Analysis and modelling of steel hot rolling assisted by machine learning

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Hot deformation is a key stage in the manufacturing of steel sheet, the requirement at the end of this stage being thickness control. The Algoma hot rolling mill has a conventional set up of seven rolling 'stands' positioned in a line, one after the other. The cast steel 'strand' is heated to a high temperature, and passes through these stands, undergoing thickness reduction at each stand, with the required thickness achieved by the last stand. Because the travel time through all these stands is very short, the roll gaps of each stand has to be pre-set before rolling. No matter how 'rigid' the rolling stands are, the roll gaps will always widen because, even at high temperatures, steel is very strong. To pre-set the roll gap, an accurate prediction of the strength of the steel is performed by a mathematical mill 'model', which is conventionally based on predictions of how the microstructure and the temperature changes at each stand. The mill model is semi-empirical; thus, whenever the steel is rolled, the measured rolling load can be used to improve the prediction of the models. A completely empirical approach to mill modelling is based on machine learning. Here, the model is generated 'purely' based on measured data inputs, such as roll speed. No knowledge of the physics of the process is required. Machine learning means that the model is developed that links the data 'intuitively' as opposed to a purely statistical approach. This proposal builds on a machine learning model recently developed for the Algoma for strip. The first iteration of the machine learning model is far superior to the current Algoma mill model However, predictions for the first rolling stands were not as accurate as for the rest to the stands. Also, analysis of the model revealed some very metallurgically puzzling correlations between process parameters and the rolling loads. In this proposal, we will improve the predictions for the initial rolling stands. We will also seek to understand the aforementioned process/rolling load correlations in order to gain further insight into the metallurgy of the process. We will further improve the model by developing algorithms that are specific to the Algoma process. Finally, we will generate a model that can predict mechanical properties of the as-hot rolled strip from rolling process parameters.