A robotized framework for real-time detection and in-situ repair of manufacturing defects in CFRP patch placement

Carbon fiber reinforced polymers (CFRP) have significant applications in aerospace and automotive manufacturing. However, due to the complexity of CFRP structures, manufacturing defects are challenging to avoid and even affect the mechanical properties. Timely detection and repair are essential to ensure product quality. In this study, we propose a robotized framework for real-time detection and in-situ repair of manufacturing defects in CFRP patch placement. First, the influence of three typical defects (delamination, wrinkle and impurity) on mechanical properties is analyzed through numerical analysis. Then, a defect detection model is improved using the channel attention mechanism and decoupling head module, which enhances detection accuracy and the ability to identify small and deep defects. Based on the identification result, a corresponding repair strategy is generated, which considers the effects of force, path, heating and repair modes. The experimental results demonstrate that the tensile stiffness and bending strength of the repaired material are improved by 12.34% and 230.92%, respectively.