Multi-faceted modelling for strip breakage in cold rolling using machine learning

In the cold rolling process of steel strip products, strip breakage is an undesired production failure which can lead to yield loss, reduced work speed and equipment damage. To perform a root cause analysis, conventional physics-based approaches which focus on mechanical and metallurgical principles have been applied in a retrospective manner. With the advancement of data acquisition technologies, numerous process monitoring data is collected by various sensors deployed along this process; however, conventional approaches cannot take advantage of these data. In this paper, a machine learning-based approach is proposed to characterise and model strip breakage in a predictive manner. First, to match the temporal characteristic of strip breakage which occurs instantaneously, historical multivariate time-series data of a cold rolling process were extracted in a run-to-failure manner, and a sliding window strategy was adopted for data annotation. Second, breakage-centric features were identified from three facets - physics-based approaches, empirical knowledge and data-driven features. Finally, these features were used as inputs for strip breakage modelling using recurrent neural networks (RNNs), which are specialised in discovering underlying patterns embedded in time-series data. An experimental study using real-world data collected from a cold-rolled electrical steel strip manufacturer revealed the effectiveness of the proposed approach.