A Deep Learning Framework for Time-Series Processing-Microstructure-Property Prediction

A process simulator provides valuable insights into the evolution of microstructures under various elementary processes, employing the Orientation Distribution Function (ODF) as a representation of the microstructure's texture. However, such simulations often involve complex physical computations, making them time-consuming. To address this, our study introduces an artificial intelligence (AI)-based framework to predict the microstructural texture of polycrystalline materials using a specified deformation process. As a case study, we apply our framework to copper. The dataset includes 3,125 unique processing parameter combinations and their corresponding ODF vectors generated using a process simulator. The resulting predictions enable the calculation of homogenized properties. As opposed to traditional material processing simulations, our AI-driven framework offers faster results with minimal error rates (less than 0.5%). This indicates that our approach is a promising tool for rapidly predicting processing-specific microstructures and properties, thereby offering significant improvements over conventional simulation techniques.