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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.
손준식(Joon-Sik Son),전광석(Kwang-Suk Chon),김일수(Ill-Soo Kim),서주환(Joo-Hwan Seo),장경천(Kyeung-Cheun Jang) 한국생산제조학회 2005 한국생산제조시스템학회 학술발표대회 논문집 Vol.2005 No.10
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.
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.
손준식(Son Junsik) 중국근현대사학회 2015 중국근현대사연구 Vol.66 No.-
After the withdrawal of the Kuomintang government to Taiwan, the United States began to reverse its hands-off policy, and tried to ensure the safety of Taiwan through military and economic aid. It aimed to stabilize Taiwan’s economy in order to prevent the expansion of communist forces in the wake of the Korean War. The United States, which recognized the need for the Taiwan’s self-sustained economic development, planned to help in expanding the education sector and reforming the vocational education system to develop the workforce. The goal of the United States was to produce practical industrial technicians through the ‘Unit Trade Training System’ and efficient rural farmers through the agricultural vocational education. However, this goal was compromised due to the educational philosophy of the Kuomintang government, the education system of Taiwan, and the traditional view on occupations. Eventually, the American agricultural education system, which was divorced from the Taiwanese reality, was discarded. Nevertheless, U.S. aid was used to cultivate students’ practical skills in order to solve the unemployment problem. In addition, the expansion of teacher training and facilities provided the technical work force necessary for Taiwan’s economic development. Meanwhile, aid from the United States in the form of overseas Chinese education aimed to increase anti-communist sentiments and to restrain communism in Southeast Asia. This policy coincided with efforts by the Kuomintang government to be recognized as the legitimate regime, as the Kuomintang and the Chinese Communist Party were quarreling about which should get the overseas Chinese. Because most of the overseas Chinese aid was used for school buildings and facilities and their travel and cost of living, Taiwan and the United States did not disagree on curriculum and content. Overseas Chinese who received their higher education in Taiwan played a pivotal role in state building in their countries and in the improvement of relationships between Taiwan and their countries. However, their contribution to enhancing democracy, which the United States expected, remains unclear. Even though U.S. educational aid did not achieve its purpose, the aid trained the workforce, which was needed for economic development, and provided an environment, in which the government could obtain the support of the Chinese community. The Kuomintang government, which desperately needed U.S. aid, made an effort to accept the U.S. proposals as far as possible without its sovereignty, and on the basis of the reality in Taiwan.
표면 비드높이 예측을 위한 최적의 신경회로망의 적용에 관한 연구
손준식(J. S. Son),김일수(I. S. Kim),박창언(C. E. Park),김인주(I. J. Kim),김학형(H. H Kim),서주환(J. H. Seo),심지연(J. Y. Shim) 한국생산제조학회 2007 한국생산제조학회지 Vol.16 No.4
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.
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.