한국공작기계학회논문집 (Transactions of the Korean Society of Machine Tool Engineers)

한국생산제조학회 (The Korean Society of Manufacturing Technology Engineers)
권호 표지

On-line 학습 신경회로망을 이용한 열간 압연하중 예측

Prediction for Rolling Force in Hot-rolling Mill Using On-line learning Neural Network

  • 손준식 (목포대학교 기계공학과) ;
  • 이덕만 (포항제철 기술연구소) ;
  • 김일수 (목포대학교 기계/해양시스템공학부) ;
  • 최승갑 (포항제철 기술연구소)
  • 발행 : 2005.02.01
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초록

In the foe of global competition, the requirements for the continuously increasing productivity, flexibility and quality(dimensional accuracy, mechanical properties and surface properties) have imposed a mai or 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 teaming and short-term teaming 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.

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참고문헌

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