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A multi-task learning-based optimization approach for finding diverse sets of microstructures with desired properties

Author

Listed:
  • Tarek Iraki

    (Karlsruhe University of Applied Sciences)

  • Lukas Morand

    (Fraunhofer Institute for Mechanics of Materials IWM)

  • Johannes Dornheim

    (Institute for Applied Mechanics - Computational Materials Sciences IAM-CMS, Karlsruhe Institute of Technology)

  • Norbert Link

    (Karlsruhe University of Applied Sciences)

  • Dirk Helm

    (Fraunhofer Institute for Mechanics of Materials IWM)

Abstract

Optimization along the chain processing-structure-properties-performance is one of the core objectives in data-driven materials science. In this sense, processes are supposed to manufacture workpieces with targeted material microstructures. These microstructures are defined by the material properties of interest and identifying them is a question of materials design. In the present paper, we addresse this issue and introduce a generic multi-task learning-based optimization approach. The approach enables the identification of sets of highly diverse microstructures for given desired properties and corresponding tolerances. Basically, the approach consists of an optimization algorithm that interacts with a machine learning model that combines multi-task learning with siamese neural networks. The resulting model (1) relates microstructures and properties, (2) estimates the likelihood of a microstructure of being producible, and (3) performs a distance preserving microstructure feature extraction in order to generate a lower dimensional latent feature space to enable efficient optimization. The proposed approach is applied on a crystallographic texture optimization problem for rolled steel sheets given desired properties.

Suggested Citation

  • Tarek Iraki & Lukas Morand & Johannes Dornheim & Norbert Link & Dirk Helm, 2024. "A multi-task learning-based optimization approach for finding diverse sets of microstructures with desired properties," Journal of Intelligent Manufacturing, Springer, vol. 35(4), pages 1887-1903, April.
  • Handle: RePEc:spr:joinma:v:35:y:2024:i:4:d:10.1007_s10845-023-02139-8
    DOI: 10.1007/s10845-023-02139-8
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    References listed on IDEAS

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    1. J. Kruskal, 1964. "Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis," Psychometrika, Springer;The Psychometric Society, vol. 29(1), pages 1-27, March.
    2. Rohit Batra, 2021. "Accurate machine learning in materials science facilitated by using diverse data sources," Nature, Nature, vol. 589(7843), pages 524-525, January.
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