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신경회로망과 유전자 알고리즘을 이용한 열연두께 정도 향상
손준식(Joon-Sik Son),김일수(Ill-Soo Kim),이덕만(Duk-Man Lee),권영섭(Yeong-Seob Kueon) 한국생산제조학회 2006 한국생산제조학회지 Vol.15 No.5
The automation of hot rolling process requires the developments of several mathematical models for simulation and quantitative description of the industrial operations involved in order to achieve the continuously increasing productivity, flexibility and quality(dimensional accuracy, mechanical properties and surface properties). The mathematical modeling of hot rolling process has long been recognized to be a desirable approach to investigate rolling operating practice and design of mill requirement. 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.