Revolutionizing iron texture analysis: the role of cold reduction and rolling directions through machine learning insights

This study employs machine learning (ML) to analyze the melting and reconsolidation behaviors of iron, emphasizing the influence of cold reduction ratios and rolling sequences. Five samples with varied cold reduction ratios and rolling patterns were examined. Findings indicate that when the cold reduction ratio exceeds 65%, coordinated cold melting minimally impacts crystallographic consistency. Texture formation remains largely unaffected during cold melting and short-duration annealing. However, extended annealing prompts irregular grain growth, altering crystal orientation. Sheets rolled in alignment with their initial condition exhibit consistency patterns similar to conventionally cold-melted pure iron after prolonged annealing. Key parameters influencing material performance were evaluated, revealing annealing temperature as the most significant factor (5.94), followed by cold melting direction order (1.46), while the hanging period during annealing had minimal impact (1.02). ML models were employed to predict Goss angle expansion using cold-rolling and annealing parameters. This approach demonstrates the potential of ML to predict texture evolution in pure iron, offering valuable insights for optimizing industrial cold-rolling practices.