• 한국공작기계학회논문집
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• 제15권5호
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• pp.123-130
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• 2006

In manufacturing, process automation and parameter optimization are required in order to improve productivity. Especially in welding process, productivity and weldablity should be considered to determine the process parameter. In this paper, optimization methodology was proposed to determine the welding conditions using the objective function in terms of productivity and weldablity. In order to conduct this, welding experiments were carried out. Tensile test was performed to evaluate the weldability. Neural network model to estimate tensile strength using the laser power, welding speed, and wire feed rate was developed. Objective function was defined using the normalized tensile strength which represented the weldablilty and welding speed and wire feed rate which represented the productivity. The optimal welding parameters which maximized the objective function were determined.

• Journal of Welding and Joining
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• 제20권2호
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• pp.102-108
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• 2002

Resistance spot welding of aluminum alloys is based upon Joule heating of the components by passing a large current in a short duration. Since aluminum alloys have the potential to replace steels fur automobile body assemblies, it is important to study the process robustness of aluminum spot welding process. In order to evaluate the effects of process parameters on the weld quality, major process variables and abnormal process conditions were selected and analyzed. A newly developed two-stage, sliding-level experiment was adopted fur effective parameter design and analysis. Suitable ranges of welding current and button diameters were obtained through the experiment. The effects of the factors and their levels on the variation of acceptable welding current were considered in terms of main effects. From the results, it is concluded that any abnormal process condition decreases the suitable current range in the weld lobe curve. Pareto analysis of variance was also introduced to estimate the significant factors on the signal-to-noise (S/N) ratio. Among the six factors studied, fit-up condition is found to be the most significant factor influencing the SM ratio. Using a Pareto diagram, the optimal condition is determined and the SM ratio is significantly improved using the optimal condition.

• Journal of Welding and Joining
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• 제33권4호
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• pp.17-23
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• 2015

Laser-gas metal arc hybrid welding has been considered as an alternative process of gas metal arc welding for offshore pipe laying. Fiber delivered high power lasers which enable deep penetration welding were recently developed but high power welding characteristics were not fully understood yet. In this study, the influence of shielding gas composition on welding phenomena in high power laser welding was investigated. Bead shapes, melt ejection and dropping were observed after autogenous laser welding with 100% Ar, Ar-20% $CO_2$, Ar-50% $CO_2$, and 100% $CO_2$ shielding gas. Process parameter window was widest with Ar-50% $CO_2$ shielding gas and the penetration was deepest with 100% $CO_2$ shielding gas. The melt dropping was not observed when Ar-50% $CO_2$ or 100% $CO_2$ shielding gas was supplied.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2004년도 추계학술대회논문집
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• pp.301-304
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• 2004

Friction welding was used to weld the turbine wheel and shaft and have a good welding quality. Friction welding was conducted an the two dissimilar material, Nimonic 80A and SNCrW. The control of friction welding process parameter such as flywheel energy, interface temperature, amount of upset have an effect on the mechanical properties of the welded joint. FE simulation can be a useful tool to optimize the weld geometry and process parameters. Flash shape and thickness weld is consistent with the simulated results. Process analysis was performed by the commercial code DEFORM 2D. Mechanical property of weld joints was evaluated by microstructure, chemical component, tensile, impact, hardness test so on.

• 한국공작기계학회논문집
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• 제17권6호
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• pp.49-55
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• 2008

Arc welding process is one of the most important technologies to join metal plates. Robotic welding offers the reduced manufacturing cost sought, but its widespread use demands a means of sensing and correcting for inaccuracies in the part, the fixturing and the robot. A number of problems that need to be addressed in robotic arc welding processes include sensing, joint tracking, and lack of adequate models for process parameter prediction and quality control. Problems with parameter settings and quality control occur frequently in the GMA(Gas Metal Arc) welding process due to the large number of interactive process parameters that must be set and accurately controlled. The objectives of this paper are to realize the mapping characteristics of bead width using a sensitivity analysis and develop the neural network and multiple regression method, and finally select the most accurate model in order to control the weld quality(bead width) for fillet welding. The experimental results show that the proposed neural network estimator can predict bead width with reasonable accuracy, and guarantee the uniform weld quality.

• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 2000년도 특별강연 및 추계학술발표대회 개요집
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• pp.43-45
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• 2000

Resistance Spot Welding has been one of the important process in the sheet metal fabrication of auto-body industry It is well known that the nugget formation of RSW is the major factor for the strength of the body. A high speed camera was used to consider initial melting and growth of the weld nugget in order to find out the nugget formation mechanism. It was observed that such mechanism had an effect on the dynamic resistance, which was a process parameter of resistance spot welding. Also, the relationship between the mechanism and process parameter was considered for the industrial application.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1998년도 춘계학술대회 논문집
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• pp.274-280
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• 1998

The welding technology and qualities are developed significantly, in recent years, in the use of automated processing technology and welding robot systems. But these automated welding technologies have many difficulties for finding the optimal welding parameter conditions. Because of the lack of mathematical model for determination of optimal welding process parameters. In this study, the sensitivity analysis of the empirical equations for finding weld bead width, height and penetration depth by using the published formulae. The selected major welding process parameters effected to weld bead geometries are the welding speed, current, voltage and weld wire diameter.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2003년도 추계학술대회 논문요약집
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• pp.150-150
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• 2003

• International Journal of Korean Welding Society
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• 제3권2호
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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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• 제18권5호
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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.