Robust design of artificial neural network for roll force prediction in hot strip mill

In the steel industry, a vast amount of data are gathered and stored in databases. These data usually exhibit high correlations, nonlinear relationships and low signal to noise ratios. Artificial neural networks (ANN) are known to be very useful for such data. However, selecting a suitable set of ANN parameter values is difficult even for an experienced user. This article proposes an experimental approach for determining ANN parameters in a robust manner for predicting the roll force in a hot strip mill process. Four design variables and two noise variables are included in the experiment, a full factorial design is adopted for the design matrix to estimate all main and two factor interaction effects, and the signal-to-noise (SN) ratio is used as a performance measure for achieving robustness. In the second experiment, only a fraction of the full factorial design is used as the design matrix and the results are compared with those from the full factorial experiment in terms of prediction accuracy. Experimental results show that the learning rate is the most significant parameter in terms of the SN ratio. The proposed method has a general applicability and can be used to alleviate the burden of selecting appropriate ANN parameter values.