Roll load prediction—data collection, analysis and neural network modelling

Abstract Roll load is a critical design parameter in steel rolling operation and mill setup. In this work a parsimonious roll load prediction model was developed using a neural network (NN). Design techniques based on orthogonal arrays were adopted for the allocation of the rolling process conditions, while a validated finite element (FE) code was used to generate the roll load data based on the process conditions specified by the orthogonal array. The rolling data obtained were then analysed using the traditional statistical techniques, such as level (mean response) analysis and ANOVA (analysis of variances), in order to find the critical input variables. A double-loop interactive training procedure was adopted in order to prevent over-fitting, with the resulting NN model balanced between the training and validation performance. Model performance analysis was conducted on the initial NN model to find if weak prediction regions exist, and further rolling data to cover these weak regions were generated and the extended data were used for re-training. The resulting model was then applied to new rolling data for testing, and the roll load prediction was satisfactory. The NN model prediction can be implemented for online application such as rolling schedule optimisation and dynamic roll gap control, due to its fast calculation ability. Post-model analyses such as model responses have been conducted to enhance the understanding of the behaviour of the neural network model, which is vital to increase the confidence in using the NN model. Model sensitivity derived from the NN model is consistent with the statistical analysis of the rolling data.