Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/308127 
Year of Publication: 
2023
Citation: 
[Journal:] Journal of Intelligent Manufacturing [ISSN:] 1572-8145 [Volume:] 35 [Issue:] 4 [Publisher:] Springer US [Place:] New York, NY [Year:] 2023 [Pages:] 1887-1903
Publisher: 
Springer US, New York, NY
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.
Subjects: 
Crystal plasticity
Distance preserving feature extraction
Machine learning
Materials design
Multi-task learning
Multidimensional scaling
Siamese neural networks
Texture optimization
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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