신경망을 이용한 Edger압연 크롭저감 연구

Crop Control by Using Neural Network in Edger Mill

  • 천명식 (포항산업과학연구원, 압연프로세스연구팀) ;
  • 장대섭 ((주)포항제철 후판부 후판기술팀) ;
  • 이준정 (포항산업과학연구원, 압연프로세스연구팀)
  • 발행 : 1999.08.01

초록

Crop minimization of the top and bottom ends of hot rolled plate, in a plate, in a plate mill, has been investigated. The existing model to determine the edging pattern at the finishing rolling pass was not reasonable to get high width accuracy and rolling yields. New models including width prediction have been formulated by using neural network model of back propagation learning algorithm and statistical analysis based on the actual production rolling data to give the optimal pattern for minimizing trimming loss. Using these models, at a given rolling condition of broadside pass and finishing pass and the permissible condition of width variation, it was possible to minimize crip at the top and bottom ends according to optimum procedure in plate mill. An application to improve the plan view pattern reduced width variation by 23% and crop length by 30% on average with an effective fishtail crop shape.

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