Deep Convolutional Generative Modeling for Artificial Microstructure Development of Aluminum-Silicon Alloy
This work addresses microstructure development for materials science, but it appears incremental as it applies existing methods to a specific alloy without clear broad advancements.
The study tackled the problem of generating artificial microstructures for Aluminum-Silicon alloys using deep generative adversarial networks, with results showing the models learned to replicate lining near certain microstructure images.
Machine learning which is a sub-domain of an Artificial Intelligence which is finding various applications in manufacturing and material science sectors. In the present study, Deep Generative Modeling which a type of unsupervised machine learning technique has been adapted for the constructing the artificial microstructure of Aluminium-Silicon alloy. Deep Generative Adversarial Networks has been used for developing the artificial microstructure of the given microstructure image dataset. The results obtained showed that the developed models had learnt to replicate the lining near the certain images of the microstructures.