A neural network model to determine the plate width set-up value in a hot plate mill

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.