Deep learning and crystal plasticity: a preconditioning approach for accurate orientation evolution prediction

Peyman Saidi, Hadi Pirgazi, Mehdi Sanjari, Saeed Tamimi, Mohsen Mohammadi, Laurent K. Béland, Mark R. Daymond, Isaac Tamblyn

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
2 Downloads (Pure)

Abstract

Efficient and precise prediction of plasticity by data-driven models relies on appropriate data preparation and a well-designed model. Here we introduce an unsupervised machine learning-based data preparation method to maximize the trainability of crystal orientation evolution data during deformation. For Taylor model crystal plasticity data, the preconditioning procedure improves the test score of an artificial neural network from 0.831 to 0.999, while decreasing the training iterations by an order of magnitude. The efficacy of the approach was further improved with a recurrent neural network. Electron backscattered (EBSD) lab measurements of crystal rotation during rolling were compared with the results of the surrogate model, and despite error introduced by Taylor model simplifying assumptions, very reasonable agreement between the surrogate model and experiment was observed. Our method is foundational for further data-driven studies, enabling the efficient and precise prediction of texture evolution from experimental and simulated crystal plasticity results.
Original languageEnglish
Article number114392
Number of pages13
JournalComputer Methods in Applied Mechanics and Engineering
Volume389
Early online date8 Dec 2021
DOIs
Publication statusPublished - 1 Feb 2022

Keywords

  • neural network
  • gated recurrent unit
  • crystal plasticity
  • Taylor model

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