Creating a Microstructure Latent Space with Rich Material Information for Multiphase Alloy Design
This work addresses the need for more reliable and effective alloy design methods in materials science, particularly for multiphase alloys, by incorporating microstructural information, though it appears incremental as it builds on existing deep-learning frameworks.
This study tackled the problem of traditional alloy design overlooking microstructural details by introducing a deep-learning algorithm that integrates real microstructural data to establish precise composition/processing-structure-property relationships, demonstrated through the design of a dual-phase steel with results assessed at three performance levels.
The intricate microstructure serves as the cornerstone for the composition/processing-structure-property (CPSP) connection in multiphase alloys. Traditional alloy design methods often overlook microstructural details, which diminishes the reliability and effectiveness of the outcomes. This study introduces an improved alloy design algorithm that integrates authentic microstructural information to establish precise CPSP relationships. The approach utilizes a deep-learning framework based on a variational autoencoder to map real microstructural data to a latent space, enabling the prediction of composition, processing steps, and material properties from the latent space vector. By integrating this deep learning model with a specific sampling strategy in the latent space, a novel, microstructure-centered algorithm for multiphase alloy design is developed. This algorithm is demonstrated through the design of a unified dual-phase steel, and the results are assessed at three performance levels. Moreover, an exploration into the latent vector space of the model highlights its seamless interpolation ability and its rich material information content. Notably, the current configuration of the latent space is particularly advantageous for alloy design, offering an exhaustive representation of microstructure, composition, processing, and property variations essential for multiphase alloys.