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GMA용접에서 비드단면형상을 예측하기 위한 실험적 모델의 개발
손준식,김일수,박창언,김인주,정호성,Son Joon-Sik,Kim Ill-Soo,Park Chang-Eun,Kim In-Ju,Jeong Ho-Seong 대한용접접합학회 2005 대한용접·접합학회지 Vol.23 No.4
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.
손준식,김일수,김학형,Son, Joon-Sik,Kim, Ill-Soo,Kim, Hak-Hyoung 대한용접접합학회 2007 대한용접·접합학회지 Vol.25 No.6
Recently, several models to control weld quality, productivity and weld properties in arc welding process have been developed and applied. Also, the applied model to make effective use of the robotic GMA(Gas Metal Arc) welding process should be given a high degree of confidence in predicting the bead dimensions to accomplish the desired mechanical properties of the weldment. In this study, a development of the on-line learning neural network models that investigate interrelationships between welding parameters and bead width as well as apply for the on-line quality control system for the robotic GMA welding process has been carried out. The developed models showed an excellent predicted results comparing with the predicted ability using off-line learning neural network. Also, the system will extend to other welding process and the rule-based expert system which can be incorporated with integration of an optimized system for the robotic welding system.
GMA용접에서 유전자 알고리즘을 이용한 비드높이 예측 모델 개발에 관한 연구
손준식(Joon-Sik Son),김일수(Ill-Soo Kim),장경천(Kyeung-Cheun Jang),이동길(Dong-Gil Lee) 한국생산제조학회 2006 한국공작기계학회 춘계학술대회논문집 Vol.2006 No.-
Gas metal arc welding process has been chosen as a metal joining technique due to the wide range of usable applications, cheap consumables and easy handling. Three main indicators such as arc voltage, welding speed and welding current have a big influence in the quality welding. Since all these factors affect the quality of the welded joining parts, the effect of these parameters was investigated experimentally. In this paper, an attempt has been made to develop the predicted models (quadratic and cubic) for bead height using genetic algorithm. Performance of the developed models were proved to be compared to the regression equation.
방사형기저함수망을 이용한 표면 비드폭 예측에 관한 연구
손준식(Joon-Sik Son),김인주(In-Ju Kim),김일수(Ill-Soo Kim),김학형(Hak-Hyeng Kim) 한국생산제조학회 2004 한국생산제조시스템학회 학술발표대회 논문집 Vol.2004 No.10
Despite the widespread use in the various manufacturing industries, the full automation of the robotic CO₂ 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 Radial basis function network model to predict the weld top-bead width as a function of key process parameters in the robotic CO₂ welding. and to compare the developed model and a simple neural network model using two different training algorithms in order to verify performance. of the developed model.
신경회로망과 유전자 알고리즘을 이용한 열연두께 정도 향상
손준식(Joon-Sik Son),김일수(Ill-Soo Kim),이덕만(Duk-Man Lee),권영섭(Yeong-Seob Kueon) 한국생산제조학회 2006 한국생산제조학회지 Vol.15 No.5
The automation of hot rolling process requires the developments of several mathematical models for simulation and quantitative description of the industrial operations involved in order to achieve the continuously increasing productivity, flexibility and quality(dimensional accuracy, mechanical properties and surface properties). The mathematical modeling of hot rolling process has long been recognized to be a desirable approach to investigate rolling operating practice and design of mill requirement. To achieve this objectives, a new learning method with neural network to improve the accuracy of rolling force prediction in hot rolling mill is developed. Also, Genetic Algorithm(GA) is applied to select the optimal structure of the neural network and compared with that of engineers experience. It is shown from this research that both structure selection methods can lead to similar results.
표면 비드높이 예측을 위한 최적의 신경회로망 선정에 관한 연구
손준식(Joon-Sik Son),김인주(In-Ju Kim),김일수(Ill-Soo Kim),장경천(Kyeung-Cheun Jang),이동길(Dong-Gil Lee) 한국생산제조학회 2005 한국생산제조시스템학회 학술발표대회 논문집 Vol.2005 No.5
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.
손준식(Joon-Sik Son),전광석(Kwang-Suk Chon),김일수(Ill-Soo Kim),서주환(Joo-Hwan Seo),장경천(Kyeung-Cheun Jang) 한국생산제조학회 2005 한국공작기계학회 추계학술대회논문집 Vol.2005 No.-
The robotic CO₂ welding is widely employed in the fabrication industry for increasing productivity and enhancing product quality by its high processing speed, accuracy and repeatability. Reprogramming techniques have proved to be inadequate in taking into consideration of the component distortion due to heat imperfections during the welding process. 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 Infrared camera in sensing and control of the bead geometry in the automated welding process are presented. Bead width and isotherm radii can be expressed in terms of process parameters using mathematical equations obtained by empirical analysis using infrared camera.