# Metallurgical Alchemy: Synthesizing Steel Microstructure Images Using DCGANs
## 5 Conclusions  
The experiments highlight the importance of a balanced adversarial game between the generator and discriminator to achieve stable training dynamics and high-quality image generation. The use of smoothed loss curves and probability metrics provides deeper insights into the training process, beyond what is visible from raw data alone. Future work could explore optimizing hyperparameters further, using advanced techniques such as Wasserstein GANs (WGANs) with gradient penalty (GP) [39], as well as addressing hardware limitations to enable longer training periods and larger batch sizes [40].  
Overall, the results demonstrate the potential of GANs in generating realistic microstructures for materials science applications, provided that careful attention is given to both quantitative metrics and visual quality assessments. The experiments with annealed steel images showed promising results, closely aligning with expert expectations for annealed carbon steel microstructures, while the experiment with martensite highlighted areas for further improvement in generating highly convincing microstructures under constrained training conditions.  
Importantly, these generation experiments are not merely exercises in image synthesis. They are crucial for new image generation and data augmentation, which are valuable for subsequent analyses and applications. This research is particularly significant because it allows us to expand the image database, which is a critical bottleneck in this field. By increasing the database, models can be continuously developed to update and enhance previous research on classification and segmentation. Additionally, the understanding of the physical properties of steels based on the observation of microstructures will be advanced, which will be a future line of investigation.  
Moreover, progress is aimed at creating synthetic microstructural images that can be tailored according to the microconstituent of interest, with custom-made images of high educational value being generated. Overall, the potential of GANs as powerful tools for materials science is highlighted by this study, provided that both quantitative and qualitative aspects are carefully managed. By generating realistic microstructures, materials development can be accelerated, processing conditions optimized, and new material compositions explored more efficiently.  
Conceptualization, [PERSON], [PERSON] and [PERSON]; methodology, [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]; software, [PERSON] and [PERSON]; validation, [PERSON], [PERSON] and [PERSON]; formal analysis, [PERSON], [PERSON] and [PERSON]; investigation, [PERSON], [PERSON] and [PERSON]; resources, [PERSON], [PERSON], [PERSON] and [PERSON]; data curation, [PERSON] and [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON], [PERSON] and [PERSON]; visualization, [PERSON] and [PERSON]; supervision, [PERSON], [PERSON], [PERSON] and [PERSON]; All authors have read and agreed to the published version of the manuscript.  
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
Figure 17: Experiment 2: Annealed steel synthetic images. Hyperparameters: epochs = 300, learning rate = 0.0002, number of filters = 16, kernel size = 4, batch size = 8.