Sz. Cho et al., A HYBRID NEURAL-NETWORK MATHEMATICAL PREDICTION MODEL FOR TANDEM COLDMILL, Computers & industrial engineering, 33(3-4), 1997, pp. 453-456
In tandem cold mill, a strip is flattened by stands of rolls to a desi
red thickness. At Pohang Iron and Steel Company (POSCO) in Pohang, Kor
ea, precalculation determines the mill settings before a strip actuall
y enters the mill and is done by an outdated mathematical model. A cor
rective neural network model is proposed to improve the accuracy of th
e roll force prediction. The network is fed not only the usual mathema
tical model's input but also a set of additional inputs such as the ch
emical composition of the coil, its coiling temperature and the aggreg
ated amount of processed strips of each roll. The network was trained
using a standard backpropagation with 4,944 process data collected at
POSCO from March 1995 through December 1995, then was tested on the un
seen 1,586 data from February 1996 through April 1996. The combined mo
del reduced the prediction error by 33.88% on average. (C) 1997 Elsevi
er Science Ltd.