3DMaterialGAN: Learning 3D Shape Representation from Latent Space for Materials Science Applications
This work addresses the need for 3D shape representation in materials science, enabling applications in additive manufacturing and aerospace, though it is incremental as it adapts existing GAN methods to a new domain.
The paper tackled the problem of 3D object generation for materials science by proposing 3DMaterialGAN, a GAN that synthesizes 3D polycrystalline microstructures from latent space, achieving performance comparable to or better than state-of-the-art on benchmark datasets and validating results on real-world titanium alloy data.
In the field of computer vision, unsupervised learning for 2D object generation has advanced rapidly in the past few years. However, 3D object generation has not garnered the same attention or success as its predecessor. To facilitate novel progress at the intersection of computer vision and materials science, we propose a 3DMaterialGAN network that is capable of recognizing and synthesizing individual grains whose morphology conforms to a given 3D polycrystalline material microstructure. This Generative Adversarial Network (GAN) architecture yields complex 3D objects from probabilistic latent space vectors with no additional information from 2D rendered images. We show that this method performs comparably or better than state-of-the-art on benchmark annotated 3D datasets, while also being able to distinguish and generate objects that are not easily annotated, such as grain morphologies. The value of our algorithm is demonstrated with analysis on experimental real-world data, namely generating 3D grain structures found in a commercially relevant wrought titanium alloy, which were validated through statistical shape comparison. This framework lays the foundation for the recognition and synthesis of polycrystalline material microstructures, which are used in additive manufacturing, aerospace, and structural design applications.