• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.4
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• pp.87-92
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• 2007

The full automation welding has not yet been achieved partly because the mathematical model for the process parameters of a given welding task is not fully understood and quantified. Several mathematical models to control welding quality, productivity, microstructure and weld properties in arc welding processes have been studied. However, it is not an easy task to apply them to the various practical situations because the relationship between the process parameters and the bead geometry is non-linear and also they are usually dependent on the specific experimental results. Practically, it is difficult, but important to know how to establish a mathematical model that can predict the result of the actual welding process and how to select the optimum welding condition under a certain constraint. In this paper, an attempt has been made to develop an neural network model to predict the weld top-bead height as a function of key process parameters in the welding. and to compare the developed models using three different training algorithms in order to select an adequate neural network model for prediction of top-bead height.

• Journal of Welding and Joining
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• v.31 no.1
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• pp.33-37
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• 2013

Robotized MIG welding of large aluminium components for high speed trains is state-of-the-art. The implementation of online laser cameras enables seam tracking and adoptive modification of welding parameters. A constant fill is achieved regardless of the gap tolerances. Friction Stir Welding has been introduced to the market as a reliable and fast joining technology. The advantages of high welding speeds and the elimination of arc light, fumes and liquefaction in the welding spot lead to economical realisation of heavy-duty gantry systems. FSW robots offer a high flexibility with regard to welding of curved parts, and can be equipped with laser cameras for exact joint tracking.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.5
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• pp.30-35
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• 2008

As the quality of a weld feint is strongly influenced by process parameters during the welding process, an intelligent algorithms that can predict the bead geometry and shape to accomplish the desired mechanical properties of the weldment should be developed. This paper focuses on the development of mathematical models fur the selection of process parameters and the prediction of bead geometry(bead width, bead height and penetration) in robotic GMA(Gas Metal Arc) welding. Factorial design can be employed as a guide for optimization of process parameters. Three factors were incorporated into the factorial model: arc current, welding voltage and welding speed. A sensitivity analysis has been conducted and compared the relative impact of three process parameters on bead geometry in order to verify the measurement errors on the values of the uncertainty in estimated parameters. The results obtained show that developed mathematical models can be applied to estimate the effectiveness of process parameters for a given bead geometry, and a change of process parameters affects the bead width and bead height more strongly than penetration relatively.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.2
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• pp.92-99
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• 2007

Welding fabrication invariantly involves three district sequential steps: preparation, actual process execution and post-weld inspection. One of the major problems in automating these steps and developing autonomous welding systems, is the lack of proper sensing strategies. Conventionally, machine vision is used in robotic arc welding only for the correction of pre-taught welding paths in single pass. In this paper, novel presented, developed vision processing techniques are detailed, and their application in welding fabrication is covered. The software for joint tracking system is finally proposed.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1845-1848
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• 2005

The automatic $CO_2$ welding is a manufacturing process to produce high quality joints for metal and it could provide a capability of full automation to enhance productivity. Despite the widespread use in the various manufacturing industries, the full automation of the robotic $CO_2$ welding has not yet been achieved partly because the mathematical model for the process parameters of a given welding task is not fully understood and quantified. Several mathematical models to control welding quality, productivity, microstructure and weld properties in arc welding processes have been studied. However, it is not an easy task to apply them to the various practical situations because the relationship between the process parameters and the bead geometry is non-linear and also they are usually dependent on the specific experimental results. Practically, it is difficult, but important to know how to establish a mathematical model that can predict the result of the actual welding process and how to select the optimum welding condition under a certain constraint. In this research, an attempt has been made to develop an intelligent algorithm to predict the weld geometry (top-bead width, top-bead height, back-bead width and back-bead height) as a function of key process parameters in the robotic $CO_2$welding. A sensitivity analysis has been conducted and compared the relative impact of three process parameters on bead geometry in order to verify the measurement errors on the values of the uncertainty in estimated parameters.

