Multi-response optimization and machine learning-based prediction of straight-groove warm incremental sheet forming of AZ31 magnesium alloy

Key Points

• The study predicts performance in warm incremental sheet forming of AZ31 magnesium alloy, enhancing efficiency with optimized parameters.
• Key evidence shows a significant improvement in precision, with metrics indicating efficiency gains across various parameters.
• Observational analysis using machine learning techniques leverages experimental data to optimize forming processes effectively.
• These findings may enable improved manufacturing practices in metal forming, though further validations in real-world applications are needed.