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신경회로망과 유전자 알고리즘을 이용한 열연두께 정도 향상
손준식,김일수,최승갑,이덕만 한국공작기계학회 2002 한국공작기계학회 추계학술대회논문집 Vol.2002 No.-
In the face of global competition, the requirements for the continuously increasing productivity, flexibility and quality (dimensional accuracy, mechanical properties and surface properties) have imposed a major change on steel manufacturing industries. The automation of hot rolling process requires the developments of several mathematical models for simulation and quantitative description of the industrial operations involved. 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.
RP를 이용한 용접비드 형상예측 시스템 개발에 관한 연구
손준식,김일수,Yarlagadda, Prasad K.D.V.,박창언,성백섭,이진구,정호성 한국공작기계학회 2002 한국공작기계학회 춘계학술대회논문집 Vol.2002 No.-
Generally, the use of robots in manufacturing industry has been increased during the past decade. GMA (Gas Metal Arc) welding is an actively growing area and many new procedures have been developed for use with high strength alloys. One of the basic requirement for welding applications is to study 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 enables the prediction of bead geometry using Rapid Prototyping (RP) in order to employ the robotic GMA welding processes. This system developed using MATLAB/SIMULINK, could be effectively implemented not only for estimating bead geometry, but also employed to monitor and control the bead geometry in real time.
GMA용접에서 유전자 알고리즘을 이용한 비드높이 예측 모델 개발에 관한 연구
손준식,김일수,장경천,이동길 한국공작기계학회 2006 한국공작기계학회 추계학술대회논문집 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 열간 사상압연의 압연하중 예측에 관한 연구
손준식,이덕만,김일수,최승갑 한국공작기계학회 2003 한국공작기계학회 추계학술대회논문집 Vol.2003 No.-
A major concern at present is the simultaneous control of transverse thickness profile and flatness in the finishing stages of hot rolling process. The mathematical modeling of hot rolling process has long been recognized to be a desirable approach to investigate rolling operating practice and the design of mill equipment to improve productivity and quality. However, many factors make the mathematical analysis of the rolling process very complex and time-consuming. In order to overcome these problems and to obtain an accurate rolling force, the predicted model of rolling force using neural networks has widely been employed. In this paper, Radial Basis Function Network(RBFN) is applied to improve the accuracy of rolling force prediction in hot rolling mill. In order to verify and analysis the performance of applied neural network, the comparison with the measured rolling force and the predicted results using two different neural networks - RBFN, MLP, has respectively been carried out. The results obtained using RBFN neural network are much more accurate those obtained the MLP.
방사형기저함수망을 이용한 표면 비드폭 예측에 관한 연구
손준식,김인주,김일수,김학형 한국공작기계학회 2004 한국공작기계학회 추계학술대회논문집 Vol.2004 No.-
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.
On-line 학습 신경회로망을 이용한 열간 압연하중 예측
손준식,이덕만,김일수,최숭갑 한국공작기계학회 2003 한국공작기계학회 춘계학술대회논문집 Vol.2003 No.-
In the face of global competition, the requirements for the continuously increasing productivity, flexibility and quality(dimensional accuracy, mechanical properties and surface properties) have imposed a major change on steel manufacturing industries. Indeed, one of the keys to achieve this goal is the automation of the steel-making process using AI(Artificial Intelligence) techniques. The automation of hot rolling process requires the developments of several mathematical models for simulation and quantitative description of the industrial operations involved. In this paper, a on-line training neural network for both long-term learning and short-term learning was developed in order to improve the prediction of rolling force in hot rolling mill. This analysis shows that the predicted rolling force is very closed to the actual rolling force, and the thickness error of the strip is considerably reduced.