• Journal of Welding and Joining
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• v.23 no.4
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• pp.41-47
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• 2005

Generally, the use of robots in manufacturing industry has been increased during the past decade. GMA(Gas Metal Arc) welding process is an actively Vowing area, and many new procedures have been developed for use with high strength alloys. One of the basic requirement for the automatic welding applications is to investigate relationships between process parameters and bead geometry. The objective of this paper is to develop a new approach involving the use of neural network and multiple regression methods in the prediction of bead geometry for GMA welding process and to develop an intelligent system that visualize bead geometry in order to employ the robotic GMA welding processes. Examples of the simulation for GMA welding process are supplied to demonstrate and verify the proposed system developed using MATLAB. The developed system could be effectively implemented not oかy for estimating bead geometry, but also employed to monitor and control the bead geometry in real time.

• Journal of Welding and Joining
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• v.24 no.5
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• pp.37-42
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• 2006

The robotic arc welding is widely employed in the fabrication industry fer increasing productivity and enhancing product quality by its high processing speed, accuracy and repeatability. 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. In this paper, the possibilities of the Infrared camera in sensing and control of the bead geometry in the automated welding process are presented. Both bead width and thermal images from infrared thermography are effected by process parameters. Bead width and isotherm radii can be expressed in terms of process parameters(welding current and welding speed) using mathematical equations obtained by empirical analysis using infrared camera. A linear relationship exists between the isothermal radii producted during the welding process and bead width.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.66-70
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• 2005

The full automation of welding has not yet been achieved partly because the mathematical model for the process parameters of a given welding task is not fully understood and quantified. Several mathematical models to control welding quality, productivity, microstructure and weld properties in arc welding processes have been studied. However, it is not an easy task to apply them to the various practical situations because the relationship between the process parameters and the bead geometry is non-linear and also they are usually dependent on the specific experimental results. Practically, it is difficult, but important to know how to establish a mathematical model that can predict the result of the actual welding process and how to select the optimum welding condition under a certain constraint. In this paper, an attempt has been made to develop an neural network model to predict the weld top-bead height as a function of key process parameters in the welding. and to compare the developed model and a simple neural network model using two different training algorithms in order to select an optimal neural network model.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1992.04b
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• pp.91-101
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• 1992

본 연구에서는 용접선 추출의 방법으로 현재 많이 사용되고 있는 용접선을 따라 연속적으로 이미지를 얻어 처리하는 사전관찰(preview)기법을 개선하여 용접모재를 한번에 촬영(snapshot)하여 화상처리를 거친 후 용접정보가 들어 있는 CAD database와 비교, 매칭시켜 필요한 용접정보를 획득하는 새로운 방법을 제시한다. 또한 정확한 꼭지점을 추출하기 위해서는 정확한 직선식이 필요한데 이의 계산에는 허프변환(Hough Transform)이 이용되고 있지만 계산시간이 많이 소요되며 부정확하다. 계산시간의 감소 및 정확도의 향상을 위해 기존의 허프변환(Hough Transform)을 개선한 수정된 허프변환(Modified HoughTranform)을 개발하였다.

• Journal of Welding and Joining
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• v.14 no.1
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• pp.15-23
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• 1996

용접은 힘든 작업환경 때문에 심한 인력난을 겪고 있는 3D 업종 중의 하나이 다. 특히 아크용접을 위해서는 숙련된 작업자가 필요하기 때문에 인력난은 더욱 심각 하다. 현장에서 젊은 작업자는 보기 힘들고 대부분의 작업자는 중년이상의 나이이다. 따라서 머지않아 많은 업체들이 인력난으로 조업을 중단해야 할 사태도 우려하지 않을 수 없는 형편이다. 이러한 문제점들을 해결할 수 있는 용접자동화는 최근 많은 관심을 끌고 있으며 이제 세계적인 추세가 되었다. 용접자동화의 이점은 무엇보다도 일정한 수준의 품질과 생산성에서 찾아볼 수 있다. 용접자동화에서는 가공자동화와는 달리 시스템의 자율성이 특히 중요하다. 작업조건의 설정, 용접봉의 위치와 방향 등은 전적 으로 컴퓨터와 센서시스템을 장착한 로봇에 의해 결정되어야 한다. 따라서 작업자들이 용접에 이용하는 각종 지능적인 능력을 습득하고 이를 각종 제어기와 센서로 모방하여 자율적으로 제어하는 인공지능기술을 응용하는 용접을 로봇의 개발에 관한 각종 연구 가 활발히 진행되고 있다.