A multi-task learning-based optimization approach for finding diverse sets of material microstructures with desired properties and its application to texture optimization
This addresses a core challenge in data-driven materials science for designing materials with specific properties, but it appears incremental as it builds on existing multi-task and siamese network techniques.
The paper tackles the problem of identifying diverse material microstructures with desired properties by introducing a multi-task learning-based optimization approach, which was applied to texture optimization for rolled steel sheets, though no concrete numerical results are provided.
The 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.