표면 비드높이 예측을 위한 최적의 신경회로망 선정에 관한 연구

손준식,김인주,김일수,장경천,이동길 한국공작기계학회 2005 한국공작기계학회 춘계학술대회논문집 Vol.2005 No.-

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.

Multi-Domain 방법을 이용한 TN-LCD의 광시야각 구현

김영식,김일두 단국대학교 1999 論文集 Vol.34 No.-

Multi-domain method has been used for broadening the viewing angle of TN-LCD which is generally showing an unsymmetry in the up and down side and a narrow viewing angle in the up side. We measured the orientational order and the T-V curve to find out the suitable rubbing strength of the multi-domain cell. The characteristics of the cell was estimated by measuring the viewing angle and the gray scale. The four-domain cell has a symmetrical wide viewing angle in every side and the gray inversion is not occurred in any side. The multi-domain cell is found to be useful in application of the gray scale.

연속냉간압연의 두께제어 모델 개발에 관한 연구

손준식,김일수,권욱현,최승갑,박철재,이덕만 한국공작기계학회 2001 한국생산제조학회지 Vol.10 No.5

The quality requirements for thickness accuracy in cold rolling continue to become more stringent, particularly in response to exacting design specification from automotive customers. One of the major impacts from the tighter tolerance level is more unusable product on the head end and tail end of tandem mill coils when the mill is in transition to or from steady state rolling condition. A strip thickness control system for a tandem cold steel rolling mills is composed with blocked non-interacting controller and controllers for strip thickness and tension control of each rolling stands. An intelligent mathematical model included an elastic deformation of strip has been developed and applied to the field in order to predict the rolling force. The simulated results showed that the effect of elastic recovery should be included the model, even if the effect of elastic compression was not important.

On-line 학습 신경회로망을 이용한 열간 압연하중 예측

손준식,이덕만,김일수,최승갑 한국공작기계학회 2005 한국생산제조학회지 Vol.14 No.1

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, an 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.

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.

경제위기하의 복지정책의 변화 : 영국의 경험 Experience in England

전일주,안강식 진주산업대학교 1998 論文集 Vol.37 No.-

This paper is to seek the wisdom about the direction and response of the welfare policy in Korea by examining closely the change of the English welfare policy in the past as history teaches the wisdom. The contents of this paper are to compare the ideologies, organizations, programs and finances of welfare in England before and after the economic crisis. First of all I can discover the distinguished changes in ideologise, organizations and programs of welfare and discover the constant and incremental change in terms of finances. Especially It is in contrast to the intention of the Thatcher government that it shows us constant and incremental change in the financial aspect. The case of the change of the welfare policy in England is not of general application in our welfare policy. However, There is any inkling. The economic crisis in England is attributable to the expanding welfare expenditures but our economic crisis has its origin in political corruption, undeveloped monetary situation and wasteful importation of foreign capital of the financial cliques, I think. Therefore, the method to deal successfully with the economic crisis is differs from country to country. We ought to not cutback the welfare expenditures. If not so, our level of welfare will go down more and more. We have to change the welfare policy drastically to emancipate the poverty class through the increase of welfare expenditures.

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.

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.

방사형기저함수망을 이용한 열간 사상압연의 압연하중 예측에 관한 연구

손준식,이덕만,김일수,최승갑 한국공작기계학회 2004 한국생산제조학회지 Vol.13 No.6

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 analyze 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.

용접비드 형상예측 시스템 개발에 관한 연구

김일수,손준식,박창언,서주환,장경천,이동길 朝鮮大學校 機械技術硏究所 2005 機械技術硏究 Vol.8 No.2

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