Prediction of the microstructure of cold-compacted Astaloy 85Mo with deep generative models

Abstract

In powder metallurgy, iron-based powders are compacted to so-called green bodies, which undergo a subsequent sintering process. The microstructure of the green bodies affects the sintering process and the resulting properties of the produced component. Assessing the microstructure experimentally is time consuming and costly, while simulation approaches rely on simplifications that significantly affect relevant microstructural information. This work investigates the suitability of deep generative models to synthetically generate realistic micrographs of green bodies as an alternative to experiments and simulations. For that purpose experiments comparing Generative Adversarial Neural Networks and Variational Autoencoders were conducted. The two deep learning frameworks were used to train models which produce synthetic micrographs in dependence of two parameters, namely powder particle size and green body porosity. The data utilized to train and evaluate the models was acquired from compacted water-atomized Astaloy 85Mo via scanning electron microscopy. Besides visual inspection, the trained models were evaluated via quantitative metrics. Distributions of relevant microstructural parameters such as pore perimeter or minimum and maximum pore feret diameter of the synthetically generated micrographs were compared to those of experimentally acquired micrographs. The results indicate that Generative Adversarial Networks represent a promising approach to train models, which produce synthetic micrographs that capture relevant properties of green body micrographs in dependence of given parameters such as powder particle size. In contrast, the Variational Autoencoders trained for this study did not capture microstructural properties well.