Deep Learning Based Inverse Modeling for Materials Design: From Microstructure and Property to Processing

Polycrystalline materials are crucial in various industries, necessitating a comprehensive understanding of the processing-structure-property-performance (PSPP) relationships. Traditional experimental methods are laborious and slow, while computational approaches predominantly address forward problems, deriving structures and properties from processing conditions. Conversely, inferring processing parameters from desired microstructures and properties remains a crucial yet challenging inverse problem due to the complex and nonlinear mappings involved. In this work, we propose a deep learning-based framework exploring non-sequential and sequential models to address two key inverse problems: predicting processing parameters from microstructures and from properties. Focusing on microstructural texture defined by the orientation distribution function (ODF), we apply our framework to copper, generating a dataset of 31,588 unique processing strain rates ($s$^-1) in [0, 1] with corresponding ODFs and homogenized properties through simulations. Our inverse prediction results on processing parameters demonstrate high accuracy, with average test RMSEs of 0.0152 from microstructures and 0.0295 from properties. These findings validate the framework's efficacy as a tool for polycrystalline materials process design, enabling the precise determination of processing methods to achieve desired microstructures and properties.