The New Advanced System of Plate Mill Computer Control

Okamoto, Toyohiko; Misaka, Yoshisuke; Yokoi, Takeshi; Kise, Kazutake; Fujimaki, I.

doi:10.1016/s1474-6670(17)67671-7

Cited by 1 publication

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“…Maintaining a constant relative crown, i.e., the thickness difference between strip edge and center with respect to the current strip thickness requires less roll bending and thus linearly reducing roll force with decreasing strip thickness as already pointed out by Shohet and Townsend (1968) and Okamoto et al (1975). Building on this insight Jonsson and Mäntylä (1985) proposed a backwards calculation scheme for plate schedules that derived the required roll force from the final desired plate crown and the bending characteristics of the mill.…”

Section: Pass Schedule Generationmentioning

confidence: 73%

Coupling of an analytical rolling model and reinforcement learning to design pass schedules: towards properties controlled hot rolling

2023

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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.

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Section: Pass Schedule Generationmentioning

confidence: 73%