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

Tarek Iraki (), Lukas Morand (), Johannes Dornheim (), Norbert Link () and Dirk Helm ()
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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

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 4, No 25, 1887-1903

Abstract: 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.

Keywords: Crystal plasticity; Distance preserving feature extraction; Machine learning; Materials design; Multi-task learning; Multidimensional scaling; Siamese neural networks; Texture optimization (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10845-023-02139-8

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