to optimization of weld bead geometry. In the optimization problem, the input variables was wire feed rate, welding voltage, and welding speed and the output variables were bead height, bead width, and penetration. The number of level for each input variable is 16, 16, and 8, respectively. Therefore, according to the conventional full factorial design, in order to find the optimal welding conditions, 2048 experiments must be performed. The genetic algorithm, however, found the near optimal welding conditions from less than 40 experiments.

• International Journal of Korean Welding Society
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• v.1 no.1
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• pp.44-50
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• 2001

A genetic algorithm was applied to the arc welding process as to determine the near-optimal settings of welding process parameters that produce the good weld quality. This method searches for optimal settings of welding parameters through the systematic experiments without the need for a model between the input and output variables. It has an advantage of being capable to find the optimal conditions with a fewer number of experiments rather than conventional full factorial designs. A genetic algorithm was applied to the optimization of the weld bead geometry. In the optimization problem, the input variables were wire feed rate, welding voltage, and welding speed. The output variables were the bead height bead width, and penetration. The number of levels for each input variable is 16, 16, and 8, respectively. Therefore, according to the conventional full factorial design, in order to find the optimal welding conditions,2048 experiments must be performed. The genetic algorithm, however, found the near optimal welding conditions in less than 40 experiments.

• Journal of Welding and Joining
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• v.26 no.2
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• pp.85-91
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• 2008

In general, the quality of a welding process tends to vary with depending on the work environment or external disturbances. Hence, in order to achieve the desirable quality of welding, we should have the optimal welding condition that is not significantly affected by these changes in the environment or external disturbances. In this study, we used a dual response surface method in consideration of both the mean output variables and the standard deviation in order to optimize the V-groove arc welding process. The input variables for GMA welding process with the dual response surface are welding voltage, welding current and welding speed. The output variables are the welding quality function using the shape factor of bead geometry. First, we performed welding experiment on the interested area according to the central composite design. From the results obtained, we derived the regression model on the mean and standard deviation between the input and output variables of the welding process and then obtained the dual response surface. Finally, using the grid search method, we obtained the input variables that minimize the object function which led to the optimal V-groove arc welding process.

• International Journal of Korean Welding Society
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• v.3 no.2
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• pp.24-28
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• 2003

This study was aimed to evaluate the quality of flash welded joints and optimize the welding process for flash butt welding of 780MPa grade high strength steel. And then the relationship between the welding process variables and the joint quality would be established. The effect of process variables between flashing and upsetting process was elucidated. Microstructure observation of the joint indicated that the decarburized band was mainly changed with upsetting process. Width of HAZ was also related to the upsetting conditions rather than the flashing conditions. Generally maximum hardness at HAZ was correlated with Ceq of steel and the empirical relationship was obtained to estimate the HAZ properties. Tensile elongation at the joint was usually decreased with increasing the initial clamping distance. Investigation of fracture surface after tensile and bending tests reveal that the origin of cracking at the joint was oxide inclusions composed of $SiO_2$, MnO, $Al_2O_3$, and/or FeO. The amount of inclusions was dependent on the composition ratio of Mn/Si in steel. If this ratio was above 4, the amount of inclusions was low and then the resistance to cracking at the joint was enough to maintain the joint performance. It was obtained that the flashing process influenced the conditions for the energy input to establish uniform or non­uniform molten layer, while the upsetting conditions influenced the joint strength. Heat input variable during flashing process was also discussed with the joint properties.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.04a
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• pp.36-41
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• 1997

With the trend towards welding automation and robozation, mathematical models for studying the influence of various parameters on the weld bead geometry in Gas Metal Arc(GMA) welding process are required. The results of bead on plate welds deposited using the GMA welding process has enabled mathematical relationships to be developed that model the weld bead geometry. Experimental results were compared to outputs obtained using existing formulae that correlate process input variables to output parameters and subsequent modelling was performed in order to better predict the output of the GMA welding process. The aim of this work was to explain the relationships between GMA welding variables and weld bead geometry and thus, be able to predict input weld bead size. The relationships can be usefully employed for open loop process control and also for adaptive control provided that dynamic sensing of process output is performed.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.754-758
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• 2005

Optimization of process variables such as arc current, welding voltage and welding speed in terms of the weld characteristics desired is the key step in achieving high quality and improving performance characteristics without increasing the cost. Consequently, incorrect settings of those process variables give rise to deviations in the welding characteristics from the desired bead geometry. Therefore, trainee welders are referred to the tabulated information relating different metal types and thickness as to recommend the desired values of process variables. Basically, the bead geometry plays an important role in determining the mechanical properties of the weld. So that it is very important to select the process variables for obtaining optimal bead geometry. However, it is difficult for the traditional identification methods to provide an accurate model because the optimized welding process is non-linear and time-dependent. In this paper, the possibilities of the Infra-red sensor in sensing and control of the bead geometry in the automated welding process are presented. Infra-red sensor is a well-known method to deal with the problems with a high degree of fuzziness so that the sensor is employed to build the relationship between process variables and the quality characteristic the proposed above respectively. Based on several neural networks, the mathematical models are derived from extensive experiments with different welding parameters and complex geometrical features. The developed system enables to select the optimal welding parameters and control the desired weld dimensions during arc welding process.

• Journal of Power System Engineering
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• v.18 no.2
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• pp.50-56
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• 2014

Friction welding is a very useful joining process to weld metals which have axially symmetric cross section. In this paper, for the friction welding with tube-to-tube shape, the feasibility of industry application was determined using analyzing mechanical properties of weld and optimized welding variables was suggested. In order to accomplish this object, rotating speed, friction heating pressure, and friction heating time were selected as the major process variables and the experiment was performed in three levels of each parameter. Weld characteristic was investigated in terms of weld shape and metal loss, and 7mm of metal loss was regarded as the optimal metal loss. By tensile test, tensile strength and yielding strength was measured and fracture was occurred at base metal. In order to optimize the welding condition, fitness function was defined with respect to metal loss and yielding strength and the fitness values for each welding condition could be calculated in experimental range. Consequently, we set the optimal welding condition as the point which had maximum value of fitness function. As the result of this paper the optimal welding variables could be suggested as rotating speed was 1300 rpm, friction heating pressure was 15 MPa, and friction heating time was 10 sec.