This master’s thesis investigates the classification of microscopic aluminium inclusions using machine learning. Generative Adversarial Networks (GANs) are employed to address the challenge of limited labeled data. The study proposes generating synthetic datasets to improve the robustness of deep learning models in identifying defects in aluminium alloys despite environmental contaminants. The model achieves an impressive overall accuracy of 95.6%, suggesting its potential for improved defect detection. Additionally, the research explores the implications of integrating machine learning and computer vision for the manufacturing sector, particularly in high-strength aluminium alloys where precision is crucial. The findings indicate that this approach can significantly enhance defect detection and classification, leading to more reliable materials for critical applications in aerospace, automotive, and construction industries.