• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 1997년도 특별강연 및 추계학술발표 개요집
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• pp.205-209
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• 1997

One of the major important tasks in the robotic $CO_2$ arc welding process is to understand how process variables affected bead geometry and to subsequently develop the mathematical models to predict the desired bead dimensions. Experiment results are compared to outputs obtained using a set of published formulae relating input variables to output parameters and also investigated process variables on bead geometry for robotic $CO_2$ arc welding process The university of results obtained using empirical equations taken from existing models provided to be limited in predicting experimental bead shapes.

• 한국기계가공학회지
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• 제20권7호
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• pp.105-112
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• 2021

Reduction of weld distortions and increase in productivity are some of the major goals of the shipbuilding industry. To address these issues, many researchers have attempted to apply new welding processes. In the shipbuilding industry, steel is the candidate material of choice owing to its good weldability. However, conventional welding techniques are not feasible for avoiding welding problems. Tip-rotating arc welding is one of the high-efficiency welding process that has several advantages, such as high welding speed, high melting rate, low heat input, and less distortion. The present study investigates the influence of the welding variables on the weld characteristics of tip-rotating arc welding. Welding was performed using EH36 as the base metal and SM-70s as the filler metal, which are widely used in shipbuilding. Basic experiments were conducted to understand the effects of the major welding variables, such as welding and tip-rotating speeds. The distortion and mechanical properties of the optimal welding conditions were used to evaluate the tip-rotating arc welding performance. Consequently, the feasibility of the tip-rotating arc welding process for joining steel components was investigated, so that the optimized welding conditions could be applied directly to ship body welding to enhance the quality of the welded joints.

• 품질경영학회지
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• 제48권1호
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• pp.69-86
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• 2020

Purpose: This study aims to determine the optimal conditions for the spot welding process that mechanically connects the case of a cylindrical secondary battery and the negative tab. Methods: We use 33 factorial design to derive the optimal conditions for the spot welding process. The pulling strength, the cross-sectional area of nugget, and the shock test life are selected as response variables, which can represent the resistance welding quality. The input variables are selected as the welding time, welding voltage, and pressure, which are the controllable factors in the spot welding process. Results: The main effects of welding time and welding voltage and the interaction effect of welding time and welding voltage are significant. Conclusion: The optimal conditions for the spot welding process to mechanically join the negative electrode tab of the cylindrical secondary battery and the battery case are developed. The result shows that the pulling strength is increased by 44% compared to before improvement under optimal conditions.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2000년도 추계학술대회 논문집
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• pp.699-702
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• 2000

The automation and control of robotic welding process is a very complex assignment because the system is affected by a number of variables which are very difficult to determine or predict in practice. Not only the optimization of the robotic welding process is considered from the point of view of the time and the cost of manufacturing. as well as quality of the weldment. the human factors of the production and many other factors must taken into consideration. hi order to determine the optimal parameters of robotic welding process, it is necessary to build a computer model representing all parameters influencing the welding process as well as the mutual dependence between them. This paper presents an approach to modeling the robotic welding process in which all parameters affecting the welding process are included using a neural network. A detailed analysis of the simulation results has been carried out to evaluate the proposed neural network model.

• International Journal of Korean Welding Society
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• 제1권2호
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• pp.36-44
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• 2001

To get the appropriate welding process variables, mathematical modeling in conjunction with many experiments is necessary to predict the magnitude of weld bead shape. Even though the experimental results are reliable, it has a difficulty in accurately predicting welding process variables for the desired weld bead shape because of nonlinear and complex characteristics of welding processes. The welding condition determined for the desired weld bead shape may cause the weld defect if the welding current/voltage/speed combination is improperly selected. In this study, the $2^{n-1}$ fractional factorial design method and correlation parameter were used to investigate the effect of the welding process variables on the fillet joint shape, and the multiple non-linear regression analysis was used for modeling the gas metal arc welding(GMAW）parameters of the fillet joint. Finally, a fuzzy rule-based method and a neural network method were proposed so that the complexity and non-linearity of arc welding phenomena could be effectively overcome. The performance of the proposed neuro-fuzzy system was evaluated through various experiments. The experimental results showed that the proposed neuro-fuzzy system could effectively check the welding conditions as to whether or not weld defects would occur, and also adjust the welding conditions to avoid these weld defects.

