A machine learning method to predict grain refinement and hardness of severely deformed materials

Key Points

• Grain refinement and hardness are predicted with high accuracy using a machine learning model, indicating better material performance.
• The predictive model demonstrates an accuracy rate of over 90% in assessing material properties from deformation levels.
• Assessment using a machine learning method reveals new insights into the mechanics of severely deformed materials.
• These findings suggest a new approach for optimizing material properties, indicating broad applications in engineering.