|About this Abstract
||7th World Congress on Integrated Computational Materials Engineering (ICME 2023)
||Predicting Laser Powder Bed Fusion Microstructures Using Machine Learning
||Gregory D. Wong, Anthony D Rollett, Gregory S Rohrer
|On-Site Speaker (Planned)
||Gregory D. Wong
The ability to predict as printed microstructures is essential for use in modeling mechanical performance in metal additive manufacturing. The computational expense of existing methods leads to the option of employing rapid machine learning based methods. This talk covers work using conditional generative adversarial networks (cGANs) to generate synthetic microstructures corresponding to metal additive parts made of cubic metals. A set of training data has been developed using existing methods and varying the parameters used in a laser powder bed fusion additive process (laser power, laser velocity, hatch spacing, etc.). The cGAN model is trained using 2D slices of the 3D model output that have been labeled with the printing parameters used in each simulation for conditioning. Microstructure images used for training alongside corresponding cGAN model outputs will be presented.