Abstract. Hot steel rolling is amongst the most important industrial techniques because of huge amount of consumed resources, immense environmental impact, and the significance of the long products in overall economy. Criteria for improving rolling operations include process efficiency, resource consumption, system reliability, product quality and ergo-ecological sustainability, all of which being critically influenced by roll pass design (RPD). With advances in computerised information processing, it becomes apparent that further progress is to be sought in intelligently combining different RPD strategies. The key to optimising rolling systems is to be found in hybrid modelling i.e. in combining stochastic, deterministic and evolutionary analyses. Evidence obtained by using small-scale chemo-physical modelling encourages the use of experimental rolling to study the RPD interactions. However, with the advent of data acquisition and processing systems, the large collections of industrial records can nowadays be analyzed within the real time, thus allowing for online extracting and applying useful knowledge. This leads to implementing I4 and I5 paradigms. A precondition for employing machine learning and big data analytics is to establish a suitable metrics including digitization of the RPD variables. Examples of roll pass deformation zone translation into vectors are presented along with an application of the inferred models to solve an actual RPD problem.
Keywords: steel rolling, roll pass design, knowledge, manufacturing, statistical analysis.