A neural network model to determine the plate

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Performance of the process reducing the slab width in hot plate mill called edging is critical to produce rolled products with a desired dimension, which otherwise increase the yield loss caused by trimming. This process, therefore, requires a stringent width control performance. In this paper, an edger set-up model generating the desired slab width required for the control is proposed based upon the neural network approach. This neural network model accounts for variation of the dimension of incoming slabs to predict the preset value of the width as accurately as possible. A series of simulations were conducted to evaluate the performance of the neural network estimator for a variety of operating conditions needed for producing rolled products of various dimensions. The results show that the proposed model can estimate the preset value of the slab width with good accuracy, thereby enhancing the dimensional accuracy of rolled products. The estimation performance is discussed in detail for various process operation conditions.
Publisher
Springer
Issue Date
2000-06
Language
English
Article Type
Article
Citation

JOURNAL OF INTELLIGENT MANUFACTURING, v.11, no.6, pp.547 - 557

ISSN
0956-5515
DOI
10.1023/A:1026552406200
URI
http://hdl.handle.net/10203/1938
Appears in Collection
ME-Journal Papers(저널논문)
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