A multi-task learning-based optimization approach for finding diverse sets of microstructures with desired properties
| Autor: | Tarek Iraki, Lukas Morand, Johannes Dornheim, Norbert Link, Dirk Helm |
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| Přispěvatelé: | Publica |
| Rok vydání: | 2023 |
| Předmět: |
crystal plasticity
multidimensional scaling Siamese neural networks distance preserving feature extraction materials design Crystal plasticity Materials design multi-task lerning texture optimization Industrial and Manufacturing Engineering machine learning siamese neural networks Multi-task learning Artificial Intelligence Distance preserving feature extraction Machine learning Texture optimization ddc:620 Multidimensional scaling Engineering & allied operations Software |
| Zdroj: | Journal of Intelligent Manufacturing |
| ISSN: | 1572-8145 0956-5515 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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