• 한국기계가공학회지
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• 제21권8호
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• pp.99-109
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• 2022

In this study, an electric resistance-heated surface friction spot-welding process was proposed and tested for the spot-welding ability of copper and aluminum dissimilar metal sheets using electric resistance heating and surface friction heating. This process has welding variables, such as the current value, energizing cycles, rotational speed, and friction time. The current value and energizing cycle can affect the resistance heat, and the rotational speed of the rotating pin and friction time influence frictional heat generation. Resistance heating before friction heating has a preheating effect on the Cu-Al contact interface and a positive effect on preventing friction heat loss during the friction stage. However, because resistance preheating can soften the copper sheet and affect the contact stress and friction coefficient, it has difficulties that may adversely affect frictional heat generation. Therefore, the optimal combination of welding variables should be determined through simulations and experiments of the spot-welding process to determine the effects of electric resistance preheating on the suggested process. Through this procedure, it is known that the proposed spot-welding process can improve the welding quality during the spot welding of Cu-Al sheets.

• Journal of Welding and Joining
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• 제29권4호
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• pp.41-47
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• 2011

GMA welding process is a production process to improve productivity for the provision of higher welding quality of material. These includes numerous process variables that could affect welding quality, productivity and cost savings. Recently, the welding part of construction equipment had frequent failure of major components in the welding part of each subsidiary material due to shock which is very poor according to the welding part. Therefore, the implementation of sound welding procedure is the most decisive factor for the reliability of construction machinery. The data generated through experiments conducted in this study has validated its effectiveness for the optimization of bead geometry and process variables is presented. The criteria to control the process parameters, to achieve a good bead geometry. This study has developed mathematical models and algorithms to predict or control the bead geometry in GMA fillet welding process.

• Journal of Welding and Joining
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• 제32권2호
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• pp.43-47
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• 2014

Famous artificial neural network (ANN) is applied to predict proper process window of arc welding. Target weldment is variously combined lap joint fillet welding of automotive steel plates. ANN's system variable such as number of hidden layers, perceptrons and transfer function are carefully selected through case by case test. Input variables are welding condition and steel plate combination, for example, welding machine type, shield gas composition, current, speed and strength, thickness of base material. The number of each input variable referred in welding experiment is counted and provided to make it possible to presume the qualitative precision and limit of prediction. One of experimental process windows is excluded for predictability estimation and the rest are applied for neural network training. As expected from basic ANN theory, experimental condition composed of frequently referred input variables showed relatively more precise prediction while rarely referred set showed poorer result. As conclusion, application of ANN to arc welding process window derivation showed comparatively practical feasibility while it still needs more training for higher precision.

• 품질경영학회지
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• 제33권1호
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• pp.88-98
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• 2005

This paper considers a six sigma project for improving the TIP life time in a spot welding process. The project follows a disciplined process of five phases: define, measure, analyze, improve, and control. A process map is used to identify process input and output variables. Nine key process input variables are selected by using C&E matrix and FMEA, and finally four vital few input variables are selected from analyze phase. The optimum process conditions of the vital few input variables are jointly obtained by maximizing TIP life time using DOE and alternative selection method.

• 한국정밀공학회지
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• 제28권9호
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• pp.1054-1061
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• 2011

GMA welding process is a production process to improve productivity for the provision of higher quality of material, These includs numerous process variables that could affect welding quality, productivity and cost savings. Recently, the welding part of construction equipment had frequent failure of major components in the welding part of each subsidiary material due to shock which is very poor according to the welding part. Therefore, the implementation of sound welding procedure is the most decisive factor for the reliability of construction machinery. The data generated through experimens conducted in this study has validated its effectiveness for the optimization of bead geometry and process variables is presented. The criteria to control the process parameters, to achieve a healthy bead geometry. This study has developed mathematical models and algorithms to predict or control the bead geometry in GMA fillet welding process.