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Abstract. Rolling is a well-established forming process for producing finished or semi-finished products in various industries. Although highly automated, most rolling processes are designed manually by experts based on their knowledge, highly specialized heuristics and analytical process models or numerical simulations. This manual design approach does not lead to an optimization accounting for multiple objectives. Previous work [1] has shown the potential of coupling reinforcement learning (RL) with fast analytical rolling models (FRM) to optimize hot rolling processes. However, the designed pass schedules do not robustly reach the desired final height within typical industrial tolerances. Therefore, in this paper the existing approach of coupling RL with an FRM is extended by dynamically ranges for height reductions. This extension guarantees that the target height is always reached exactly. In addition to the height reduction, the RL algorithm can determine the inter-pass time, initial slab temperature and rolling velocity. For the optimization, an objective function, called reward function, considering all relevant optimization objectives such as the final grain size and energy consumption, was developed. An exemplary training was performed for a defined starting (140 mm) and final height (25 mm). The resulting, automatically designed pass schedules reach the target height and fulfill all defined optimization objective including the required average austenite grain size.

Rolling is a well-established forming process employed in many industrial sectors. Although highly optimized, process disruptions can still lead to undesired final mechanical properties. This paper demonstrates advances in pass schedule design based on reinforcement learning and analytical rolling models to guarantee sound product quality. Integrating an established physical strengthening model into an analytical rolling model allows tracking the microstructure evolution throughout the process, and furthermore the prediction of the yield strength and ultimate tensile strength of the rolled sheet. The trained reinforcement learning algorithm Deep Deterministic Policy Gradient (DDPG) automatically proposes pass schedules by drawing upon established scheduling rules combined with novel rule sets to maximize the final mechanical properties. The designed pass schedule is trialed using a laboratory rolling mill while the predicted properties are confirmed using micrographs and materials testing. Due to its fast calculation time, prospectively this technique can be extended to also account for significant process disruptions such as longer inter-pass times by adapting the pass schedule online to still reach the desired mechanical properties and avoid scrapping of the material.

Decision support systems can provide real-time process information and correlations, which in turn assists process experts in making decisions and thus further increase productivity. This also applies to well-established and already highly automated processes in continuous production employed in various industrial sectors. Continuous production refers to processes in which the produced material, either fluid or solid form, is continuously in motion and processed. As a result, the process can usually not be stopped. It is only possible to influence the running process. However, the highly nonlinear interactions between process parameters and product quality are not always known in their entirety which led to inferior product quality in terms of mechanical properties and surface quality. This requires accurate representations of the processes and the products in real-time, so-called digital shadows.Therefore, this contribution shows the necessary steps to provide a digital shadow based on numerical, physical models and process data and to couple the digital shadow with data analysis and machine learning to enable automatic decision support. This is exemplified at various stages throughout two different process chains with continuous processes: first, by using a thermoplastic production process called profile extrusion, and second, on the example of a metal processing process chain, from which three processes are described in more detail, namely, hot rolling, tempering, and fine blanking. Finally, the presented approaches and results are summarized.