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New discoveries in cold rolling: understanding stress distribution and parameter dependence for faster, more accurate models

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conference contribution
posted on 2024-01-10, 14:22 authored by Francis FlanaganFrancis Flanagan, Doireann O'KielyDoireann O'Kiely, Alison O'ConnorAlison O'Connor, Mozhdeh Erfanian, Edward James Brambley

The finite element (FE) method is a powerful tool for simulating industrial metal forming processes such as metal rolling. FE allows users to estimate the stress distribution in the metal sheet during the rolling process. However, FE simulations do not allow for real-time online process control due to model complexity and computational time. This paper forms part of a largescale research project aimed at designing a simple-but-accurate mathematical model that provides sufficiently precise results (compared to FE simulations) with faster computational timescales allowing for real-time process control. To validate the asympotics-based mathematical model, an accurate FE model is required. In this paper, we give a detailed description of a quasi-static Abaqus/Explicit FE model and show how this is optimised to represent the rolling process. We report new insights gained from the FE simulations which can guide the development of simpler, faster mathematical models.

Funding

SFI Centre for Research Training in Foundations of Data Science

Science Foundation Ireland

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Machine Learning for Structural Integrity Assessments

European Commission

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Applied Mathematical Modelling of Industrial Metal Forming

UK Research and Innovation

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History

Publication

Proceedings of the 14th International Conference on the Technology of Plasticity - Current Trends in the Technology of Plasticity. ICTP 2023. Lecture Notes in Mechanical Engineering

Publisher

Springer

Rights

This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-031-41023-9_22

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  • Bernal Institute

Department or School

  • School of Engineering
  • Mathematics & Statistics

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