Metallurgical Alchemy: Synthesizing Steel Microstructure Images Using DCGANs

Abstract

Characterizing the microstructures of steel subjected to heat treatments is crucial in the metallurgical industry for understanding and controlling their mechanical properties. In this study, we present a novel approach for generating images of steel microstructures that mimic those obtained with optical microscopy, using the deep learning technique of generative adversarial networks (GAN). The experiments were conducted using different hyperparameter configurations, evaluating the effect of these variations on the quality and fidelity of the generated images. The obtained results show that the images generated by artificial intelligence achieved a resolution of 512 × 512 pixels and closely resemble real microstructures observed through conventional microscopy techniques. A precise visual representation of the main microconstituents, such as pearlite and ferrite in annealed steels, was achieved. However, the performance of GANs in generating images of quenched steels with martensitic microstructures was less satisfactory, with the synthetic images not fully replicating the complex, needle-like features characteristic of martensite. This approach offers a promising tool for generating steel microstructure images, facilitating the visualization and analysis of metallurgical samples with high fidelity and efficiency.

Keywords

• deep learning
• GANs
• low-carbon steels
• optical microstructure