Speaker
Description
Identifying representations and characterizing the microstructure features is crucial for developing the damage-tolerant dual-phase steels. These representations serve as variables to establish a structure-property relationship and to design microstructures of desired mechanical property. However, the complex nature of the DP steel microstructure, poses a challenge and the existing characterization methods are limited in encoding this information using the handcrafted features.
To tackle this challenge, I will introduce machine learning models
1. To extract features automatically from synthetic DP steel images and represent the microstructure in low dimensional latent space.
2. To conditionally generate damage-tolerant microstructure patterns with desired yield stress.
