Speaker
Description
Prediction processing-microstructure-property (PMP) link is critical for material processing, characterization, and discovery. We demonstrate GAN-based machine learning models that can accurately predict PMP relationships, specifically in the prediction of (1) the microstructure of alumina under arbitrary laser power, (2) the expected microstructure from the desired hardness, (3) real-time, in-situ microstructure during laser manufacturing, and (4) phases and element distributions of multi-phase materials. In all these studies, the ceramic samples were fabricated using high-throughput, ultra-fast laser convergent manufacturing of ceramics. We demonstrate that experimentally-obtained data of processing paramters, microstructure, and properties were sufficient for training of large models that contain tens of millions of parameters. An independent neural network was developed and showed that ML-predicted microstructure had less than 10% error from real ones, in terms of projected hardness. To monitor the microstructure during laser sintering, we demonstrated an ML model that can instantaneously predict the ceramic’s microstructure at the laser spot, based on the laser spot brightness. The ML model can generate more than 10 predictions per second, and the error in average grain size was less than 5% from the experimental observations.
