Alloy synthesis and processing are crucial for designing alloys with desired microstructures and properties. This paper introduces a semi-supervised text mining method to extract synthesis and processing parameters from a corpus of superalloy articles. The method automatically extracts 9853 superalloy synthesis and processing actions with chemical compositions, improving the performance of a data-driven γ′ phase coarsening prediction model. This approach complements data-driven methods in exploring the relationship between synthesis and alloy structures.
