Accounting for shear deformation in fast models for plate rolling

Seuren, Simon; Seitz, Joachim; Krämer, A.; Bambach�, Markus

doi:10.1007/s11740-013-0500-4

Cited by 12 publications

(21 citation statements)

References 19 publications

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“…The total equivalent strain profiles after von Mises (black solid line) shown in Fig. 18 are already observed by several authors like Lachmann and Zouhar [102], Moon and Lee [134], Seuren et al [84] and Serajzadeh [135]. The first rolling case with the small roll gap ratio l c / h m shows a maximum total equivalent strain after von Mises below the surface.…”

Section: Resultssupporting

confidence: 70%

“…This explains the maximum equivalent strain below the surface shown in Fig. 18 and observed by Lachmann and Zouhar [102] and others [84,134].…”

Section: Small Roll Gap Ratiosupporting

confidence: 66%

“…Seuren et al [84] presented a data-based approach with the aim to improve the total equivalent strain prediction over the rolling stock height for hot plate rolling of C-Mn steel. They derived based on FE simulations an analytical equation to consider shear and combined it with the slab model to predict the total equivalent strain after the roll gap exit.…”

Section: Data-based Extension Of the Slab Modelmentioning

confidence: 99%

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Procedure to implement shear evolution in fast rolling models with online capability on the example of an aluminum flat rolling process

et al. 2022

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The rolling process induces a heterogeneous deformation over the rolling stock height. The causes are the frictional shear stress between the contacting surfaces and the roll gap geometry. They induce a complex material flow within the rolling stock describable by the shear evolution. The shear evolution has a significant impact on rolling values like grain size and crystallographic texture and therefore on the final material properties of the rolled product. Industry and academia use fast rolling models for flat rolling processes to predict the material properties due to their short computation time. The time advantage enables online applicability to adapt processes in real time or to evaluate the influence of different process conditions several times within seconds. However, these models have a limitation regarding the shear evolution. They do not consider all relevant influences or apply simplifying assumptions, valid only for specific rolling cases. This work presents a general approach to extend fast rolling models to consider the shear evolution without any restrictions to specific rolling cases. The approach derives the shear evolution as shear angle $$\alpha$$ α evolution based on FE simulations. The shear angle $$\alpha$$ α is a geometrical description and not influenced by rotations, which occur during the rolling process. This enables an enhanced and simple analysis of the material flow. The outcome is an extended model that completely describes the deformation along a deformation path and enables the calculation of each desired deformation value (strain, deformation gradient, velocity gradient, etc.).

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Section: Resultssupporting

confidence: 70%

“…This explains the maximum equivalent strain below the surface shown in Fig. 18 and observed by Lachmann and Zouhar [102] and others [84,134].…”

Section: Small Roll Gap Ratiosupporting

confidence: 66%

Section: Data-based Extension Of the Slab Modelmentioning

confidence: 99%

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Procedure to implement shear evolution in fast rolling models with online capability on the example of an aluminum flat rolling process

et al. 2022

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“…For the thermal model an explicit finite difference technique was used, which subdivided a transverse section of the rolled material into quadrilateral elements. Seuren et al (2014), showed that such a model can be extended to incorporate shear strain determined in FE simulations and in turn improved the microstructure prediction over roll stock thickness. Furthermore, Lohmar et al (2014a) combined inverse modeling and fast rolling models to determine semiempirical material model parameters based on industrial rolling data.…”

Section: Fast Rolling Modelsmentioning

confidence: 99%

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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“…42. Grid distortion and net shear strain through the roll gap for thick plate (400 mm, 20% reduction) and thin plate (10 mm, 20% reduction) [195]. [190].…”

Section: Product Property: Anisotropymentioning

confidence: 99%