Machine Learning-Based Microstructure Prediction for Laser-Sintered Alumina

Predicting material’s microstructure is essential to link processing-structure-property (PSP) during advanced manufacturing. Usually, electron microscopy is used to characterize the microstructure at the needed scale. This process is time- and labor-consuming. In this work, we demonstrate a machine learning-based microstructure prediction that not only predicts the microstructure for unknown conditions but also predicts all features of microstructure (e.g., grain size, grain shape, porosity, etc.). We modified and improved the generative adversarial network (GAN) to be suitable for better microstructure predictions. Realistic SEM micrographs can be generated from a condition. When the condition is a processing parameter, we can correlate and predict the material’s microstructure under an unknown processing parameter. If the condition is a material’s property, such as hardness. We can predict material’s microstructure for an unknown hardness. We show that the GAN-based algorithm can predict more complicated features in the microstructure after it is modified with residual blocks. To evaluate the prediction accuracy, we use pre-trained convolutional neural network (CNN) to predict hardness from the GAN-generated SEM micrographs. We found CNN-predicted hardness matched well with the hardness that was used to predict the microstructure.