후판 압연에서 신경망 알고리즘을 이용한 스키 예측

Abstract

A series of finite element analyses of the rolling process were performed and a neural network algorithm was employed to calculate the amount of ski-effect for an arbitrary thickness of incoming material in the roll gap. Pilot hot plate rolling tests were also conducted to verify the usefulness of the finite element analyzes conducted in this study. In these experiments, plates with thicknesses varying from 25 to 65 mm were tested. In addition, a number of rolling reductions of up to 31% were examined. Finally, a number of circumferential upper and lower rolls were investigated. Experimental validations demonstrated that the neural network algorithm predicted the proper amount of ski when rolling conditions(material thickness, reduction ratio, roll velocity differential) changed arbitrarily.