Comparing the Visual Quality of Deep Generative Models for Steel Microstructures

Marcel Wentzien, Jerome Ingber, Jörg Schlötterer, Dirk Schmalzried

Abstract: In the realm of AI-driven material sciences, generating highly realistic steel microstructure images represents an opportunity to address data scarcity and to advance data-driven diagnostic and predictive applications. While previous work achieved good performance values for generating scanning electron microscopy grayscale images of steel microstructures, research on colorful light optical microscopy (LOM) is limited. We investigate the capabilities of two deep generative models to generate highly realistic LOM steel microstructure images. We train a StyleGANv2 and the recently published FractalGen on a dataset of microstructural images from 30 different steels and conduct a small expert study to evaluate how realistic the generated images look. Because of visible grid-like patterns, the experts almost always correctly identified FractalGen generated images as synthetic, while StyleGANv2 generated images were mostly indistinguishable from real images to them. We also used the StyleGANv2’s discriminator to conduct the same task, resulting in not yet fully explainable classification results. Our findings are a first step towards the generation of realistic synthetic pairs of microstructures and the steels chemical composition and processing, which could reduce costs in future AI-driven material sciences research. Code is available at https://github.com/eah-materials/RealOrFakeSteelMicrostructure

External IDs:doi:10.1007/978-3-032-02813-6_23