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The optimization along the chain processing-structure-properties-performanceis one of the core objectives in data-driven materials science. In this sense,processes are supposed to manufacture workpieces with targeted materialmicrostructures. These microstructures are defined by the material propertiesof interest and identifying them is a question of materials design. In thepresent paper, we addresse this issue and introduce a generic multi-tasklearning-based optimization approach. The approach enables the identificationof sets of highly diverse microstructures for given desired properties andcorresponding tolerances. Basically, the approach consists of an optimizationalgorithm that interacts with a machine learning model that combines multi-tasklearning with siamese neural networks. The resulting model (1) relatesmicrostructures and properties, (2) estimates the likelihood of amicrostructure of being producible, and (3) performs a distance preservingmicrostructure feature extraction in order to generate a lower dimensionallatent feature space to enable efficient optimization. The proposed approach isapplied on a crystallographic texture optimization problem for rolled steelsheets given desired properties.

A multi-task learning-based optimization approach for finding diverse  sets of material microstructures with desired properties and its application  to texture optimization