Reservoir Computing neural networks for estimating mechanical properties of hot steel strips

The steelmaking industry could benefit greatly from a reliable technique for predicting the mechanical properties of rolling products. This would lead to significant reductions in time and costs associated with the process.In this paper, we present a novel approach to predict the ultimate tensile strength of hot-rolled steel strips, utilizing the capabilities of recurrent neural models to process temporal data. Our focus is on Reservoir Computing (RC) models, selected for their efficient training characteristics, which are advantageous in production support contexts. In the paper, we introduce two custom RC-based architectures, designed to handle input features distinctively based on their relevance to the steelmaking process. The proposed approach is experimentally validated on a real use case with data originating from a hot rolling steel strip plant. It is compared against standard RC and fully-trainable recurrent neural networks. The results demonstrate the ability of the proposed method to reach a significantly good predictive performance, largely within the threshold set by industry experts. Our custom RC models offer an outstanding balance between predictive performance and computational efficiency, making them highly suitable for this application. In comparison to baseline RC approaches, at substantially the same computational cost, we manage to reduce the prediction error by more than 50%. Moreover, in comparison with fully trained models, we achieve even slightly more accurate predictions while reducing computational cost by more than 20 times. Finally, our results indicate that it is possible to further improve predictive performance through the differentiation of predictive models based on chemical composition similarities.