Deep-learning-based surrogate modeling for accelerated curing process optimization in scarf-repaired composite laminates

Key Points

• The optimization of the curing process is significantly improved using deep learning techniques—indicating enhanced performance in composite laminates.
• Deep learning achieved a reduction in curing time by up to 30% during the analysis of scarf-repaired laminates, enhancing efficiency.
• Assessment using surrogate modeling and optimization algorithms identified crucial parameters affecting the curing process—providing valuable insights.
• The findings highlight the potential of deep learning in improving the manufacturing processes of composite materials—calling for further industry applications.