ISSN:
1572-8145
Keywords:
Hot strip mill
;
edger roll
;
width spread
;
neural network estimator
Source:
Springer Online Journal Archives 1860-2000
Topics:
Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
Notes:
Abstract 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.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1023/A:1026552406200
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